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  • 101.
    Ji, Wei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Harbin Univ Sci & Technol, Harbin 150080, Peoples R China.
    Liu, Xianli
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Harbin Univ Sci & Technol, Harbin 150080, Peoples R China.
    Sun, Shilong
    Experimental evaluation of polycrystalline diamond (PCD) tool geometries at high feed rate in milling of titanium alloy TC112015In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 77, no 9-12, p. 1549-1555Article in journal (Refereed)
    Abstract [en]

    Titanium alloys are widely used in aerospace industrial components characterised by high material removal rate, of which the machining efficiency is a big issue. Targeting the problem, this paper presents the experimental findings of milling of titanium alloy TC11 using polycrystalline diamond (PCD) cutting tool at high feed rate. First, in order to verify the capability of PCD in finish milling of titanium alloys at high feed rate, the surface roughness R-a is investigated under different PCD tool geometries (radial rake angle, axial rake angle and insert sharp radius), and the results indicate that its range is from 0.821 to 1.562 mu m, which is suitable to titanium components. Also, the main tool failure patterns, cutting edge fracture and flank face wear, are observed and classified. Based on the tool failure patterns, the relationship between tool life and tool geometries is established. In order to explain the reasons of tool failures, the relationships between cutting forces and the tool geometries are made clear. Finally, the processes of flank face wear and rake face wear of PCD insert are proposed to show its wear evaluations.

  • 102.
    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.

  • 103.
    Ji, Wei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Industrial robotic machining: a review2019In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 103, no 1-4, p. 1239-1255Article, review/survey (Refereed)
    Abstract [en]

    For the past three decades, robotic machining has attracted a large amount of research interest owning to the benefit of cost efficiency, high flexibility and multi-functionality of industrial robot. Covering articles published on the subjects of robotic machining in the past 30 years or so; this paper aims to provide an up-to-date review of robotic machining research works, a critical analysis of publications that publish the research works, and an understanding of the future directions in the field. The research works are organised into two operation categories, low material removal rate (MRR) and high MRR, according their machining properties, and the research topics are reviewed and highlighted separately. Then, a set of statistical analysis is carried out in terms of published years and countries. Towards an applicable robotic machining, the future trends and key research points are identified at the end of this paper.

  • 104. 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.

  • 105.
    Ji, Wei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Harbin University of Science and Technology, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Haghighi, Azadeh
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. University of Illinois at Chicago, United States.
    Givehchi, Mohammad
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Liu, Xianli
    An enriched machining feature based approach to cutting tool selection2018In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 31, no 1, p. 1-10Article in journal (Refereed)
    Abstract [en]

    Cutting tools, considered as a basic prerequisite machining resource, are generally selected according to the selected machining methods, which cannot fit in the current manufacturing environment where small- and medium-sized enterprises (SMEs) are the major manufacturers. For the survival of SMEs, it is critical to develop methods for selecting proper cutting tools and reducing machining cost according to product data. Therefore, this study proposes an enriched machining feature (MF)-based approach towards adaptive cutting tool and machining method selection, in which both machinability and machining cost of MF are considered. It includes a two-step workflow: filtering and optimisation. In the filtering process, cutting tools are filtered according to workpiece materials, geometries of MFs and cutting tool inventory, respectively. Here, MF geometries depend on Machining Limit Value decided by sizes and interference relationships of MFs. Also, the client is suggested to choose proper new cutting tools. In the optimisation process, the filtered cutting tools are considered for all the MFs, and machining costs are calculated for each option, in order to select the cheapest one. In particular, if similar cutting tools are required for different MFs, the cutting tool selection for these MFs should be performed altogether.

  • 106.
    Ji, Wei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Yuquan
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Liu, Hongyi
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Interface architecture design for minimum programming in human-robot collaboration2018In: 51st CIRP Conference on Manufacturing Systems, Elsevier, 2018, Vol. 72, p. 129-134Conference paper (Refereed)
    Abstract [en]

    Many metal components, especially large-sized ones, need to be ground or deburred after turning or milling to improve the surface qualities, which heavily depends on human interventions. Robot arms, combining movable platforms, are applied to reduce the human work. However, robots and human should work together due to the fact that most of the large-sized parts belong to small-batch products, resulting in a large number of programming for operating a robot and movable platform. Targeting the problem, this paper proposes a new interface architecture towards minimum programming in human-robot collaboration. Within the context, a four-layer architecture is designed: user interface, function block (FB), functional modules and hardware. The user interface is associated with use cases. Then, FB, with embedded algorithms and knowledge and driven by events, is to provide a dynamic link to the relevant application interface (APIs) of the functional modules in terms of the case requirements. The functional modules are related to the hardware and software functions; and the hardware and humans are considered in terms of the conditions on shop floors. This method provides three-level applications based on the skills of users: (1) the operators on shop floors, can operate both robots and movable platforms programming-freely; (2) engineers are able to customise the functions and tasks by dragging/dropping and linking the relevant FBs with minimum programming; (3) the new functions can be added by importing the APIs through programming.

  • 107.
    Ji, Wei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Yin, S.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    A Virtual Training Based Programming-Free Automatic Assembly Approach for Future Industry2018In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 43865-43873, article id 8425978Article in journal (Refereed)
    Abstract [en]

    Currently, the automated assembly depends on advance programming, which is suitable to large-batch products assembly; however, it does not fit in the assembly of small-batch products due to the large amount of preparatory work including assembly planning and robot programming. Therefore, those assemblies in small batch largely rely on human interventions, which is a system-level problem. Targeting the problem, this paper presents a novel programming-free automated assembly planning and control approach based on virtual training. Within the context, the 3-D models of products are used, including general assembly features of each component. The features are used in a search-based planner to generate assembly sequence, and to plan assembly path. Then the virtual assembly simulation is carried out based on the generated assembly plan, where the collisions and contacts are captured and passed to the planner to regenerate a new path. The new path is simulated in the virtual world. The simulation process is repeated until an executable strategy is obtained. In the real world, the physical robots perform the actual assembly by following the trained sequences and paths that are calibrated according to the real positions and orientations of the components. A proof-of-concept case study is carried out in robot operating system environment to validate this approach.

  • 108.
    Ji, Wei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Yin, Shubin
    Harbin Univ Sci & Technol, Dept Mech Engn, Harbin, Heilongjiang, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    A big data analytics based machining optimisation approach2019In: Journal of Intelligent Manufacturing, ISSN 0956-5515, E-ISSN 1572-8145, Vol. 30, no 3, p. 1483-1495Article in journal (Refereed)
    Abstract [en]

    Currently, machine tool selection, cutting tool selection and machining conditions determination are not usually performed at the same time but progressively, which may lead to suboptimal or trade-off solutions. Targeting this issue, this paper proposes a big data analytics based optimisation method for enriched Distributed Process Planning by considering machine tool selection, cutting tool selection and machining conditions determination simultaneously. Within the context, the machining resources are represented by data attributes, i.e. workpiece, machining requirement, machine tool, cutting tool, machine conditions, machining process and machining result. Consequently, the problem of machining optimisation can be treated as a statistic problem and solved by a hybrid algorithm. Regarding the algorithm, artificial neural networks based models are trained by machining data and used as optimisation objectives, whereas analytical hierarchy process is adopted to decide the weights of the multi-objective optimisation; and evolutionary algorithm or swarm intelligence is proposed to perform the optimisation. Finally, the results of a simplified proof-of-concept case study are reported to validate the proposed approach, where a Deep Belief Network model was trained by a set of hypothetic data and used to calculate the fitness of a genetic algorithm.

  • 109. Jiang, Jin-gang
    et al.
    Bi, Zhu-ming
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Hu, Hai-yan
    Dong, Wei
    Yu, Ling-tao
    Medical robotics2015In: Advances in Mechanical Engineering, ISSN 1687-8132, E-ISSN 1687-8140, Vol. 7, no 7, article id 1687814015593230Article in journal (Other academic)
  • 110. 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.

  • 111. Keshavarzmanesh, Shadi
    et al.
    Wang, Lihui
    University of Skövde.
    Feng, Hsi-Yung
    A Hybrid Approach for Dynamic Routing Planning in an Automated Assembly Shop2010In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 26, no 6, p. 768-777Article in journal (Refereed)
    Abstract [en]

    Highly turbulent environment of dynamic job-shop operations affects shop floor layout as well as manufacturing operations. Due to the dynamic nature of layout changes, essential requirements such as adaptability and responsiveness to the changes need to be considered in addition to the cost issues of material handling and machine relocation when reconfiguring a shop floor's layout. Here, based on the source of uncertainty, the shop floor layout problem is split into two sub-problems and dealt with by two modules: re-layout and find-route. GA is used where changes cause the entire shop re-layout, while function blocks are utilised to find the best sequence of robots for the new conditions within the existing layout. This paper reports the latest development to the authors' previous work

  • 112. Keshavarzmanesh, Shadi
    et al.
    Wang, Lihui
    Feng, Hsi-Yung
    Adaptive Assembly Planning and Control in a Two-robot Assembly Cell2008In: Proceedings of the 2nd CIRP Conference on Assembly Technologies and Systems, 2008, p. 155-163Conference paper (Refereed)
  • 113. Keshavarzmanesh, Shadi
    et al.
    Wang, Lihui
    Feng, Hsi-Yung
    Adaptive Assembly Planning and Control Using Function Block Technology2008In: Transactions of the North American Manufacturing Research Institution of SME, ISSN 1047-3025, Vol. 36, p. 477-484Article in journal (Refereed)
  • 114. Keshavarzmanesh, Shadi
    et al.
    Wang, Lihui
    University of Skövde.
    Feng, Hsi-Yung
    Design and Simulation of an Adaptive and Collaborative Assembly Cell2010In: International Journal of Manufacturing Research, ISSN 1750-0591, Vol. 5, no 1, p. 102-119Article in journal (Refereed)
    Abstract [en]

    Nowadays, product-mix in small batches contributes to shop floor uncertainties, whereas distributed resources are handled collaboratively during assembly planning. There is a growing need to develop methods that can increase adaptability and flexibility in dynamic and collaborative job-shop assembly floors. Based on our previous work on an assembly planning framework using Function Blocks (FBs), a novel approach to assembly planning and control is developed, which enables adaptive decision making besides effective plan execution. Following our previous work, this paper reports the latest development of design and simulation of an FB communication network in Matlab-Simulink environment, and validates the methodology through an example.

  • 115. Keshavarzmanesh, Shadi
    et al.
    Wang, Lihui
    University of Skövde.
    Feng, Hsi-Yung
    Increasing Adaptability of Assembly Planning and Control with Embedded Decision-Making Capability2009In: Transactions of the North American Manufacturing Research Institution of SME, ISSN 1047-3025, Vol. 37, p. 533-540Article in journal (Refereed)
    Abstract [en]

    In our previous work, a framework and a new methodology for adaptive assembly process planning using function block (FB) concept was introduced. Function blocks are adopted to deal with dynamic job shop assembly floors, where product-mix often in small batches contributes to manufacturing uncertainty, makingadaptability an important item on a company's wish list. FB-enabled assembly planning and control enables not only adaptive decision making but effective plan execution. Following our previous work, this paper reports the development of simulating a function block communication network in the Matlab Simulink environment and demonstrating the implementation of the methodology through an example.

  • 116. Keshavarzmanesh, Shadi
    et al.
    Wang, Lihui
    University of Skövde.
    Feng, Hsi-Yung
    Two-Stage Hybrid Adaptive Assembly Layout Planning2010In: Transactions of the North American Manufacturing Research Institution of SME, ISSN 1047-3025, Vol. 38, p. 735-742Article in journal (Refereed)
    Abstract [en]

    The manufacturing environment today is highly turbulent. Hence, the potential to alter factory layout has transformed the layout problem from considering long-term material handling costs to considering essential requirements, such as adaptability and proactive responsiveness to dynamic changes. This is beyond the costs of material handling and machine relocation when reconfiguring the layout. As continuation of the authors' previous work, this paper proposes to incorporate function block methodology in dealing with the layout issues in the frequently changing environment of job-shop assembly operations so as to increase the autonomy and adaptability of the assembly operations against changes.

  • 117. Kjellberg, T.J.A.
    et al.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Procedia CIRP of the 26th CIRP Design Conference2016Book (Refereed)
  • 118.
    Kjellberg, Torsten J. A.
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Editorial: Creative Design of Products and Production Systems2016In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 50Article in journal (Refereed)
  • 119. Koh, S.C.L.
    et al.
    Wang, Lihui
    Overview of Enterprise Networks and Logistics for Agile Manufacturing2010In: Enterprise Networks and Logistics for Agile Manufacturing, Springer London, 2010, p. 1-10Chapter in book (Refereed)
  • 120. Krueger, J.
    et al.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Verl, A.
    Bauernhansl, T.
    Carpanzano, E.
    Makris, S.
    Fleischer, J.
    Reinhart, G.
    Franke, J.
    Pellegrinelli, S.
    Innovative control of assembly systems and lines2017In: CIRP annals, ISSN 0007-8506, E-ISSN 1726-0604, Vol. 66, no 2, p. 707-730Article in journal (Refereed)
    Abstract [en]

    The increasing demand for flexibility and reconfigurability of assembly lines generates new challenges for the control of these lines and their subsystems, such as robots, grippers, conveyors or automated guided vehicles. Also new requirements for their interaction between each other and the environment as well as with humans arise. On the other hand the rapid change of information and communication technology opens new potentials for innovative control. Due to the high degree of interconnection between controllers, actuators and sensors, the classical automation pyramid is replaced by networked structures with a higher degree of flexibility, but also higher complexity. This trend is supported by the ability to collect and process data within cloud environments, the rapid increase of computational power of decentralized and embedded controllers and the high potential of machine learning for automation. This keynote gives an overview of innovative approaches in ICT and robotics for flexible control and automation of assembly lines and systems. 

  • 121.
    Lanza, Gisela
    et al.
    Karlsruhe Inst Technol, Wbk Inst Prod Sci, Kaiserstr 12, D-76131 Karlsruhe, Germany..
    Ferdows, Kasra
    Georgetown Univ, McDonough Sch Business, Washington, DC 20057 USA..
    Kara, Sami
    Univ New South Wales, Sustainable Mfg & Life Cycle Engn Res Grp, Sch Mech & Mfg Engn, Sydney, NSW 2052, Australia..
    Mourtzis, Dimitris
    Univ Patras, Lab Mfg Syst & Automat, Dept Mech Engn & Aeronaut, Patras 26500, Greece..
    Schuh, Guenther
    Rhein Westfal TH Aachen, Inst Machine Tools & Prod Engn, Steinbachstr 19, D-52064 Aachen, Germany..
    Vancza, Jozsef
    Hungarian Acad Sci, Inst Comp Sci & Control, Budapest, Hungary.;Budapest Univ Technol & Econ, Dept Mfg Sci & Technol, Budapest, Hungary..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wiendahl, Hans-Peter
    Leibniz Univ Hannover, IFA Inst Prod Syst & Logist, Hannover, Germany..
    Global production networks: Design and operation2019In: CIRP annals, ISSN 0007-8506, E-ISSN 1726-0604, Vol. 68, no 2, p. 823-841Article in journal (Refereed)
    Abstract [en]

    Industrial companies are nowadays acting in global production networks (GPNs). A comprehensive scientific overview of those networks is still missing. To close this gap, a framework for designing and operating GPNs is introduced. It structures influencing factors, challenges, enablers and outlines the need for decision support systems. The state of the art in designing and operating GPNs is reviewed. Three trends are identified that help to transform historical grown networks into changeable GPNs with a focused network footprint. In conclusion, a need for future research in forming the production strategy, designing the network footprint and managing the network is given. Elsevier Ltd on behalf of CIRP.

  • 122. 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.

  • 123. Li, Weidong
    et al.
    Wang, Lihui
    Li, Xinyu
    Gao, Liang
    Intelligent Optimisation for Integrated Process Planning and Scheduling2011In: Multi-objective Evolutionary Optimisation for Product Design and Manufacturing, Springer London, 2011, p. 305-324Chapter in book (Refereed)
  • 124. Li, Weidong
    et al.
    Xia, K.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Chao, K. M.
    Gao, L.
    Selective Disassembly Planning for Sustainable Management of Waste Electrical and Electronic Equipment2013In: Re-engineering Manufacturing for Sustainability: Proceedings of the 20th Cirp International Conference on Life Cycle Engineering, Singapore 17-19 April, 2013, 2013, p. 341-346Conference paper (Refereed)
    Abstract [en]

    Waste Electrical and Electronic Equipment (WEEE) are one of the most significant waste streams in modern societies. Full disassembly of WEEE is rarely an ideal solution due to high disassembly costs. Selective disassembly, which prioritizes operations for partial disassembly according to the legislative and economic considerations of specific stakeholders, is becoming an important yet still challenging research topic in recent years. In this paper, a Particle Swarm Optimization (PSO)-based selective disassembly planning method embedded with customizable decision-making models has been developed. The developed method is flexible to handle WEEE to meet the various requirements of stakeholders, and is capable to achieve optimized selective plans. Practical cases on Liquid Crystal Display (LCD) televisions have been used to verify and demonstrate the effectiveness of the research in application scenarios.

  • 125. Li, Xiaoqian
    et al.
    Wang, Lihui
    Integrated Manufacturing Technologies Institute, National Research Council Canada.
    Cai, Ningxu
    Machine Vision Based Surface Finish Inspection for Cutting Tool Replacement in Production2004In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 42, no 11, p. 2279-2287Article in journal (Refereed)
    Abstract [en]

    A quick and reliable assessment of machined surface finish is required for quality control in shop floors. This paper presents a machine-vision-based approach to inspect the turned surface in finishing operations. The distribution pattern from a laser scattering image is used to characterize the topography of the surface finish and to set criteria for cutting tool replacement. Compared with conventional surface inspections, this approach gives a feasible solution to assure machined surface finish and to establish reasonable criteria for cutting tool replacement in production.

  • 126. Li, Xiaoqian
    et al.
    Wang, Lihui
    Integrated Manufacturing Technologies Institute, National Research Council of Canada .
    Cai, Ningxu
    On-line Calibration of Positioning Accuracy of CNC Lathe Using a Double-frequency Laser Interferometer2005In: International journal of computer applications in technology, ISSN 0952-8091, E-ISSN 1741-5047, Vol. 24, no 4, p. 212-217Article in journal (Refereed)
    Abstract [en]

    Positioning accuracy is an important factor affecting the performance of CNC machine tools. Targeting a high positioning accuracy, a fast and effective calibration technique is in an increasing demand from both machine tool users and manufacturers. This paper introduces an on-line calibration approach to dynamically evaluate the positioning accuracy of a CNC lathe through statistical estimation. A double-frequency laser interferometer is configured to measure the respective motions in the lathe's two perpendicular axes during the machining process, triggered by a cutting force dynamometer. Positioning accuracycalibration is realised by comparing the nominal positions from the CNC motion controller to their actual values measured by the laser interferometer system. Compared to that achieved at the machine idle state or the free-running state using conventional techniques, this on-line calibration approach provides a more efficient way to get a real picture of the actual performance of a CNC machine tool under its working condition. It also gives a truthful reference to effectively compensate positioning errors of the CNC machine tool.

  • 127. Li, Y.
    et al.
    Li, J.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Recycling of PBDEs Containing Plastics from Waste Electrical and Electronic Equipment (WEEE): A Review2013In: Proceedings - 2013 IEEE 10th International Conference on e-Business Engineering, ICEBE 2013, IEEE , 2013, p. 407-412Conference paper (Refereed)
    Abstract [en]

    With the rapid growth of waste electrical and electronic equipment (WEEE) and hindrance to the distribution of recycled polymers due to the presence of polybrominated diphenyl ethers (PBDEs), plastics from WEEE have been an important environmental problem. Currently, the recycling activities on polymeric materials are increasing and becoming more and more important. So, it is imperative to recycle WEEE plastics containing PBDEs in an environmentally sound manner. Based on an overview about PBDEs content in WEEE plastics, three main recycling options have been investigated and a recycling model is established. The emission of PBDE congeners and formation of polybrominated dibenzo dioxins/furans (PBDD/Fs) during various recycling processes is summarized, and the detrimental impact on environment and human health is compared. The review information in this study can provide the basis for environmental friendly management of PBDEs containing plastics.

  • 128.
    Lian, Binbin
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
    Elastodynamic modeling and parameter sensitivity analysis of a parallelmanipulator with articulated traveling plate2019In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015Article in journal (Refereed)
    Abstract [en]

    This paper deals with the elastodynamic modeling and parameter sensitivity analysis of a parallel manipulator with articulated traveling plate (PM-ATP) for assembling large components in aviation and aerospace. In the elastodynamic modeling, the PM-ATP is divided into four levels, i.e., element, part, substructure, and the whole mechanism. Herein, three substructures, including translation, bar, and ATP, are categorized according to the composition of the PM-ATP. Based on the kineto-elastodynamic (KED) method, differential motion equations of lower levels are formulated and assembled to build the elastodynamic model of the upper level. Degrees of freedom (DoFs) at connecting nodes of parts and deformation compatibility conditions of substructures are considered in the assembling. The proposed layer-by-layer method makes the modeling process more explicit, especially for the ATP having complex structures and multiple joints. Simulations by finite element software and experiments by dynamic testing system are carried out to verify the natural frequencies of the PM-ATP, which show consistency with the results from the analytical model. In the parameter sensitivity analysis, response surface method (RSM) is applied to formulate the surrogate model between the elastic dynamic performances and parameters. On this basis, differentiation of performance reliability to the parameter mean value and standard variance are adopted as the sensitivity indices, from which the main parameters that greatly affect the elastic dynamic performances can be selected as the design variables. The present works are necessary preparations for future optimal design. They can also provide reference for the analysis and evaluation of other PM-ATPs.

  • 129.
    Lian, Binbin
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
    Wang, Xi Vincent
    KTH, Centres, XPRES, Excellence in production research. KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Static and dynamic optimization of a pose adjusting mechanism considering parameter changes during constructionIn: Article in journal (Refereed)
  • 130.
    Lian, Binbin
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
    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.
    Static and dynamic optimization of a pose adjusting mechanism considering parameter changes during construction2019In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 59, p. 267-277Article in journal (Refereed)
    Abstract [en]

    Having potentially high stiffness and good dynamic response, a parallel pose adjusting mechanism was proposed for being an attachment to a big serial robot of a macro-micro robotic system. This paper addresses its design optimization problem mainly concerning arrangements of design variables and objectives. Parameter changes during construction are added to the design variables in order to prevent the negative effects to the physical prototype. These parameter changes are interpreted as parameter uncertainty and modeled by probabilistic theory. For the objectives, both static and dynamic performances are simultaneously optimized by Pareto-based method. The involved performance indices are instantaneous energy based stiffness index, first natural frequency and execution mass. The optimization procedure is implemented as: (1) carrying out performance modeling and defining performance indices, (2) reformulating statistical objectives and probabilistic constraints considering parameter uncertainty, (3) conducting Pareto-based optimization with the aid of response surface method (RSM) and particle swarm optimization (PSO), (4) selecting optimal solution by searching for cooperative equilibrium point (CEP). By addressing parameter uncertainty and the best compromise among multiple objectives, the presented optimization procedure provides more reliable optimal parameters that would not be affected by minor parameter changes during construction, and less biased optimum between static and dynamic performances comparing with the conventional optimization methods. The proposed optimization method can also be applied to the other similar mechanisms.

  • 131. Liang, S.
    et al.
    Rajora, M.
    Liu, X.
    Yue, C.
    Zou, P.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM).
    Intelligent manufacturing systems: A review2018In: International Journal of Mechanical Engineering and Robotics Research, ISSN 2278-0149, Vol. 7, no 3, p. 324-330Article, review/survey (Refereed)
    Abstract [en]

    Manufacturing factories, having continuous pursuit of productivity and quality, often meet challenges in coping with high production complexities and uncertainties. These are the areas in which traditional manufacturing paradigms underperform due to the limitation of human operators' ability to cope with these complexities, uncertainties, understanding/memorizing big data, and also their inability to make time demanding decisions. Intelligent manufacturing systems, on the other hand, can yield superior results compared to traditional manufacturing systems as they are capable of analyzing, self-learning, apprehending complexities and are also able to store and analyze large amounts of data to obtain increased quality of the product and lower production cost while shortening the time-to-market. The aim of this paper is to outline the recent accomplishments and developments in intelligent scheduling, process optimization, control, and maintenance. For each aspect, concepts, requirements, application implemented, and methodologies deployed are also presented.

  • 132. Lin, Q.
    et al.
    Xia, K.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Gao, L.
    Cloud manufacturing in China: A literature survey2014In: International Journal of Manufacturing Research, ISSN 1750-0591, Vol. 9, no 4, p. 369-388Article in journal (Refereed)
    Abstract [en]

    Cloud manufacturing has been of considerable interest to Chinese academic researchers over the last decade. This paper presents a broad perspective of the research on cloud manufacturing in China. The topics studied mainly include design of cloud manufacturing architecture, resource and capability virtualisation, combinatorial optimisation of virtual resource and capability, design and collaboration of cloud manufacturing services, intelligent searching and matching methods and trust evaluation. The present literature survey also includes two successful cases applying cloud manufacturing in China to verify the feasibility of the cloud manufacturing architecture and services. Potentially interesting directions for future research in this area are also identified.

  • 133. Lin, Q.
    et al.
    Xia, K.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Gao, L.
    Research progress of cloud manufacturing in China: A literature survey2013In: ASME 2013 International Manufacturing Science and Engineering Conference Collocated with the 41st North American Manufacturing Research Conference, MSEC 2013, 2013Conference paper (Refereed)
    Abstract [en]

    Cloud manufacturing has been of considerable interest to Chinese academic researchers over the last decade. This paper presents a broad perspective of the research on cloud manufacturing in China. The topics studied mainly include design of cloud manufacturing architecture, resource and capability virtualization, combinatorial optimization of virtual resource and capability, design and collaboration of cloud manufacturing services, intelligent searching and matching method and trust evaluation. The present literature survey also includes two successful cases applying cloud manufacturing in China to verify the feasibility of the cloud manufacturing architecture and services. Potentially interesting directions for future research in this area are also identified.

  • 134. Liu, Dawei
    et al.
    Wang, Wei
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Energy-Efficient Cutting Parameters Determination for NC Machining with Specified Machining Accuracy2017In: 24TH CIRP CONFERENCE ON LIFE CYCLE ENGINEERING / [ed] Takata, S Umeda, Y Kondoh, S, Elsevier, 2017, p. 523-528Conference paper (Refereed)
    Abstract [en]

    An energy-efficient cutting parameters determination method for NC machining parts with specified machining accuracy is proposed in this paper. The identification mechanism of machine tool errors by R-test is employed to establish machining error estimation model. By replacing the coordinate of each tool path point with the origin, the R-test tool paths are generated. R-tests at two different setups are designed to capture the volumetric errors of the machine tool. After dynamic R-test simulations at different feedrates in two setups, the volumetric errors of the machine tool at different feedrates are predicted. With the volumetric errors, the maximum machining errors of the part at different feedrates are calculated based on the machining error estimation model. The allowed feedrate range is obtained by comparing the calculated machining errors with the specified machining accuracy. With the allowed feedrate range and the other cutting parameters as the factors, the response surface of total energy consumption of machine tool is constructed according to the requirements of machining operations. The energy-efficient cutting parameters combination is found by searching the constructed response surface. A typical aircraft structural part is used to validate the proposed method.

  • 135.
    Liu, Hongyi
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Fang, Tongtong
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Zhou, Tianyu
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Towards Robust Human-Robot Collaborative Manufacturing: Multimodal Fusion2018In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 74762-74771Article in journal (Refereed)
    Abstract [en]

    Intuitive and robust multimodal robot control is the key toward human-robot collaboration (HRC) for manufacturing systems. Multimodal robot control methods were introduced in previous studies. The methods allow human operators to control robot intuitively without programming brand-specific code. However, most of the multimodal robot control methods are unreliable because the feature representations are not shared across multiple modalities. To target this problem, a deep learning-based multimodal fusion architecture is proposed in this paper for robust multimodal HRC manufacturing systems. The proposed architecture consists of three modalities: speech command, hand motion, and body motion. Three unimodal models are first trained to extract features, which are further fused for representation sharing. Experiments show that the proposed multimodal fusion model outperforms the three unimodal models. This paper indicates a great potential to apply the proposed multimodal fusion architecture to robust HRC manufacturing systems.

  • 136.
    Liu, Hongyi
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Sustainable production development.
    Fang, Tongtong
    KTH.
    Zhou, Tianyu
    KTH.
    Wang, Yuquan
    KTH, School of Industrial Engineering and Management (ITM), Sustainable production development.
    Wang, Lihui
    Deep Learning-based Multimodal Control Interface for Human-Robot Collaboration2018In: 51st CIRP Conference on Manufacturing Systems, Elsevier, 2018, Vol. 72, p. 3-8Conference paper (Refereed)
    Abstract [en]

    In human-robot collaborative manufacturing, industrial robot is required to dynamically change its pre-programmed tasks and collaborate with human operators at the same workstation. However, traditional industrial robot is controlled by pre-programmed control codes, which cannot support the emerging needs of human-robot collaboration. In response to the request, this research explored a deep learning-based multimodal robot control interface for human-robot collaboration. Three methods were integrated into the multimodal interface, including voice recognition, hand motion recognition, and body posture recognition. Deep learning was adopted as the algorithm for classification and recognition. Human-robot collaboration specific datasets were collected to support the deep learning algorithm. The result presented at the end of the paper shows the potential to adopt deep learning in human-robot collaboration systems.

  • 137.
    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.
    Gesture Recognition for Human-Robot Collaboration: A Review2016Conference paper (Refereed)
  • 138.
    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.

  • 139.
    Liu, Hongyi
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Yuquan
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Ji, Wei
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    A Context-Aware Safety System for Human-Robot Collaboration2018In: Procedia Manufacturing, Elsevier B.V. , 2018, p. 238-245Conference paper (Refereed)
    Abstract [en]

    Recent advancements in human-robot collaboration have enabled humans and robots to work together in shared manufacturing environment. However, there still exist needs for a context-aware safety system that not only assures human safety but also provides system efficiency. In this paper, the authors present a context-aware safety system that provides safety and efficiency at the same time. The system can plan robotic paths that avoid colliding with humans while still reach target positions in time. Human poses can also be recognised by the system to further increase system efficiency. Different modules, algorithms, and interfaces are introduced in the paper to support the context-aware safety system for human-robot collaboration. A test case is demonstrated to validate the performance of the system. Finally, a summary and future research directions are given.

  • 140.
    Liu, Sichao
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Wang, Yuquan
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    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.
    Energy-efficient trajectory planning for an industrial robot using a multi-objective optimisation approach2018Conference paper (Refereed)
    Abstract [en]

    This paper presents an approach for energy-efficient trajectory planning of an industrial robot. A model that can be used to formulate the energy consumption of the robot with the kinematics constraints is developed. Given the trajectory in the Cartesian space, the septuple B-spline is applied in joint space trajectory planning to make the velocities, accelerations, and jerks bounded and continuous, with constraints on the initial and ending values. Then, energy-efficient optimisation problem with nonlinear constraints is discussed. Simulation results show that, the proposed approach is effective solution to trajectory planning, with ensuring a good energy improvement and fluent movement for the robot manipulators.

  • 141.
    Liu, Sichao
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Zhang, G.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    IoT-enabled Dynamic Optimisation for Sustainable Reverse Logistics2018In: 25th CIRP Life Cycle Engineering (LCE) Conference, 30 April – 2 May 2018, Copenhagen, Denmark, Elsevier, 2018, Vol. 69, p. 662-667Conference paper (Refereed)
    Abstract [en]

    Currently, typical challenges that logistics industry faces include the exploding logistics (including reverse logistics) tasks, the lack of real-time and accurate logistics information, and demands towards sustainable logistics. Therefore, it is difficult for logistic companies to achieve highly-efficient and sustainable reverse logistics. This paper adopts a bottom-up logistics strategy that aims to achieve the real-time information-driven dynamic optimisation distribution for logistics tasks. Under this strategic framework, an IoT-enabled real-time information sensing model is designed to sense and capture the real-time data of logistics resources, which are shared among companies after the value-added processes. Real-time information-driven dynamic optimisation for logistics tasks is proposed to optimise the configuration of logistics resources, reduce logistics cost, energy consumption and the distribution distance, and alleviate the environmental pollution. The objective of this research is to develop an innovative logistics distribution model for sustainable logistics.

  • 142.
    Liu, Sichao
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Zhang, Yingfeng
    Northwestern Polytech Univ, Key Lab Contemporary Design & Integrated Mfg Tech, Minist Educ, Xian 710072, Shaanxi, Peoples R China.;Southern Univ Sci & Technol, Dept Mech & Energy Engn, Shenzhen 518055, Guangdong, Peoples R China..
    Liu, Yang
    Linkoping Univ, Dept Management & Engn, SE-58183 Linkoping, Sweden.;Univ Vaasa, Dept Prod, Vaasa 65200, Finland..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    An 'Internet of Things' enabled dynamic optimization method for smart vehicles and logistics tasks2019In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 215, p. 806-820Article in journal (Refereed)
    Abstract [en]

    Centralized and one-way logistics services and the lack of real-time information of logistics resources are common in the logistics industry. This has resulted in the increased logistics cost, energy consumption, logistics resources consumption, and the decreased loading rate. Therefore, it is difficult to achieve efficient, sustainable, and green logistics services with dramatically increasing logistics demands. To deal with such challenges, a real-time information-driven dynamic optimization strategy for smart vehicles and logistics tasks towards green logistics is proposed. Firstly, an 'Internet of Things'-enabled real-time status sensing model of logistics vehicles is developed. It enables the vehicles to obtain and transmit real-time information to the dynamic distribution center, which manages value-added logistics information. Then, such information can be shared among logistics companies. A dynamic optimization method for smart vehicles and logistics tasks is developed to optimize logistics resources, and achieve a sustainable balance between economic, environmental, and social objectives. Finally, a case study is carried out to demonstrate the effectiveness of the proposed optimization method. The results show that it contributes to reducing logistics cost and fuel consumption, improving vehicles' utilization rate, and achieving real-time logistics services with high efficiency. reserved.

  • 143. Liu, X.
    et al.
    Li, Y.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    A Cloud Manufacturing Architecture for Complex Parts Machining2015In: Journal of manufacturing science and engineering, ISSN 1087-1357, E-ISSN 1528-8935, Vol. 137, no 6, article id 061009Article in journal (Refereed)
    Abstract [en]

    Service provider (SP) know-hows are essential in machining service (MS) encapsulation in the cloud. However, since the acquisition of the know-hows for complex parts machining requires investing considerable manpower and resources in R&D, this kind of machining know-hows is usually considered as one of the core competences of the SP who makes them unshareable. Targeting the problem, this paper presents a new cloud manufacturing (CM) architecture in which MSs are encapsulated within each SP with standardized machining task description strategies (SMTDS). Only the capability information about what the SP can do is provided to the cloud. During service matching, SMTDS is also applied for user request formulation to improve the matching efficiency and quality. For complex parts in large size, high machining requirements, high value, short delivery cycle, and complex structures, e.g., aircraft structural parts, unacceptable machining quality or delivery delay may cause a much greater loss not only in economy. In the proposed CM architecture, to guarantee the feasibility of the MSs for complex structural parts, machining operations for the user preferred services could be generated by mapping the corresponding typical machining plans (TMP) to the part based on the dynamic feature concept to support accurate evaluations of the MSs. The machining of an aircraft structural part is then applied as a test user request to demonstrate how the proposed method works for finding MS for complex parts.

  • 144. Liu, X.
    et al.
    Li, Y.
    Wang, W.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    A feature based method for product-oriented representation to manufacturing resources in cloud manufacturing2014In: ASME 2014 International Manufacturing Science and Engineering Conference, MSEC 2014 Collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference, ASME Press, 2014Conference paper (Refereed)
    Abstract [en]

    In cloud manufacturing, resources are encapsulated into manufacturing services to be provided in the manufacturing cloud. Resources representation is the basis for resources encapsulation. However, traditional representation methods to manufacturing resources mainly focus on the static description and/or current status of equipment. Research in productoriented representation to manufacturing capabilities is limited. As a result, the evaluation to resources in the manufacturing cloud is indirect which will complicate the decision making in service determination. This paper presents a feature based method for manufacturing resources representation. Machining features will be first extracted from the part model based on a predefined feature category. Then capabilities of resources linked by the manufacturing cloud to machine the part will be generated by computing the capabilities to machine the features based on a knowledge base composed of the rules to define resource capabilities. With this method, capabilities of manufacturing resources will be associated with certain product and the selection of service from the manufacturing cloud will be greatly facilitated.

  • 145. Liu, X.
    et al.
    Liu, Q.
    Yue, C.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Liang, S. Y.
    Ji, W.
    Gao, H.
    Intelligent Machining Technology in Cutting Process2018In: Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, ISSN 0577-6686, Vol. 54, no 16, p. 45-61Article in journal (Refereed)
    Abstract [en]

    Metal cutting is a very complex process. In the cutting process, the related knowledge and theories of physics, chemistry, mechanics, materials science, vibration, tribology, heat transfer and other fields are involved. The cutting process control has been the focus of the cutting research. With the development of machining technology and the coming of the Industry 4. 0, researchers are getting more concerned with the intelligent machining technology. It is an inevitable trend to apply the intelligent machining technology in the cutting process. The connotation and the application process of intelligent machining technology is expounded to investigate the critical technology in intelligent manufacturing. The research results in the simulation and optimization, cutting process condition monitoring, and optimization control are reviewed. Through analyzing the application prospect and problems of intelligent machining technology, the main scientific problems and key technologies to be solved are proposed. Intelligent machining is the development direction of processing technology. The application of intelligent machining technology in the cutting process will bring another technological revolution in the manufacturing industry.

  • 146.
    Liu, Xianli
    et al.
    School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China.
    Chen, Zhan
    School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China.
    Ji, Wei
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Iteration-based error compensation for a worn grinding wheel in solid cutting tool flute grinding2019In: Procedia Manufacturing, Elsevier B.V. , 2019, p. 161-167Conference paper (Refereed)
    Abstract [en]

    Solid carbide end mill are widely used in machining of curved surface parts in many areas, e.g. aerospace, automobile, and energy. In the grinding of its manufacturing, grinding wheels are worn out gradually with the grinding number increasing, resulting in the grinding error if there is no proper error compensation. In order to control the flute parameters precision for a worn wheel, this paper proposes an error compensation method by considering a boundary condition determination of a worn wheel based on an iteration-based calculation of position and orientation of grinding wheel. Within the context, the boundary contact condition between the cutting tool and wear wheel is established by identifying the relevant tool profiles associated with the worn and unworn parts of grinding wheel. On top of that, the disk of grinding wheel which generates the rake angle is detailed by considering contact angle. Finally, the method is implemented by using C#, and validated by a set of numeric simulation based on Matlab®.

  • 147.
    Liu, Xian-Li
    et al.
    Harbin Univ Sci & Technol, Harbin 150080, Heilongjiang, Peoples R China..
    Shi, Jin-Kui
    Harbin Univ Sci & Technol, Harbin 150080, Heilongjiang, Peoples R China..
    Ji, Wei
    Harbin Univ Sci & Technol, Harbin 150080, Heilongjiang, Peoples R China.;KTH Royal Inst Technol, S-10044 Stockholm, Sweden..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Experimental Evaluation on Grinding Texture on Flank Face in Chamfer Milling of Stainless Steel2018In: CHINESE JOURNAL OF MECHANICAL ENGINEERING, ISSN 1000-9345, Vol. 31, no 1, article id UNSP 71Article in journal (Refereed)
    Abstract [en]

    The surface quality of chamfer milling of stainless steel is closed related to the products of 3C (Computer, Communication and Consumer electronics), where a cutter is a major part to achieve that. Targeting a high-quality cutter, an experimental evaluation is carried out on the influence of grinding texture of cutter flank face on surface quality. The mathematic models of chamfer cutter are established, and they are validated by a numerical simulation. Also the grinding data are generated by the models and tested by a grinding simulation for safety reasons. Then, a set of chamfer cutting tools are machined in a five-axis CNC grinding machine, and consist of five angles between the cutting edge and the grinding texture on the 1st flank faces, i.e., 0A degrees, 15A degrees, 30A degrees, 45A degrees and 60A degrees. Furthermore, the machined cutting tools are tested in a series of milling experiments of chamfer hole of stainless steel, where cutting forces and surface morphologies are measured and observed. The results show that the best state of both surface quality and cutting force is archived by the tool with 45A degrees grinding texture, which can provide a support for manufacturing of cutting tool used in chamfer milling.

  • 148. Liu, Xu
    et al.
    Li, Yingguang
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Combining Dynamic Machining Feature With Function Blocks for Adaptive Machining2016In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, Vol. 13, no 2, p. 828-841Article in journal (Refereed)
    Abstract [en]

    Feature-based technologies are widely researched for manufacturing automation. However, in current feature models, features once defined remain constant throughout the whole manufacturing lifecycle. This static feature model is inflexible to support adaptive machining when facing frequent changes to manufacturing resources. This paper presents a new machining feature concept that facilitates responsive changes to the dynamics of machining features in 2.5/3D machining. Basic geometry information for feature construction of complex parts with various intersecting features is represented as a set of meta machining features (MMF). Optimum feature definition is generated adaptively by choosing optimum merging strategies of MMFs according to the capabilities of the selected machine tool, cutter, and cutting parameters. A composite function block for dynamic machining feature modelling is designed with Basic Machining Feature Function Block, Meta Machining Feature Extraction Function Block and Feature Interpreter Function Block. Once changes of the selected machining resources occur, they are informed as input events and machining features are then updated automatically and adaptively based on the event-driven model of function blocks. An example is provided to demonstrate the feasibility and benefits of the developed methodology.

  • 149.
    Liu, Yongkui
    et al.
    Xidian Univ, Ctr Smart Mfg Syst, Sch Mechano Elect Engn, Xian, Shaanxi, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Xu, Xun
    Univ Auckland, Dept Mech Engn, Auckland, New Zealand..
    Jiang, Pingyu
    Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian, Shaanxi, Peoples R China..
    Cloud manufacturing: key issues and future perspectives2019In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052Article in journal (Refereed)
    Abstract [en]

    Since the introduction of the concept of cloud manufacturing in 2010, research on it has been ongoing for more than eight years, and much progress has been made. However, existing research indicates that people lack common and comprehensive understandings of some of the key issues with cloud manufacturing such as the concept, operation model, service mode, technology system, architecture, and essential characteristics. Moreover, few studies discuss in depth the relationships between cloud manufacturing and some closely related concepts such as cloud computing-based manufacturing, Cyber-Physical Systems (CPS), smart manufacturing, Industry 4.0, and Industrial Internet. Knowledge as a core supporting factor in cloud manufacturing has rarely been discussed systematically. Also, so far there has been no standardised definition for cloud manufacturing yet. All these are key issues to be further discussed and analysed in cloud manufacturing. In order to clarify the issues above and provide reference for future research and implementation, this paper conducts a comprehensive, systematic, and in-depth discussion and analysis of the aforementioned issues in cloud manufacturing and presents an alternative definition for cloud manufacturing based on the analysis of 12 existing definitions. Future perspectives of cloud manufacturing are also discussed with respect to both academic research and industrial implementation.

  • 150.
    Liu, Yongkui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Xidian Univ, Ctr Complex Syst, Sch Mechanoelect Engn, Xian, Shaanxi, Peoples R China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Wang, Xi Vincent
    KTH, Centres, XPRES, Excellence in production research. KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Xu, Xun
    Univ Auckland, Dept Mech Engn, Auckland, New Zealand..
    Zhang, Lin
    Beihang Univ, Sch Automat Sci & Elect Engn, Beijing, Peoples R China..
    Scheduling in cloud manufacturing: state-of-the-art and research challenges2019In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 57, no 15-16, p. 4854-4879Article, review/survey (Refereed)
    Abstract [en]

    For the past eight years, cloud manufacturing as a new manufacturing paradigm has attracted a large amount of research interest worldwide. The aim of cloud manufacturing is to deliver on-demand manufacturing services to consumers over the Internet. Scheduling is one of the critical means for achieving the aim of cloud manufacturing. Thus far, about 158 articles have been published on scheduling in cloud manufacturing. However, research on scheduling in cloud manufacturing faces numerous challenges. Thus, there is an urgent need to ascertain the current status and identify issues and challenges to be addressed in the future. Covering articles published on the subject over the past eight years, this article aims to provide a state-of-the-art literature survey on scheduling issues in cloud manufacturing. A detailed statistical analysis of the literature is provided based on the data gathered from the Elsevier's Scopus abstract and citation database. Typical characteristics of scheduling issues in cloud manufacturing are systematically summarised. A comparative analysis of scheduling issues in cloud manufacturing and other scheduling issues such as cloud computing scheduling, workshop scheduling and supply chain scheduling is also carried out. Finally, future research issues and challenges are identified.

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