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  • 1. Adamson, Göran
    et al.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Skövde, Sweden.
    Moore, Philip
    Feature-based control and information framework for adaptive and distributed manufacturing in cyber physical systems2017In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 143, p. 305-315Article in journal (Refereed)
    Abstract [en]

    Modern distributed manufacturing within Industry 4.0, supported by Cyber Physical Systems (CPSs), offers many promising capabilities regarding effective and flexible manufacturing, but there remain many challenges which may hinder its exploitation fully. One major issue is how to automatically control manufacturing equipment, e.g. industrial robots and CNC-machines, in an adaptive and effective manner. For collaborative sharing and use of distributed and networked manufacturing resources, a coherent, standardised approach for systemised planning and control at different manufacturing system levels and locations is a paramount prerequisite. In this paper, the concept of feature-based manufacturing for adaptive equipment control and resource task matching in distributed and collaborative CPS manufacturing environments is presented. The concept has a product perspective and builds on the combination of product manufacturing features and event-driven Function Blocks (FB) of the IEC 61499 standard. Distributed control is realised through the use of networked and smart FB decision modules, enabling the performance of collaborative runtime manufacturing activities according to actual manufacturing conditions. A feature-based information framework supporting the matching of manufacturing resources and tasks, as well as the feature-FB control concept, and a demonstration with a cyber-physical robot application, are presented.

  • 2.
    Adane, Tigist Fetene
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Bianchi, Maria Floriana
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Archenti, Andreas
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Nicolescu, Mihai
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Application of system dynamics for analysis of performance of manufacturing systems2019In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 53, p. 212-233Article, review/survey (Refereed)
    Abstract [en]

    Machining of parts by using dedicated production systems has been, and continues to be, a viable manufacturing method. There are situations, however, where this type of system is not feasible due to changes in product type, customer demand, work-piece material, or design specification. From a competitive manufacturing environment, production system selection is a crucial issue for all component manufacturing companies. Improper selections could negatively affect the overall performance of a manufacturing system, for instance the productivity, as well as the cost and quality of manufactured components. In this paper, the application of system dynamics modelling and simulation of a complex manufacturing process is presented as a potential tool to investigate and analyse the performance of manufacturing system in response to disturbances in the system's inputs (e.g., volume of products). In order to investigate the model soundness, a case study applied to the manufacturing of an engine block will be examined. The model presented here has been developed based on current engine block production for the vehicle manufacturing industry. Such a model can assist manufacturing system selection-centered round the capacity to control machining system parameters -as a testable way to choose a machining strategy from pre-selected performance criteria. More specifically, the benefit of this research lies in the fact that it will enable companies to implement improved potential manufacturing system optimization that responds during unexpected demand fluctuations. In addition, it will help in understanding the complex interaction between the process and operational parameters of a manufacturing system and help identify those critical parameters, ones that can lead to an optimizing strategy in the manufacturing standards of engine block production.

  • 3. Baroroh, D. K.
    et al.
    Chu, C. -H
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Systematic literature review on augmented reality in smart manufacturing: Collaboration between human and computational intelligence2021In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 61, p. 696-711Article in journal (Refereed)
    Abstract [en]

    Smart manufacturing offers a high level of adaptability and autonomy to meet the ever-increasing demands of product mass customization. Although digitalization has been used on the shop floor of modern factory for decades, some manufacturing operations remain manual and humans can perform these better than machines. Under such circumstances, a feasible solution is to have human operators collaborate with computational intelligence (CI) in real time through augmented reality (AR). This study conducts a systematic review of the recent literature on AR applications developed for smart manufacturing. A classification framework consisting of four facets, namely interaction device, manufacturing operation, functional approach, and intelligence source, is proposed to analyze the related studies. The analysis shows how AR has been used to facilitate various manufacturing operations with intelligence. Important findings are derived from a viewpoint different from that of the previous reviews on this subject. The perspective here is on how AR can work as a collaboration interface between human and CI. The outcome of this work is expected to provide guidelines for implementing AR assisted functions with practical applications in smart manufacturing in the near future.

  • 4.
    Bi, Z. M.
    et al.
    Indiana University-Purdue University Indianapolis.
    Wang, Lihui
    University of Skövde.
    Optimization of machining processes from the perspective of energy consumption: A case study2012In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 31, no 4, p. 420-428Article in journal (Refereed)
    Abstract [en]

    One of the primary objectives of sustainable manufacturing is to minimize energy consumption in its manufacturing processes. A strategy of energy saving is to adapt new materials or new processes; but its implementation requires radical changes of the manufacturing system and usually a heavy initial investment. The other strategy is to optimize existing manufacturing processes from the perspective of energy saving. However, an explicit relational model between machining parameters and energy cost is required: while most of the works in this field treat the manufacturing processes as black or gray boxes. In this paper, analytical energy modeling for the explicit relations of machining parameters and energy consumption is investigated, and the modeling method is based on the kinematic and dynamic behaviors of chosen machine tools. The developed model is applied to optimize the machine setup for energy saving. A new parallel kinematic machine Exechon is used to demonstrate the procedure of energy modeling. The simulation results indicate that the optimization can result in 67% energy saving for the specific drilling operation of the given machine tool. This approach can be extended and applied to other machines to establish their energy models for sustainable manufacturing

  • 5.
    Chatti, Sami
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Syrou, Meni
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Kleiner, Matthias
    Lindström, Bo
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    New-transnational curricula for BSc/MSc programs in production engineering2005In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 24, no 3, p. 145-152Article in journal (Refereed)
    Abstract [en]

    The paper describes the curricula of two new international programs in production engineering-one a tri-national master of science in "industrial design and manufacturing" (IDM) program and the other an international bachelor of science program. The IDM master's program has been developed at the University of Dortmund in cooperation with the University of Twente in the Netherlands. Since 2004, the third partner in the IDM program is the University of Strathclyde in the UK. The IDM program provides an integrated, holistic, and internationally oriented graduate education in mechanical engineering that keeps the balance between theory and practice. For the development of the bachelor's program, the pilot project EPRODE (European Production Engineer) was initiated in 2003. It has been granted by the European Union (EU) program Leonardo da Vinci and aims at specifying a standardized European curriculum. Partners of EPRODE are institutes from universities and the industry sector in Sweden, Germany, Poland, and Spain.

  • 6. Chu, C. -H
    et al.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Liu, S.
    Zhang, Y.
    Menozzi, M.
    Augmented reality in smart manufacturing: Enabling collaboration between humans and artificial intelligence2021In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 61, p. 658-659Article in journal (Refereed)
  • 7.
    de Giorgio, Andrea
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Cacace, Stefania
    Politecn Milan, Dept Mech Engn, Via La Masa 1, I-20156 Milan, Italy..
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Monetti, Fabio Marco
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Digital Smart Production.
    Roci, Malvina
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Manufacturing and Metrology Systems.
    Onori, Mauro
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Assessing the influence of expert video aid on assembly learning curves2022In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 62, p. 263-269Article in journal (Refereed)
    Abstract [en]

    Since the introduction of the concept of learning curves in manufacturing, many articles have been applying the model to study learning phenomena. In assembly, several studies present a learning curve when an operator is trained over a new assembly task; however, when comparisons are made between learning curves corresponding to different training methods, unaware researchers can show misleading results. Often, these studies neglect either or both the stochastic nature of the learning curves produced by several operators under experimental conditions, and the high correlation of the experimental samples collected from each operator that constitute one learning curve. Furthermore, recent studies are testing newer technologies, such as assembly animations or augmented reality, to provide assembly aid, but they fail to observe deeper implications on how these digital training methods truly influence the learning curves of the operators. This article proposes a novel statistical study of the influence of expert video aid on the learning curves in terms of assembly time by means of functional analysis of variance (FANOVA). This method is better suited to compare learning curves than common analysis of variance (ANOVA), due to correlated data, or graphical comparisons, due to the stochastic nature of the aggregated learning curves. The results show that two main effects of the expert video aid influence the learning curves: one in the transient and another in the steady state of the learning curve. The transient effect of the expert video aid, where the statistical tests suffer from a high variance in the data, appears to be a reduction in terms of assembly time for the first assemblies: the operators seem to benefit from the expert video aid. As soon as the steady state is reached, a slower and statistically significant effect appears to favor the learning processes of the operators who do not receive any training aid. Since the steady state of the learning curves represents the long term production efficiency of the operators, the latter effect might require more attention from industry and researchers.

  • 8.
    de Giorgio, Andrea
    et al.
    Artificial Engineering, Via del Rione Sirignano, 10, Naples, 80121, Italy.
    Cola, Gabriele
    Catholic University of the Sacred Heart, Largo Agostino Gemelli, 1, Milan, 20123, Italy.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Systematic review of class imbalance problems in manufacturing2023In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 71, p. 620-644Article, review/survey (Refereed)
    Abstract [en]

    Class imbalance (CI) is a well-known problem in data science. Nowadays, it is affecting the data modeling of many of the real-world processes that are being digitized. The manufacturing industry turns out to be highly affected by this problem, especially in fault inspection, prediction or monitoring processes, and in all those processes where the production efficiency is high and the data samples of anomalous events are rare. In this work, we systematically review all the data manipulation, machine learning or deep learning solutions to the CI problem in the manufacturing domain. We also critically evaluate all the different metrics that researchers can compare in order to estimate the improvements carried by their proposed solutions, and we look at the availability of public source code and data-imbalanced datasets that can be used for benchmarking. Finally, we summarize the most applied solutions to the CI problem in manufacturing and we look at future challenges. While posing a reference for the best practices at the time of this review, we challenge researchers to standardize the use of data science algorithms for CI in the manufacturing domain.

  • 9.
    de Giorgio, Andrea
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Onori, Mauro
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Towards online reinforced learning of assembly sequence planning with interactive guidance systems for industry 4.0 adaptive manufacturing2021In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 60, p. 22-34Article in journal (Refereed)
    Abstract [en]

    Literature shows that reinforcement learning (RL) and the well-known optimization algorithms derived from it have been applied to assembly sequence planning (ASP); however, the way this is done, as an offline process, ends up generating optimization methods that are not exploiting the full potential of RL. Today’s assembly lines need to be adaptive to changes, resilient to errors and attentive to the operators’ skills and needs. If all of these aspects need to evolve towards a new paradigm, called Industry 4.0, the way RL is applied to ASP needs to change as well: the RL phase has to be part of the assembly execution phase and be optimized with time and several repetitions of the process. This article presents an agile exploratory experiment in ASP to prove the effectiveness of RL techniques to execute ASP as an adaptive, online and experience-driven optimization process, directly at assembly time. The human-assembly interaction is modelled through the input-outputs of an assembly guidance system built as an assembly digital twin. Experimental assemblies are executed without pre-established assembly sequence plans and adapted to the operators’ needs. The experiments show that precedence and transition matrices for an assembly can be generated from the statistical knowledge of several different assembly executions. When the frequency of a given subassembly reinforces its importance, statistical results obtained from the experiments prove that online RL applications are not only possible but also effective for learning, teaching, executing and improving assembly tasks at the same time. This article paves the way towards the application of online RL algorithms to ASP.

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  • 10.
    de Giorgio, Andrea
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production engineering. Artificial Engineering, Via del Rione Sirignano 10, 80121 Naples, Italy.
    Monetti, Fabio Marco
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Romero, Mario
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Adopting extended reality?: A systematic review of manufacturing training and teaching applications2023In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 71, p. 645-663Article in journal (Refereed)
    Abstract [en]

    The training of future experts and operators in manufacturing engineering relies on understanding procedural processes that require applied practice. Yet, current manufacturing education and training overwhelmingly continues to depend on traditional pedagogical methods that segregate theoretical studies and practical training. While educational institutes have generally improved theoretical studies, they often lack facilities and labs to properly reproduce the working environments necessary for practice. Even in industrial settings, it is difficult, if not impossible, to halt the actual production lines to train new operators. Recently, applications with extended reality (XR) technologies, such as virtual, augmented, or mixed reality, reached a mature technology readiness level. With this technological advancement, we can envision a transition to a new teaching paradigm that exploits simulated learning environments. Thus, it becomes possible to bridge the gap between theory and practice for both students and industrial trainees. This article presents a systematic literature review of the main applications of XR technologies in manufacturing education, their goals and technology readiness levels, and a comprehensive overview of the development tools and experimental strategies deployed. This review contributes: (1) a state-of-the-art description of current research in XR education for manufacturing systems, and (2) a comprehensive analysis of the technological platforms, the experimental procedures and the analytical methodologies deployed in the body of literature examined. It serves as a guide for setting up and executing experimental designs for evaluating interventions of XR in manufacturing education and training.

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    fulltext
  • 11.
    Fratini, Livan
    et al.
    Univ Palermo, Palermo, Italy.
    Ragai, Ihab
    Penn State Univ, Erie, PA 16563 USA.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    New trends in manufacturing systems research2019In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 52, p. 209-210Article in journal (Refereed)
  • 12.
    Fratini, Livan
    et al.
    University of Palermo, Palermo, Italy.
    Ragai, Ihab
    Penn State University, Erie, Pennsylvania, USA.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    New trends in Manufacturing Systems Research 20202020In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642Article in journal (Refereed)
  • 13. Gao, Y.
    et al.
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, X. V.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Gao, L.
    A Review on Recent Advances in Vision-based Defect Recognition towards Industrial Intelligence2021In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642Article in journal (Refereed)
    Abstract [en]

    In modern manufacturing, vision-based defect recognition is an essential technology to guarantee product quality, and it plays an important role in industrial intelligence. With the developments of industrial big data, defect images can be captured by ubiquitous sensors. And, how to realize accuracy recognition has become a research hotspot. In the past several years, many vision-based defect recognition methods have been proposed, and some newly-emerged techniques, such as deep learning, have become increasingly popular and have addressed many challenging problems effectively. Hence, a comprehensive review is urgently needed, and it can promote the development and bring some insights in this area. This paper surveys the recent advances in vision-based defect recognition and presents a systematical review from a feature perspective. This review divides the recent methods into designed-feature based methods and learned-feature based methods, and summarizes the advantages, disadvantages and application scenarios. Furthermore, this paper also summarizes the performance metrics for vision-based defect recognition methods. And some challenges and development trends are also discussed. 

  • 14.
    Givehchi, Mohammad
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Haghighi, Azadeh
    KTH, School of Chemical Science and Engineering (CHE), Chemical Engineering and Technology, Chemical Technology.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Generic machining process sequencing through a revised enriched machining feature concept2015In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 37, p. 564-575Article in journal (Refereed)
    Abstract [en]

    Nowadays, small and medium-sized enterprises (SMEs) require a highly competitive process planning approach in order to survive. This mainly is due to the abrupt and continuous changes that occur every day in the production plant. This paper proposes a generic process sequencing approach that due to its independency to available resources can increase adaptability and flexibility of the system. The proposed method can be used by the Cloud-DPP (distributed process planning) in an integrated cyber-physical system. This rule-based approach requires the definition of a new revised enriched machining feature concept. The proposed concept not only possesses information of the machining feature itself (geometrical information, tolerances and coordinates system), but also contains additional information that are discussed in detail throughout the paper. A data format has been defined for the introduced additional data and the machinability rule has been defined as the key rule for sequencing. The sequencing approach in this work applies four sets of rules but can be extended if new rules are needed. The proposed method is then validated through a case study.

  • 15. Givehchi, Mohammad
    et al.
    Ng, Amos H.C.
    Wang, Lihui
    University of Skövde, Sweden.
    Evolutionary optimization of robotic assembly operation sequencing with collision-free paths2011In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 30, no 4, p. 196-203Article in journal (Refereed)
    Abstract [en]

    Many problems in the lifecycle of product and production development (PPD) can be formulated as optimization problems. But in most of the real-world cases, they are too complex to be solved by analytical models or classical optimization methods. CAx and virtual manufacturing (VM) tools are on the other hand being employed to create virtual representation of products and processes before any physical realization is conducted. Synergy of these two domains is of interest in this paper where planning a process with the minimum cycle-time for assembling a spot welded sheet-metal product is desired. The methodology suggests an extendible virtual manufacturing-based optimization approach using evolutionary algorithms. Accordingly, a novel toolset with integration of evolutionary optimization and a commercial VM environment is developed. More specifically, the latest feature which takes advantage of the collision avoidant segment path planning functionality of the VM tool and integrates it with the sequence optimizer is described.

  • 16.
    Huang, Sihan
    et al.
    Beijing Inst Technol, Sch Mech Engn, Beijing, Peoples R China..
    Wang, Baicun
    Zhejiang Univ, Sch Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou, Peoples R China..
    Li, Xingyu
    Univ Michigan, Dept Mech Engn, Ann Arbor, MI USA..
    Zheng, Pai
    Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hung Hom, Hong Kong, Peoples R China..
    Mourtzis, Dimitris
    Univ Patras, Dept Mech & Aeronaut Engn, Lab Mfg Syst & Automat, Rion, Greece..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Industry 5.0 and Society 5.0-Comparison, complementation and co-evolution2022In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 64, p. 424-428Article in journal (Refereed)
    Abstract [en]

    Recently, the futuristic industry and society have caught increasing attention, that is, on Industry 5.0 and Society 5.0. Industry 5.0 is announced by European Commission toward a sustainable, human-centric, and resilient European industry. Society 5.0 is proposed by Japan Cabinet to balance economic advancement with the reso-lution of social problems in Japanese society. Generally, the revolutions of industry and society have profoundly interacted with each other since the first industrial revolution. The coexistence of Industry 5.0 and Society 5.0 could raise varying confusions to be clarified and a series of questions to be answered. Therefore, we attempt to present the comparison, complementation, and co-evolution between Industry 5.0 and Society 5.0 to address the corresponding foundational arguments about Industry 5.0 and Society 5.0, which could be the basic inspiration for future investigation and discussion and accelerate the development of Industry 5.0 and Society 5.0.

  • 17.
    Huang, Zhiwen
    et al.
    School of Mechanical Engineering, University of Shanghai for Science and Technology, China.
    Li, Weidong
    School of Mechanical Engineering, University of Shanghai for Science and Technology, China.
    Zhu, Jianmin
    School of Mechanical Engineering, University of Shanghai for Science and Technology, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Cross-domain tool wear condition monitoring via residual attention hybrid adaptation network2024In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 72, p. 406-423Article in journal (Refereed)
    Abstract [en]

    Intelligent models for tool wear condition monitoring (TWCM) have been extensively researched. However, in industrial scenarios, limited acquired monitoring signals and variations of machining parameters lead to insufficient training samples and data distribution shifts for the models. To address the issues, this research presents a novel residual attention hybrid adaptation network (RAHAN) model based on a residual attention network (ResAttNet) and a hybrid adaptation strategy. In the RAHAN model, by integrating a channel attention mechanism and deep residual modules, ResAttNet is designed as a feature extractor to acquire features from monitoring signals for tool wear conditions. Embedding subdomain adaptation into a condition recognizer while establishing separate adversarial learning in a domain obfuscator, the hybrid adaptation strategy is developed to eliminate global distribution shifts and align local distributions of each tool wear phase simultaneously. Six migration tasks under a laboratory and two factory machining platforms were conducted to evaluate the effectiveness of the RAHAN model. Compared with a baseline model, four ablation models, and six state-of-the-art transfer learning models, the RAHAN model achieved the highest average accuracy of 92.70% on six migration tasks. Furthermore, the RAHAN model shows clearer feature representations of each tool wear condition than other compared models. The comparative results demonstrate that the RAHAN model has superior transferability and therefore can be considered as a good potential solution to support cross-domain TWCM under different machining processes.

  • 18.
    Ji, Wei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Harbin University of Science and Technology, Harbin, 150080, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Big Data Analytics Based Fault Prediction for Shop Floor Scheduling2017In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 43, p. 187-194Article in journal (Refereed)
    Abstract [en]

    The current task scheduling mainly concerns the availability of machining resources, rather than the potential errors after scheduling. To minimise such errors in advance, this paper presents a big data analytics based fault prediction approach for shop floor scheduling. Within the context, machining tasks, machining resources, and machining processes are represented by data attributes. Based on the available data on the shop floor, the potential fault/error patterns, referring to machining errors, machine faults and maintenance states, are mined for unsuitable scheduling arrangements before machining as well as upcoming errors during machining. Comparing the data-represented tasks with the mined error patterns, their similarities or differences are calculated. Based on the calculated similarities, the fault probabilities of the scheduled tasks or the current machining tasks can be obtained, and they provide a reference of decision making for scheduling and rescheduling the tasks. By rescheduling high-risk tasks carefully, the potential errors can be avoided. In this paper, the architecture of the approach consisting of three steps in three levels is proposed. Furthermore, big data are considered in three levels, i.e. local data, local network data and cloud data. In order to implement this idea, several key techniques are illustrated in detail, e.g. data attribute, data cleansing, data integration of databases in different levels, and big data analytic algorithms. Finally, a simplified case study is described to show the prediction process of the proposed method.

  • 19. Ji, Wei
    et al.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Haghighi, Azadeh
    KTH.
    Givehchi, Mohammad
    KTH.
    Liu, Xianli
    A reachability based approach for machining feature sequencing2016In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 40, p. 96-104Article in journal (Refereed)
    Abstract [en]

    Small and medium-sized enterprises (SMEs) are involved in highly personalised machining equipment and customised products. The trend in manufacturing systems towards higher degree of adaptability and automation has urged SMEs to seek new adaptable approaches for production, especially in the area of machining and process planning. In these areas, Cloud-DPP concept and machining feature (MF) based process sequencing have been proposed for increasing adaptability, and have gained attention recently. Both cutting tool conditions and product requirements are required in MF sequencing. In response to this fact, this paper proposes a reachability based method for MF sequencing which aims to reduce the number of tool changes and to meet specific machining requirements. This method is based on an MF path graph, an adjacency matrix, and a reachability matrix. MF path graph is mapped based on four types of mapping principles (MPs). Here, a basic MP is associated with MF sequencing rules, particular requirement MPs are relevant to machining requirements, and cutting tool MPs refer to MF machining strategies. According to MF path graph, adjacency matrix can be determined, which provides a basic matrix for calculating the reachability matrix. This method can be applied to cross-setup MF sequencing (using cross-setup MP) while making adaptive decisions along with unexpected changes of cutting tools. Finally, the results of the machined test part validate that the method can reduce the number of tool changes compared to the current MF sequencing methods.

  • 20.
    Jiang, Pei
    et al.
    State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China.
    Wang, Zuoxue
    State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China.
    Li, Xiaobin
    State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China.
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Yang, Bodong
    State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China.
    Zheng, Jiajun
    State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China.
    Energy consumption prediction and optimization of industrial robots based on LSTM2023In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 70, p. 137-148Article in journal (Refereed)
    Abstract [en]

    Due to wide distribution and low energy efficiency, the energy-saving of industrial robots draws more and more attention, and a large number of methods have emerged to predict or optimize the energy consumption (EC) of robots. However, many dynamic and electrical parameters are unavailable due to the commercial limitations of industrial robots, which constrains the application of those model-based methods. Therefore, this paper proposes a data-driven method for the prediction and optimization of robot EC. Initially, the cause-and-effect relationship between robot EC and joint motion variables, such as the joint position, velocity, and acceleration, is qualitatively analyzed based on the influence of the capacitive and inductive components in the drive system. And a deep neural network based on long short-term memory (LSTM) is proposed to reveal the nonlinear mapping between the industrial robot EC and the joint motion variables, which can predict EC without the parameters of the industrial robot. Based on the proposed neural network, the adaptive genetic algorithm is adopted to optimize the time-variant scaling function, which can optimize the scaled trajectory to reduce EC without hardware modification. To validate the accuracy and efficacy of the proposed method, experiments are conducted on a KUKA KR60-3 six degree-of-freedom (DOF) industrial robot. The results demonstrate that the proposed neural network can predict EC with a mean absolute percentage error less than 4.21% and the proposed method reduces the EC by 22.35%.

  • 21. Jin, G.Q.
    et al.
    Li, W.D.
    Tsai, C.F.
    Wang, Lihui
    University of Skövde.
    Adaptive Tool-Path Generation of Rapid Prototyping for Complex Product Models2011In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 30, no 3, p. 154-164Article in journal (Refereed)
    Abstract [en]

    Rapid prototyping (RP) provides an effective method for model verification and product development collaboration. A challenging research issue in RP is how to shorten the build time and improve the surface accuracy especially for complex product models. In this paper, systematic adaptive algorithms and strategies have been developed to address the challenge. A slicing algorithm has been first developed for directly slicing a Computer-Aided Design (CAD) model as a number of RP layers. Closed Non-Uniform Rational B-Spline (NURBS) curves have been introduced to represent the contours of the layers to maintain the surface accuracy of the CAD model. Based on it, a mixed and adaptive tool-path generation algorithm, which is aimed to optimize both the surface quality and fabrication efficiency in RP, has been then developed. The algorithm can generate contour tool-paths for the boundary of each RP sliced layer to reduce the surface errors of the model, and zigzag tool-paths for the internal area of the layer to speed up fabrication. In addition, based on developed build time analysis mathematical models, adaptive strategies have been devised to generate variable speeds for contour tool-paths to address the geometric characteristics in each layer to reduce build time, and to identify the best slope degree of zigzag tool-paths to further minimize the build time. In the end, case studies of complex product models have been used to validate and showcase the performance of the developed algorithms in terms of processing effectiveness and surface accuracy.

  • 22. Lei, P.
    et al.
    Zheng, L.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Wang, Y.
    Li, C.
    Li, X.
    MTConnect compliant monitoring for finishing assembly interfaces of large-scale components: A vertical tail section application2017In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 45, p. 121-134Article in journal (Refereed)
    Abstract [en]

    Monitoring is a significant issue for finishing the assembly interfaces of large-scale components before final assembly. Acquisition and supervision of the pivotal data is essential to ensure the security and reliability for machining the large and complicated components with high-value. This process is generally cumbersome and time-consuming because there are various types of data coming from different components and sensors. The problem becomes more serious when considering the whole shop floor. Recently, MTConnect has been proven to be an effective method to realize standardized data collection and monitoring process. However, MTConnect is still under development and cannot cover the whole finishing process such as on-machining measuring (OMM) and fixturing. To address the issue, an MTConnect compliant method with extended data models is proposed in this paper to implement a standardized monitoring system. Firstly, a finishing system for the assembly interfaces is introduced, including the framework, workflow and key procedures and data. Then extended MTConnect data models are proposed to represent the finishing system including on-machine touch-trigger probe and sensor-based intelligent fixturing related information. Based on the extended MTConnect data models, a web-based monitoring system is developed for data collection and monitoring by combining an MTConnect agent and an OPC adapter. The proposed approach is validated by collecting and monitoring the key process data using an airplane vertical tail as an application. The advantages of using MTConnect would be more significant when extended to the entire factory and implemented in cloud manufacturing in the future.

  • 23.
    Leng, Jiewu
    et al.
    Guangdong Univ Technol, State Key Lab Precis Elect Mfg Technol & Equipmen, Guangzhou 510006, Peoples R China.;City Univ Hong Kong, Shenzhen Res Inst, Shenzhen 518057, Peoples R China..
    Sha, Weinan
    Guangdong Univ Technol, State Key Lab Precis Elect Mfg Technol & Equipmen, Guangzhou 510006, Peoples R China..
    Wang, Baicun
    Zhejiang Univ, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310027, Peoples R China..
    Zheng, Pai
    Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong 999077, Peoples R China..
    Zhuang, Cunbo
    Beijing Inst Technol, Sch Mech Engn, Lab Digital Mfg, Beijing 100081, Peoples R China..
    Liu, Qiang
    Guangdong Univ Technol, State Key Lab Precis Elect Mfg Technol & Equipmen, Guangzhou 510006, Peoples R China..
    Wuest, Thorsten
    West Virginia Univ, Dept Ind & Management Syst Engn, Morgantown, WV 26506 USA..
    Mourtzis, Dimitris
    Univ Patras, Dept Mech Engn & Aeronaut, Lab Mfg Syst & Automat, Patras 26504, Greece..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Industry 5.0: Prospect and retrospect2022In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 65, p. 279-295Article in journal (Refereed)
    Abstract [en]

    Industry 5.0 blows the whistle on global industrial transformation. It aims to place humans' well-being at the center of manufacturing systems, thereby achieving social goals beyond employment and growth to provide prosperity robustly for the sustainable development of all humanity. However, the current exploration of Industry 5.0 is still in its infancy where research findings are relatively scarce and little systematic. This paper first reviews the evolutionary vein of Industry 5.0 and three leading characteristics of Industry 5.0: human-centricity, sustainability, and resiliency. The connotation system of Industry 5.0 is discussed, and its diversified essence is analyzed. Then, this paper constructs a tri-dimension system architecture for implementing Industry 5.0, namely, the technical dimension, reality dimension, and application dimension. The paper further discusses key enablers, the future implementation path, potential applications, and challenges of realistic scenarios of Industry 5.0. Finally, the limitations of the current research are discussed with potential future research directions highlighted. It is expected that this review work will arouse lively discussions and debates, and bring together the strengths of all beings for building a comprehensive system of Industry 5.0.

  • 24.
    Leng, Jiewu
    et al.
    Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing, and State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, China.
    Zhu, Xiaofeng
    Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing, and State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, China.
    Huang, Zhiqiang
    Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing, and State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, China.
    Li, Xingyu
    School of Engineering Technology, Purdue University, West Lafayette, IN 47907, USA.
    Zheng, Pai
    Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China.
    Zhou, Xueliang
    Department of Electrical and Information Engineering, HuBei University of Automotive Technology, Shiyan 442002, China.
    Mourtzis, Dimitris
    Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Rio Patras, 26504, Greece.
    Wang, Baicun
    State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China.
    Qi, Qinglin
    School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
    Shao, Haidong
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
    Wan, Jiafu
    School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China.
    Chen, Xin
    Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing, and State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    Liu, Qiang
    Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing, and State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, China.
    Unlocking the power of industrial artificial intelligence towards Industry 5.0: Insights, pathways, and challenges2024In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 73, p. 349-363Article, review/survey (Refereed)
    Abstract [en]

    With the continuous development of human-centric, resilient, and sustainable manufacturing towards Industry 5.0, Artificial Intelligence (AI) has gradually unveiled new opportunities for additional functionalities, new features, and tendencies in the industrial landscape. On the other hand, the technology-driven Industry 4.0 paradigm is still in full swing. However, there exist many unreasonable designs, configurations, and implementations of Industrial Artificial Intelligence (IndAI) in practice before achieving either Industry 4.0 or Industry 5.0 vision, and a significant gap between the individualized requirement and actual implementation result still exists. To provide insights for designing appropriate models and algorithms in the upgrading process of the industry, this perspective article classifies IndAI by rating the intelligence levels and presents four principles of implementing IndAI. Three significant opportunities of IndAI, namely, collaborative intelligence, self-learning intelligence, and crowd intelligence, towards Industry 5.0 vision are identified to promote the transition from a technology-driven initiative in Industry 4.0 to the coexistence and interplay of Industry 4.0 and a value-oriented proposition in Industry 5.0. Then, pathways for implementing IndAI towards Industry 5.0 together with key empowering techniques are discussed. Social barriers, technology challenges, and future research directions of IndAI are concluded, respectively. We believe that our effort can lay a foundation for unlocking the power of IndAI in futuristic Industry 5.0 research and engineering practice.

  • 25.
    Li, Dehua
    et al.
    National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
    Li, Yingguang
    National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
    Liu, Changqing
    National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
    Liu, Xu
    School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering, Industrial Production Systems.
    An online inference method for condition identification of workpieces with complex residual stress distributions2024In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 73, p. 192-204Article in journal (Refereed)
    Abstract [en]

    The residual stress field of structural components significantly influences their comprehensive performance and service life. Due to the lack of effective representation means and inference methods, existing methods are confined to inspecting local residual stress rather than the entire residual stress field, rendering the inference of complex residual stress fields quite difficult. In response to the challenges associated with the requirement for extensive sets of deformation force data from the current workpiece and the inherent difficulty in establishing a stable relationship between deformation forces and residual stress fields, this paper introduces a novel inference method of residual stress field is proposed based on a data-causal knowledge fusion model, where causal knowledge is introduced to eliminate the coupling effect of geometric change on residual stress, which can make up the drawback of pure data driven model. The proposed approach can accurately inference the residual stress within the workpieces, which provides an important basis for deformation control and part property improvement.

  • 26.
    Li, Shufei
    et al.
    Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China..
    Wang, Ruobing
    Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China..
    Zheng, Pai
    Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China.;Hong Kong Sci Pk, Lab Artificial Intelligence Design, Hong Kong, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Towards proactive human-robot collaboration: A foreseeable cognitive manufacturing paradigm2021In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 60, p. 547-552Article in journal (Refereed)
    Abstract [en]

    Human-robot collaboration (HRC) has attracted strong interests from researchers and engineers for improved operational flexibility and efficiency towards mass personalization. Nevertheless, existing HRC development mainly undertakes either human-centered or robot-centered manner reactively, where operations are conducted by following the pre-defined instructions, thus far from an efficient integration of robotic automation and human cognitions. The prevailing research on human-level information processing of cognitive computing, the industrial IoT, and robot learning creates the possibility of bridging the gap of knowledge distilling and information sharing between onsite operators, robots and other manufacturing systems. Hence, a foreseeable informatics-based cognitive manufacturing paradigm, Proactive HRC, is introduced as an advanced form of Symbiotic HRC with high-level cognitive teamwork skills to be achieved stepwise, including: (1) inter-collaboration cognition, establishing bi-directional empathy in the execution loop based on a holistic understanding of humans and robots' situations; (2) spatio-temporal cooperation prediction, estimating human-robot-object interaction of hierarchical sub-tasks/activities over time for the proactive planning; and (3) self-organizing teamwork, converging knowledge of distributed HRC systems for self-organization learning and task allocation. Except for the description of their technical cores, the main challenges and potential opportunities are further discussed to enable the readiness towards Proactive HRC.

  • 27.
    Li, Shufei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production engineering. Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China..
    Zheng, Pai
    Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China..
    Pang, Shibao
    KTH, School of Industrial Engineering and Management (ITM), Production engineering. Wuhan Univ Technol, Sch Mech & Elect Engn, Wuhan, Peoples R China..
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Self-organising multiple human-robot collaboration: A temporal subgraph reasoning-based method2023In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 68, p. 304-312Article in journal (Refereed)
    Abstract [en]

    Multiple Human-Robot Collaboration (HRC) requires self-organising task allocation to adapt to varying operation goals and workspace changes. However, nowadays an HRC system relies on predefined task arrangements for human and robot agents, which fails to accomplish complicated manufacturing tasks consisting of various operation sequences and different mechanical parts. To overcome the bottleneck, this paper proposes a temporal subgraph reasoning-based method for self-organising HRC task planning between multiple agents. Firstly, a tri-layer Knowledge Graph (KG) is defined to depict task-agent-operation relations in HRC tasks. Then, a subgraph mechanism is introduced to learn node embeddings from subregions of the HRC KG, which distills implicit information from local object sets. Thirdly, a temporal reasoning module is leveraged to integrate features from previous records and update the HRC KG for forecasting humans' and robots' subsequent operations. Finally, a car engine assembly task is demonstrated to evaluate the effectiveness of the proposed method, which outperforms other benchmarks in experimental results.

  • 28.
    Li, Xixing
    et al.
    Hubei Key Laboratory of Modern Manufacturing and Quality Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan, China.
    Zhao, Qingqing
    Hubei Key Laboratory of Modern Manufacturing and Quality Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan, China.
    Tang, Hongtao
    School of Mechanical Engineering, Wuhan University of Technology, Wuhan, China.
    Yang, Siqin
    School of Mechanical Engineering, Wuhan University of Technology, Wuhan, China.
    Lei, Deming
    School of Automation, Wuhan University of Technology, Wuhan, China.
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Joint scheduling optimisation method for the machining and heat-treatment of hydraulic cylinders based on improved multi-objective migrating birds optimisation2024In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 73, p. 170-191Article in journal (Refereed)
    Abstract [en]

    For the hydraulic cylinder parts manufacturing shop scheduling problem (HCPMS), which integrates a parallel batch processor hybrid flow shop scheduling problem with the flexible job shop scheduling problem, this paper establishes a multi-objective scheduling model with makespan, total energy consumption, and total machine workload as the optimisation objectives, and proposes an improved multi-objective migrating birds optimisation (IMOMBO) algorithm to solve the problem. First, considering the characteristics of the combination of single-piece and batch processing in the workshop, a double-layer coding rule based on the operation and processing equipment is proposed, and the corresponding decoding rule is designed according to whether the workpiece requires quenching and tempering. Second, a multi-population co-evolution mechanism is developed to enhance the diversity of solutions by conducting different evolutionary strategies. Additionally, six neighborhood structures are introduced to perform local searches for the leader and follower birds, thereby improving the quality of the solutions. Finally, the effectiveness of the IMOMBO algorithm is demonstrated by comparing its results with those of four other algorithms through comparative experiments and a practical case.

  • 29.
    Li, Xuebing
    et al.
    Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China.
    Liu, Xianli
    Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China.
    Yue, Caixu
    Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Liang, Steven Y.
    George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, 30332, USA.
    Data-model linkage prediction of tool remaining useful life based on deep feature fusion and Wiener process2024In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 73, p. 19-38Article in journal (Refereed)
    Abstract [en]

    Accurately predicting the tool remaining useful life (RUL) is critical for maximizing tool utilization and saving machining costs. Various physical model-based or data-driven prediction methods have been developed and successfully applied in different machining operations. However, many uncertain factors affect tool RUL during the cutting process, making it challenging to create a precise physical model to characterize the degradation of tool performance. The success of the purely data-driven technique depends on the amount and quality of the training samples, it does not consider the physical law of tool wear, and the interpretability of the prediction results is poor. This paper presents a data-model linkage approach for tool RUL prediction based on deep feature fusion and Wiener process to address the above limitations. A convolutional stacked bidirectional long short-term memory network with time-space attention mechanism (CSBLSTM-TSAM) is developed in the data-driven module to fuse the multi-sensor signals collected during the cutting process and then obtain the mapping relationship between signal features and tool wear values. In the physical modeling module, a three-stage tool RUL prediction model based on the nonlinear Wiener process is established by considering the evolution law of different wear stages and multi-layer uncertainty, and the corresponding probability density function is derived. The real-time estimated tool wear of the data-driven module is used as the observed value of the physical model, and the model parameters are dynamically updated by the weight-optimized particle filter (WOPF) algorithm under a Bayesian framework, thereby realizing the data-model linkage tool RUL prediction. Milling experiments demonstrate that the proposed method not only improves RUL prediction accuracy, but also has good generalization ability and robustness for prediction tasks under different working conditions.

  • 30. Lin, J.
    et al.
    Cai, B.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Prognostic and health management through collaborative maintenance2021In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 61, p. 712-713Article in journal (Refereed)
  • 31.
    Liu, Hongyi
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Human motion prediction for human-robot collaboration2017In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 44, p. 287-294Article in journal (Refereed)
    Abstract [en]

    In human-robot collaborative manufacturing, industrial robots would work alongside human workers who jointly perform the assigned tasks seamlessly. A human-robot collaborative manufacturing system is more customised and flexible than conventional manufacturing systems. In the area of assembly, a practical human-robot collaborative assembly system should be able to predict a human worker's intention and assist human during assembly operations. In response to the requirement, this research proposes a new human-robot collaborative system design. The primary focus of the paper is to model product assembly tasks as a sequence of human motions. Existing human motion recognition techniques are applied to recognise the human motions. Hidden Markov model is used in the motion sequence to generate a motion transition probability matrix. Based on the result, human motion prediction becomes possible. The predicted human motions are evaluated and applied in task-level human-robot collaborative assembly.

  • 32.
    Liu, Hongyi
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Remote human–robot collaboration: A cyber–physical system application for hazard manufacturing environment2020In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 54, p. 24-34Article in journal (Refereed)
    Abstract [en]

    Collaborative robot's lead-through is a key feature towards human–robot collaborative manufacturing. The lead-through feature can release human operators from debugging complex robot control codes. In a hazard manufacturing environment, human operators are not allowed to enter, but the lead-through feature is still desired in many circumstances. To target the problem, the authors introduce a remote human–robot collaboration system that follows the concept of cyber–physical systems. The introduced system can flexibly work in four different modes according to different scenarios. With the utilisation of a collaborative robot and an industrial robot, a remote robot control system and a model-driven display system is designed. The designed system is also implemented and tested in different scenarios. The final analysis indicates a great potential to adopt the developed system in hazard manufacturing environment.

  • 33.
    Liu, Peiji
    et al.
    Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing, Peoples R China.;Chongqing Univ, Sch Management Sci & Real Estate, Chongqing, Peoples R China..
    Zhang, Zhe
    Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing, Peoples R China..
    Wang, Xu
    Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing, Peoples R China..
    Li, Xiaobin
    Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing, Peoples R China..
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Tuo, Junbo
    Chongqing Technol & Business Univ, Sch Mech Engn, Chongqing, Peoples R China..
    A generalized method for the inherent energy performance modeling of machine tools2021In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 61, p. 406-422Article in journal (Refereed)
    Abstract [en]

    Machine tools (MTs), as the key equipment of manufacturing systems, have enormous quantities and consume a great amount of energy. However, the diversity of both machines and their energy consumption properties make it difficult to transfer the energy-saving knowledge and services among different MT. To facilitate the initialization configuration of energy-saving services, the inherent energy performance (IEP) is investigated to describe the differences in energy consumption among MTs, and a generalized method for modeling the IEP of MT and its electrical subsystems is proposed. Three key enablers, including generalized experimental design rules, automatic coding, and data processing algorithms, are presented and integrated into a supporting system to reduce the modeling efforts and knowledge requirements. Case studies of an offline manufacturing scenario and an Internet of Things (IoT)-enabled manufacturing scenario were carried out to verify the effectiveness and convenience of the proposed method. The results show that the proposed method can provide essential modeling support for large-scale energy-saving service configurations and energy-efficient MT development.

  • 34. Lu, S.
    et al.
    Xu, C.
    Zhong, R. Y.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    A RFID-enabled positioning system in automated guided vehicle for smart factories2017In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 44, p. 179-190Article in journal (Refereed)
    Abstract [en]

    Smart factory, as one of key future for our industry, requires logistics automation within a manufacturing site such as a shop floor. Automated guided vehicle (AGV) systems may be one solution, whose accuracy will be influenced by some factors. This paper presents a radio frequency identification (RFID)-enabled positioning system in AGV for smart factory. Key impact factors on AGV's accuracy such as magnetic field in circular antenna, circular magnetic field, and circular contours stability are examined quantitatively. Based on the examinations, simulation studies and a testbed are carried out to evaluate the feasibility and practicality of the proposed approach. It is observed that large diameter antennas are used in driving zone and small diameter antennas are used in parking zone. This approach was compared with another method using passive RFID tags and it is superior to that method with greatly reduced tags’ deployment. Observations and lessons from simulation and testbed studies could be used for guiding automatic logistics within a smart manufacturing shop floor.

  • 35. Lu, Yuqian
    et al.
    Xu, Xun
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Smart manufacturing process and system automation - A critical review of the standards and envisioned scenarios2020In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 56, p. 312-325Article, review/survey (Refereed)
    Abstract [en]

    Smart manufacturing is arriving. It promises a future of mass-producing highly personalized products via responsive autonomous manufacturing operations at a competitive cost. Of utmost importance, smart manufacturing requires end-to-end integration of intra-business and inter-business manufacturing processes and systems. Such end-to-end integration relies on standards-compliant and interoperable interfaces between different manufacturing stages and systems. In this paper, we present a comprehensive review of the current landscape of manufacturing automation standards, with a focus on end-to-end integrated manufacturing processes and systems towards mass personalization and responsive factory automation. First, we present an authentic vision of smart manufacturing and the unique needs for next-generation manufacturing automation. A comprehensive review of existing standards for enabling manufacturing process automation and manufacturing system automation is presented. Subsequently, focusing on meeting changing demands of efficient production of highly personalized products, we detail several future-proofing manufacturing automation scenarios via integrating various existing standards. We believe that existing automation standards have provided a solid foundation for developing smart manufacturing solutions. Faster, broader and deeper implementation of smart manufacturing automation can be anticipated via the dissemination, adoption, and improvement of relevant standards in a need-driven approach.

  • 36.
    Lu, Yuqian
    et al.
    Univ Auckland, Dept Mech & Mechatron Engn, Auckland, New Zealand..
    Zheng, Hao
    Univ Auckland, Dept Mech & Mechatron Engn, Auckland, New Zealand..
    Chand, Saahil
    Univ Auckland, Dept Mech & Mechatron Engn, Auckland, New Zealand..
    Xia, Wanqing
    Univ Auckland, Dept Mech & Mechatron Engn, Auckland, New Zealand..
    Liu, Zengkun
    Univ Auckland, Dept Mech & Mechatron Engn, Auckland, New Zealand..
    Xu, Xun
    Univ Auckland, Dept Mech & Mechatron Engn, Auckland, New Zealand..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Qin, Zhaojun
    Univ Auckland, Dept Mech & Mechatron Engn, Auckland, New Zealand..
    Bao, Jinsong
    Donghua Univ, Coll Mech Engn, Shanghai, Peoples R China..
    Outlook on human-centric manufacturing towards Industry 5.02022In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 62, p. 612-627Article in journal (Refereed)
    Abstract [en]

    The recent shift to wellbeing, sustainability, and resilience under Industry 5.0 has prompted formal discussions that manufacturing should be human-centric - placing the wellbeing of industry workers at the center of manufacturing processes, instead of system-centric - only driven by efficiency and quality improvement and cost reduction. However, there is a lack of shared understanding of the essence of human-centric manufacturing, though significant research efforts exist in enhancing the physical and cognitive wellbeing of operators. Therefore, this position paper presents our arguments on the concept, needs, reference model, enabling technologies and system frameworks of human-centric manufacturing, providing a relatable vision and research agenda for future work in human-centric manufacturing systems. We believe human-centric manufacturing should ultimately address human needs defined in an Industrial Human Needs Pyramid - from basic needs of safety and health to the highest level of esteem and self-actualization. In parallel, human-machine relationships will change following a 5C evolution map - from current Coexistence, Cooperation and Collaboration to future Compassion and Coevolution. As such, human-centric manufacturing systems need to have bi-directional empathy, proactive communication and collaborative intelligence for establishing trustworthy human-machine coevolution relationships, thereby leading to high-performance human-machine teams. It is suggested that future research focus should be on developing transparent, trustworthy and quantifiable technologies that provide a rewarding working environment driven by real-world needs.

  • 37.
    Ma, Wei
    et al.
    Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China..
    Liu, Xianli
    Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China..
    Yue, Caixu
    Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Liang, Steven Y.
    Georgia Inst Technol, George W Woodruff Sch Mech Engn, Atlanta, GA 30332 USA..
    Multi-scale one-dimensional convolution tool wear monitoring based on multi-model fusion learning skills2023In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 70, p. 69-98Article in journal (Refereed)
    Abstract [en]

    Effective tool wear monitoring (TWM) is crucial for accurately assessing the degree of tool wear, guiding tool replacement during actual cutting processes, ensuring stable machine operation, and improving workpiece processing quality. With the arrival of the era of Big data, more and more data-driven monitoring methods are used for TWM problems, but it also exposes the problems of over reliance on artificial feature extraction and selection, low robustness of the actual industrial environment and poor generalization of different machining processes. To solve these problems, this paper proposes a multi-scale one-dimensional convolution (MODC-MMFL) end-to-end TWM integrated network model based on multi-model fusion learning (MMFL) skills. Firstly, multi-scale local features of multi-sensor signals are adaptively extracted by multi-scale one-dimensional convolution (MODC) network, to realize multi-feature fusion. Then, using MMFL skills, the MMFL network is composed of deep attention temporal convolutional network (DATCN) and stacked bidirectional gate recurrent unit network (SBIGRU), parallel learning time series features related to tool wear characteristics,and use a fusion layer to fuse these learned features, in which residual channel attention mechanism (RCAM) is used to improve network performance in DATCN network. Finally, the predicted tool wear value is output by fully connected regression network (FCR). In addition, this paper uses the PHM tool wear dataset to conduct exper-imental study on the proposed model, first verifying the effectiveness of the proposed model. Then, ablation experiments were conducted to investigate the impact of hyper-parameters on the predictive performance of the model. The model was enhanced through hyper-parameter tuning, and a generalized enhanced model was established. The experimental results showed that the enhanced model had better predictive performance compared to ordinary models. Finally, Gaussian noise is added to the original signal of the PHM tool wear dataset to simulate the high noise signal of the actual industrial environment. The noise signal is used to carry out experimental study on the enhanced model. The experimental results show that the enhanced model still has good prediction performance in the high noise environment and has high robustness to the actual industrial environment. After the above research, this paper uses the NASA tool wear dataset to conduct experimental study on the proposed model. The experimental results show that the proposed model has good predictive performance for different machining processes, verifying the generalizability of the proposed model for different machining processes. In summary, the model proposed in this paper can accurately predict tool wear values based on processing monitoring information, and has good predictive performance, anti-interference ability, and envi-ronmental adaptability, making it very suitable for practical industrial applications.

  • 38. Mourtzis, D.
    et al.
    Vlachou, E.
    Xanthopoulos, N.
    Givehchi, Mohammad
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Cloud-based adaptive process planning considering availability and capabilities of machine tools2016In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 39, p. 1-8Article in journal (Refereed)
    Abstract [en]

    Disturbances on manufacturing shop-floors and the increasing number of product variants necessitate adaptive and flexible process planning methods. This paper proposes a service-oriented Cloud-based software framework comprising two services. The first service generates non-linear process plans using event-driven function blocks and a genetic algorithm. The second service, gathers data from shop-floor machine tools through sensors, input from operators, and machine schedules. An information fusion technique processes the monitoring data in order to feed the process planning service with the status, specifications, and availability time windows of machine tools. The methodology is validated in a case study of a machining SME.

  • 39.
    Mourtzis, Dimitris
    et al.
    Univ Patras, Dept Mech Engn & Aeronaut, Lab Mfg Syst & Automat, Patras 26504, Greece..
    Panopoulos, Nikos
    Univ Patras, Dept Mech Engn & Aeronaut, Lab Mfg Syst & Automat, Patras 26504, Greece..
    Angelopoulos, John
    Univ Patras, Dept Mech Engn & Aeronaut, Lab Mfg Syst & Automat, Patras 26504, Greece..
    Wang, Baicun
    Zhejiang Univ, Sch Mech Engn, State Key Lab Fluid Power & Mech Syst, Hangzhou, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Human centric platforms for personalized value creation in metaverse2022In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 65, p. 653-659Article in journal (Refereed)
    Abstract [en]

    The term "Metaverse" first used in Neal Stephenson's sci-fi book Snow Crash in 1992, refers to a fusion of virtual and real existence. Nearly 30 years later, that definition is taking shape and promises to alter how people live and operate. This next evolution of Internet also known as Web3.0 will combine digital and physical elements. Multiple definitions can be found in the literature, with the most prevalent being the "new internet", among others such as "democratized virtual society", "persistent virtual spaces", "a digital twin of our own world for personalized value creation". Consequently, the common consensus dictates that Metaverse can be realized as a new form of the Internet, totally reshaped from what is already known. As we are heading towards the coexistence of Industry 5.0 and Society 5.0 (super smart and intelligent society), this paper attempts to present the definition of Metaverse, its evolution, the advantages and disadvantages, the pillars for the technological advancement which could be the fuel to spark future investigation and discussion as well as to accelerate the development of Metaverse towards the human centric and personalized society. Furthermore, in this manuscript, challenges and opportunities are presented (including Manufacturing), a brief comparison is performed versus Virtual Reality, and a conceptual framework for integrating Metaverse in Manufacturing is also presented.

  • 40. Peng, Tao
    et al.
    Xu, Xun
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    A novel energy demand modelling approach for CNC machining based on function blocks2014In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 33, no 1, p. 196-208Article in journal (Refereed)
    Abstract [en]

    Energy efficiency remains one of the major issues in the machining domain. Today's machining systems are confronted with a number of new challenges, such as turbulent product demand and variations in production resources. Rapid and flexible energy modelling in a distributed and collaborative machining environment emerges as a new research area. Energy demand models in such an environment need to be practical, accurate, effective, scalable and reusable. Energy analysis and optimisation cannot be carried out once for all at the beginning. Instead, it is an on-going process. In this paper, the function block technique, i.e. IEC 61499, is used for the development of energy demand models as it brings advantages such as modularity, encapsulation, extensibility and reusability. A brief review on energy modelling and research on function blocks are given in the first part. A novel energy demand modelling approach based on function blocks is then proposed and elaborated. Three types of function blocks have been developed, i.e. machine tool dependent function blocks, state transition function blocks, and service interface function blocks. The first type, as the fundamental building blocks, is divided into two sub-types, machine component function block and machining state function block. Two case studies, based on a small 3-axis milling machine and an industrial production line respectively, are presented to demonstrate the possible applications using the function block-based model. Comprehensive discussions are given thereafter, including a pilot application of a distributed process planning system and a unique energy evaluation scheme. A confidence level associated energy rating system is proposed as the first step to turn energy consumption figures into useful indicators. The energy demand model based on function blocks developed here enhances the energy modelling and their practical implementations.

  • 41.
    Ping, Yaoyao
    et al.
    Xidian Univ, Sch Mechano Elect Engn, Xian 710071, Shaanxi, Peoples R China..
    Liu, Yongkui
    Xidian Univ, Sch Mechano Elect Engn, Xian 710071, Shaanxi, Peoples R China..
    Zhang, Lin
    Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Xu, Xun
    Univ Auckland, Dept Mech Engn, Auckland 1142, New Zealand..
    Sequence generation for multi-task scheduling in cloud manufacturing with deep reinforcement learning2023In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 67, p. 315-337Article in journal (Refereed)
    Abstract [en]

    Cloud manufacturing is a manufacturing model that aims to deliver on-demand manufacturing services to consumers. Scheduling is an important problem that needs to be addressed carefully and effectively for cloud manufacturing to achieve that aim. Cloud manufacturing allows consumers to submit their requirements to the cloud platform simultaneously and therefore requires cloud manufacturing scheduling systems to be able to handle multiple tasks effectively. It is further complicated when multiple composite tasks are submitted to the system and to be addressed. A vast majority of existing studies have proposed various algorithms, including meta-heuristics, heuristics, and reinforcement learning algorithms, to address cloud manufacturing scheduling (CMfg-Sch) problems, but only a very small fraction of them deal with scheduling of multiple composition tasks with deep reinforcement learning. In this work, we leverage DRL coupled with sequence generation for addressing CMfg-Sch problems. Different from all existing works, we first propose two sequence generation al-gorithms for generating scheduling sequences of multiple composite tasks prior to scheduling. Coupled with this a Deep Q-Networks (DQN) and a Double DQN-based scheduling algorithms are proposed, respectively. Perfor-mance of the proposed algorithms is compared against seven baseline algorithms using makespan, cost, and reliability as evaluation metrics. Comparison indicates that sequence generation algorithm II (SGA-II) overall has a greater advantage over algorithm I (SGA-I), especially in terms of the makespan, and the Double DQN-based scheduling algorithm outperforms the DQN-based algorithm, which in turn performs better than other base-line algorithms.

  • 42. Qi, Q.
    et al.
    Tao, F.
    Hu, T.
    Anwer, N.
    Liu, A.
    Wei, Y.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Nee, A. Y. C.
    Enabling technologies and tools for digital twin2021In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 58, p. 3-21Article in journal (Refereed)
    Abstract [en]

    Digital twin is revolutionizing industry. Fired by sensor updates and history data, the sophisticated models can mirror almost every facet of a product, process or service. In the future, everything in the physical world would be replicated in the digital space through digital twin technology. As a cutting-edge technology, digital twin has received a lot of attention. However, digital twin is far from realizing their potential, which is a complex system and long-drawn process. Researchers must model all the different parts of the objects or systems. Varied types of data needed to be collected and merged. Many researchers and participators in engineering are not clear which technologies and tools should be used. 5-dimension digital twin model provides reference guidance for understanding and implementing digital twin. From the perspective of 5-dimension digital twin model, this paper tries to investigate and summarize the frequently-used enabling technologies and tools for digital twin to provide technologies and tools references for the applications of digital twin in the future.

  • 43.
    Qin, Yiyuan
    et al.
    Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China..
    Liu, Xianli
    Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China..
    Yue, Caixu
    Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China..
    Zhao, Mingwei
    Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China..
    Wei, Xudong
    Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Tool wear identification and prediction method based on stack sparse self-coding network2023In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 68, p. 72-84Article in journal (Refereed)
    Abstract [en]

    In the process of metal cutting, the effective monitoring of tool wear is of great significance to ensure the machining quality of parts. Aiming at the problem of tool wear monitoring, a tool wear recognition and prediction method based on stack sparse self-coding network is proposed. This method can simplify the establishment process of monitoring model, monitor the tool wear according to different task requirements, and guide the tool replacement in the actual cutting process. Firstly, unsupervised K-means clustering is used to divide the tool wear stage, and the feature set is marked. Secondly, the parameters of stack sparse self-coding network layer are determined by trial, and the sensitive features that can reflect the tool wear process are obtained. Finally, the tool wear identification model of stack sparse self-encoder and the tool wear prediction model of BP neural network are established respectively, and the smoothing correction method is used to further improve the prediction accuracy. The experimental results show that the established tool wear identification and prediction model can accurately monitor the tool wear state and wear amount, and has a certain reference value for efficient tool change in the actual metal cutting process.

  • 44. Schmidt, Bernard
    et al.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Skövde, Sweden.
    Depth camera based collision avoidance via active robot control2014In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 33, no 4, p. 711-718Article in journal (Refereed)
    Abstract [en]

    A new type of depth cameras can improve the effectiveness,of safety monitoring in human-robot collaborative environment. Especially on today's manufacturing shop floors, safe human-robot collaboration is of paramount importance for enhanced work efficiency, flexibility, and overall productivity. Within this context, this paper presents a depth camera based approach for cost-effective real-time safety monitoring of a human-robot collaborative assembly cell. The approach is further demonstrated in adaptive robot control. Stationary and known objects are first removed from the scene for efficient detection of obstacles in a monitored area. The collision detection is processed between a virtual model driven by real sensors, and 3D point cloud data of obstacles to allow different safety scenarios. The results show that this approach can be applied to real-time work cell monitoring.

  • 45.
    Tang, Lifei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Feng, Lei
    KTH, School of Industrial Engineering and Management (ITM), Centres, Innovative Centre for Embedded Systems, ICES. KTH, School of Industrial Engineering and Management (ITM), Engineering Design, Mechatronics and Embedded Control Systems.
    Axelsson, Toni
    Atlas Copco Industrial Technique AB.
    Törngren, Martin
    Wilkman, Dennis
    Atlas Copco Industrial Technique AB.
    A deep learning based sensor fusion method to diagnose tightening errors2023In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 71, p. 59-69Article in journal (Refereed)
    Abstract [en]

    Modern smart assembly lines commonly include electric tools with built-in sensors to tighten safety-critical joints. These sensors generate data that are subsequently analyzed by human experts to diagnose potential tightening errors. Previous research aimed to automate the diagnosing process by developing diagnosing models based on tightening theory and calibration of the friction coefficient in specific lab setups. Generalizing these results is difficult and often unsuccessful since friction coefficients vary between lab and production environments. To overcome this problem, this paper presents a novel methodology that builds multi-label classification deep learning models for diagnosing tightening errors using production data. The proposed methodology comprises three key contributions, i.e., the Labrador method, the Model Combo (MoBo) framework, and a heuristic evaluation method. Labrador is an elastic deep learning based sensor fusion method that (1) uses feature encoders to extract features; (2) conducts data-level and/or feature-level sensor fusion in both time and frequency domains; and (3) performs multi-label classification to detect and diagnose tightening errors. MoBo is a configurable and modular framework that supports Labrador in identifying optimal feature encoders. With MoBo and Labrador, one can easily explore and design a bounded search space for sensor fusion strategies (SFSs) and feature encoders. In order to identify the optimal solution within the defined search space, this paper introduces a heuristic method. By evaluating the trade-off between machine learning (ML) metrics (e.g., accuracy, subset accuracy, and F1) and operational (OP) metrics (e.g., inference latency), the proposed method identifies the most suitable solution depending on the requirements of individual use cases. In the experimental evaluation, we adopt the proposed methodology to identify the most suitable multi-label classification solutions for diagnosing tightening errors. To optimize ML metrics, the identified solution achieved 99.69% accuracy, 93.39% subset accuracy, 97.39% F1, and 6.68ms inference latency. To optimize OP metrics, the identified solution achieved 99.66% accuracy, 92.65% subset accuracy, 97.28% F1, and 2.41ms inference latency.

  • 46. Tao, F.
    et al.
    Anwer, N.
    Liu, A.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Nee, A. Y. C.
    Li, L.
    Zhang, M.
    Digital twin towards smart manufacturing and industry 4.02021In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 58, p. 1-2Article in journal (Refereed)
  • 47.
    Wang, Baicun
    et al.
    Zhejiang University, Hangzhou, China.
    Peng, Tao
    Zhejiang University, Hangzhou, China.
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Wuest, Thorsten
    West Virginia University, Morgantown, WV, United States.
    Romero, David
    Tecnológico de Monterrey, Mexico City, Mexico.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Human-centric smart manufacturing2023In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 69, p. 18-19Article in journal (Other academic)
  • 48.
    Wang, Baicun
    et al.
    Zhejiang Univ, Sch Mech Engn, State Key Lab Fluid Power & Mechatron Syst, Hangzhou, Peoples R China.;Zhejiang Univ, Sch Mech Engn, Key Lab Adv Mfg Technol Zhejiang Prov, Hangzhou, Peoples R China..
    Zheng, Pai
    Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China..
    Yin, Yue
    Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China..
    Shih, Albert
    Univ Michigan, Dept Mech Engn, Ann Arbor, MI USA..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Toward human-centric smart manufacturing: A human-cyber-physical systems (HCPS) perspective2022In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 63, p. 471-490Article in journal (Refereed)
    Abstract [en]

    Advances in human-centric smart manufacturing (HSM) reflect a trend towards the integration of human-in-the loop with technologies, to address challenges of human-machine relationships. In this context, the human-cyberphysical systems (HCPS), as an emerging human-centric system paradigm, can bring insights to the development and implementation of HSM. This study presents a systematic review of HCPS theories and technologies on HSM with a focus on the human-aspect is conducted. First, the concepts, key components, and taxonomy of HCPS are discussed. HCPS system framework and subsystems are analyzed. Enabling technologies (e.g., domain technologies, unit-level technologies, and system-level technologies) and core features (e.g., connectivity, integration, intelligence, adaptation, and socialization) of HCPS are presented. Applications of HCPS in smart manufacturing are illustrated with the human in the design, production, and service perspectives. This research offers key knowledge and a reference model for the human-centric design, evaluation, and implementation of HCPS-based HSM.

  • 49. Wang, Charlie C. L.
    et al.
    Chu, Chih-Hsing
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Ramani, Karthik
    Depth cameras based techniques and applications in design, manufacturing and services2014In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 33, no 4, p. 675-676Article in journal (Refereed)
  • 50.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    A futuristic perspective on human-centric assembly2022In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 62, p. 199-201Article in journal (Refereed)
    Abstract [en]

    This paper provides a futuristic perspective on human-centric assembly and identifies four enhanced human abilities to empower human operators. Brain robotics is also introduced to be a central element in human-centric assembly. Remaining challenges and future opportunities are also highlighted in the end.

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