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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.
    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

  • 3.
    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.

  • 4.
    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)
  • 5.
    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.

  • 6. 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.

  • 7.
    Ji, Wei
    et al.
    KTH.
    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. 173-194Article 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.

  • 8. 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.

  • 9. 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.

  • 10. 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.

  • 11.
    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.

  • 12. 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.

  • 13. 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.

  • 14. 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.

  • 15. 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.

  • 16. 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)
  • 17.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    An overview of function block enabled adaptive process planning for machining2015In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 35, p. 10-25Article in journal (Refereed)
    Abstract [en]

    Small- and medium-sized enterprises (SMEs) in job-shop machining are experiencing more shop-floor uncertainties today than ever before, due to multi-tier outsourcing, customised product demands and shortened product lifecycle. In a fluctuating shop floor environment, a process plan generated in advance is often found unsuitable or unusable to the targeted resources, resulting both in wasted effort spent in early process planning and in productivity drop when idle machines have to wait for operations to be re-planned. Consequently, an adaptive process planning approach is in demand. Targeting shop-floor uncertainty, the objective of this research is to develop a novel adaptive process planning method that can generate process plans at runtime to unplanned changes. This paper, in particular, presents an overview of adaptive process planning research and a new methodology, including two-layer system architecture, generic supervisory planning, machine-specific operation planning, and adaptive setup planning. Particularly, function blocks are introduced as a core enabling technology to bridge the gap between computer systems and CNC systems for adaptive machining.

  • 18.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Editorial: 38th anniversary for Journal of Manufacturing Systems2019In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 51, p. 132-132Article in journal (Other academic)
  • 19.
    Wang, Lihui
    et al.
    University of Skovde.
    Adamson, Göran
    University of Skovde.
    Holm, Magnus
    University of Skovde.
    Moore, Philip
    De Montfort University.
    A review of function blocks for process planning and control of manufacturing equipment2012In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 31, no 3, p. 269-279Article in journal (Refereed)
    Abstract [en]

    Manufacturing in a job-shop environment is often characterized by a large variety of products in small batch sizes, requiring real-time monitoring for dynamic distributed decision making, and adaptive control capabilities that are able to handle, in a responsive way, different kinds of uncertainty, such as changes in demand and variations in production capability and functionality. In many manufacturing systems, traditional methods. based on offline processing performed in advance, are used. These methods are not up to the standard of handling uncertainty, in the dynamically changing environment of these manufacturing systems. Using real-time manufacturing intelligence and information to perform at a maximum level, with a minimum of unscheduled downtime, would be a more effective approach to handling the negative performance impacts of uncertainty. The objective of our research is to develop methodologies for distributed, adaptive and dynamic process planning as well as machine monitoring and control for machining and assembly operations, using event-driven function blocks. The implementation of this technology is expected to increase productivity, as well as flexibility and responsiveness in a job-shop environment. This paper, in particular, presents the current status in this field and a comprehensive overview of our research work on function block-enabled process planning and execution control of manufacturing equipment

  • 20.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Skövde.
    Feng, Hsi-Yung
    Adaptive Manufacturing2011In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 30, no 3, p. 117-117Article in journal (Other academic)
  • 21.
    Wang, Lihui
    et al.
    National Research Council of Canada.
    Feng, H.-Y.
    Cai, N.
    Architecture Design for Distributed Process Planning2003In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 22, no 2, p. 99-115Article in journal (Refereed)
    Abstract [en]

    Today's machining shop floors, characterized by a large variety of products in small batch sizes, require dynamic process planning capabilities that are responsive and adaptive to the rapid changes of production capacity and functionality. To meet the requirement, this research proposes a new methodology for dynamic and distributed process planning. The primary focus of this paper is on the architecture of a new approach using function blocks. The secondary focus is given to the other supporting technologies-machining features and agents. Different from conventional methods, this approach uses a two-layer structure-supervisory planning and operation planning. It is expected that the new architecture can improve the system performance in a dynamic environment. 

  • 22.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Fratini, Livan
    Univ Palermo, Palermo, Italy..
    Shih, Albert J.
    Univ Michigan, Ann Arbor, MI 48109 USA..
    Advancing manufacturing systems research at NAMRC 462018In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 48, p. 1-2Article in journal (Other academic)
  • 23.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. NAMRI/SME Scientific Committee Chair, KTH Royal Institute of Technology, Stockholm, Sweden.
    Fratini, Livan
    Shih, Albert J.
    Latest advancements in manufacturing systems at NAMRC 452017In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 44, p. 271-272Article in journal (Other academic)
  • 24.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Haghighi, Azadeh
    Combined strength of holons, agents and function blocks in cyber-physical systems2016In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 40, p. 25-34Article in journal (Refereed)
    Abstract [en]

    This paper presents a novel approach to implementing cyber-physical systems (CPS) using the combined strength of holons, agents and function blocks. Within the context, a CPS is represented by a holarchy of multiple holons. Each holon possesses a logical part and a physical part, which mimic the cyber and physical entities of the CPS. During implementation, the two parts of a holon are realised by agents and function blocks for information processing and materials processing, respectively. The objective of this research is to provide a concept map and associate a CPS with holons, agents and function blocks for the ease of system implementation in decentralised or cloud environment.

  • 25. Wang, Lihui
    et al.
    Keshavarzmanesha, Shadi
    Feng, Hsi-Yung
    Design of Adaptive Function Blocks for Dynamic Assembly Planning and Control2008In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 27, no 1, p. 45-51Article in journal (Refereed)
    Abstract [en]

    In today's competitive market, the life cycle of products is shrinking while product variety is growing. The product mix in small batches contributes to manufacturing uncertainty. Meanwhile, ever-growing business globalization and outsourcing also influence manufacturing. In such a dynamic environment, adaptability is of great importance. It is essential to develop a system that enables not only adaptive decision making, but also effective decision execution. Targeting manufacturing uncertainty, this paper reports a new framework and methodology for adaptive assembly planning using function blocks, which can be used directly for assembly control.

  • 26.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Skövde.
    Shih, A.J.
    Advanced Manufacturing Research2015In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 37, no 2, p. 457-458Article in journal (Refereed)
  • 27.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Shih, Albert J.
    Challenges in smart manufacturing2016In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 40, p. 1-1Article in journal (Other academic)
  • 28.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Törngren, Martin
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Onori, Mauro
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Current status and advancement of cyber-physical systems in manufacturing2015In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 37, p. 517-527Article in journal (Refereed)
    Abstract [en]

    This paper presents the current status and the latest advancement of cyber-physical systems (CPS) in manufacturing. In order to understand CPS and its future potential in manufacturing, definitions and characteristics of CPS are explained and compared with cloud manufacturing concept. Research and applications are outlined to highlight the latest advancement in the field. CPS shows great promise in factories of the future in the areas of future trends as identified at the end of this paper.

  • 29.
    Wu, Dazhong
    et al.
    Univ Cent Florida, Orlando, FL 32816 USA..
    Weiss, Brian A.
    NIST, Gaithersburg, MD 20899 USA..
    Kurfess, Thomas
    Georgia Inst Technol, Atlanta, GA 30332 USA..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Davis, Jim
    Univ Calif Los Angeles, Los Angeles, CA 90024 USA..
    Introduction to the special issue on smart manufacturing2018In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 48, p. 1-2Article in journal (Other academic)
  • 30.
    Yao, Bitao
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems. Wuhan Univ Technol, Sch Mech & ElWuhan Univ Technol, Hubei Key Lab Broadband Wireless Commun & Sensor, Wuhan 430070, Hubei, Peoples R China..
    Zhou, Zude
    Wuhan Univ Technol, Sch Mech & Elect Engn, Wuhan 430070, Hubei, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Xu, Wenjun
    Wuhan Univ Technol, Sch Informat Engn, Wuhan 430070, Hubei, Peoples R China.;Wuhan Univ Technol, Hubei Key Lab Broadband Wireless Commun & Sensor, Wuhan 430070, Hubei, Peoples R China..
    Yan, Junwei
    Wuhan Univ Technol, Sch Informat Engn, Wuhan 430070, Hubei, Peoples R China.;Wuhan Univ Technol, Hubei Key Lab Broadband Wireless Commun & Sensor, Wuhan 430070, Hubei, Peoples R China..
    Liu, Quan
    Wuhan Univ Technol, Sch Informat Engn, Wuhan 430070, Hubei, Peoples R China.;Wuhan Univ Technol, Hubei Key Lab Broadband Wireless Commun & Sensor, Wuhan 430070, Hubei, Peoples R China..
    A function block based cyber-physical production system for physical human robot interaction2018In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 48, p. 12-23Article in journal (Refereed)
    Abstract [en]

    Human-robot collaboration (HRC) is becoming a trend in manufacturing industry. However, the dramatic changes of requirements from the market put a higher demand for the flexibility of manufacturing systems. Cyber-Physical Production System (CPPS) which offers benefits of autonomy, self-organisation, and interoperability can be adopted to increase the flexibility of manufacturing systems. IEC 61499 (International Electrotechnical Commission) function blocks (FBs) are modularised and reusable software components for distributed industrial control. It is a suitable technology to realise a CPPS. Therefore, CPPS and FBs can be combined to realise the HRC system. This paper proposes a framework and the implementation method of IEC 61499 FB based CPPS for physical human-robot interaction (pHRI) which is type of HRC. An industrial robot based CPPS for pHRI is decomposed into modularised FBs that can be networked to fulfil manufacturing tasks. An energy consumption FB based on a novel empirical energy consumption model is also added to the system for energy consumption monitoring of the Robot. An assembly case is used to demonstrate the feasibility of the proposed system. Results show that the FB based CPPS for pHRI possesses the potential capability for HRC based assembly. The future work is also discussed.

  • 31. Zhang, Dan
    et al.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Skövde.
    Gao, Zhen
    An Integrated Approach for Remote Manipulation of a High-Performance Reconfigurable Parallel Kinematic Machine2010In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 29, no 4, p. 164-172Article in journal (Refereed)
    Abstract [en]

    Flexible and effective manipulation is important and meaningful for the further development and applications of parallel manipulators in the industrial fields, especially for high-performance manufacturing. Web-based manufacturing has emerged as an alternative manufacturing technology in a distributed environment. In this paper, an integrated approach is proposed for remote manipulation of the reconfigurable parallel kinematic machine (RPKM) based on sensor-driven Wise-ShopFloor framework. The concept of Wise-ShopFloor integrates the modules of detailed architecture design, module interactions, sensor data utilization and model predictive control. In order to demonstrate the efficiency of this novel methodology, an example of a five degrees-of-freedom (DOF) RPKM is developed for surface finishing. The reconfigurability, the necessary kinematic analysis, and the performance mapping of the 5-DOF RPKM are conducted so as to implement the proposed approach.

  • 32.
    Zhang, Yuyan
    et al.
    Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan, Hubei, Peoples R China..
    Li, Xinyu
    Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan, Hubei, Peoples R China..
    Gao, Liang
    Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan, Hubei, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wen, Long
    Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan, Hubei, Peoples R China..
    Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning2018In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 48, p. 34-50Article in journal (Refereed)
    Abstract [en]

    Imbalanced data problems are prevalent in the real rotating machinery applications. Traditional data-driven diagnosis methods fail to identify the fault condition effectively for lack of enough fault samples. Therefore, this study proposes an effective three-stage fault diagnosis method towards imbalanced data. First, a new synthetic oversampling approach called weighted minority oversampling (WMO) is devised to balance the data distribution. It adopts a new data synthesis strategy to avoid generating incorrect or unnecessary samples. Second, to select useful features automatically, an enhanced deep auto-encoder (DA) approach is adopted. DA is improved in two aspects: 1) a new cost function based on maximum correntropy and sparse penalty is designed to learn sparse robust features; 2) a fine-tuning operation with a self-adaptive learning rate is developed to ensure the good convergence performance. Finally, the C4.5 decision tree identifies the learned features. The proposed method named WMODA is evaluated on 25 benchmark imbalanced datasets. It achieves better results than five well-known imbalanced data learning methods. It is also evaluated on a real engineering dataset. The experimental results show that WMODA can detect more fault samples than the traditional data-driven methods.

1 - 32 of 32
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