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  • 1.
    Adamson, Göran
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, Royal Institute of Technology, Stockholm, Sweden.
    Moore, Philip
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    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. 43, 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 run-time 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.
    Department of Engineering, Indiana University Purdue University Fort Wayne, Fort Wayne, IN, USA.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    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.
    Givehchi, Mohammad
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Ng, Amos
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    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. (C) 2011 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

  • 4.
    Holm, Magnus
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    The future shop-floor operators, demands, requirements and interpretations2018In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 47, p. 35-42Article in journal (Refereed)
    Abstract [en]

    The evolution of the manufacturing industry reveals continuous progress and development throughout the years. This evolution not only includes production methodologies and the production equipment, it also includes the working environment of the shop-floor operators. The demands faced by the shop-floor operators have developed from strictly controlled, simple and monotonic tasks to self-controlled team work requiring a holistic approach that aims at continuous improvements and achieving a high degree of flexibility, adaptability and initiative.

    This paper describes the evolution of the shop-floor operator, according to the research literature and interviews with manufacturing managers and human resources specialists. In addition, the paper presents the response of future Swedish shop-floor operators, today’s high-school students, to a description of their possible future work as shop-floor operators. The Swedish manufacturing industry competes, to a large extent, on and responds to the international market. The findings made in this paper are thus also interesting for other industries and countries acting on the international market.

  • 5.
    Jin, G. Q.
    et al.
    Coventry Univ, Fac Engn & Comp, Coventry, W Midlands, England.
    Li, W. D.
    Coventry Univ, Fac Engn & Comp, Coventry, W Midlands, England.
    Tsai, C. F.
    Aletheia Univ, Dept Ind Management & Enterprise Informat, New Taipei City, Taiwan .
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    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. Crown Copyright (C) 2011 Published by Elsevier Ltd on behalf of The Society of Manufacturing Engineers. All rights reserved.

  • 6.
    Pehrsson, Leif
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Bernedixen, Jacob
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Automatic identification of constraints and improvement actions in production systems using multi-objective optimization and post-optimality analysis2016In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 39, p. 24-37Article in journal (Refereed)
    Abstract [en]

    Manufacturing companies are operating in a severely competitive global market, which renders an urgent need for them to explore new methods to enhance the performance of their production systems in order to retain their competitiveness. Regarding the performance of a production system, it is not sufficient simply to detect which operations to improve, but it is imperative to pinpoint the right actions in the right order to avoid sub-optimizations and wastes in time and expense. Therefore, a more accurate and efficient method for supporting system improvement decisions is greatly needed in manufacturing systems management. Based on research in combining simulation-based multi-objective optimization and post-optimality analysis methods for production systems design and analysis, a novel method for the automatic identification of bottlenecks and improvement actions, so-called Simulation-based Constraint Identification (SCI), is proposed in this paper. The essence of the SCI method is the application of simulation-based multi-objective optimization with the conflicting objectives to maximize the throughput and minimize the number of required improvement actions simultaneously. By using post-optimality analysis to process the generated optimization dataset, the exact improvement actions needed to attain a certain level of performance of the production line are automatically put into a rank order. In other words, when compared to other existing approaches in bottleneck detection, the key novelty of combining multi-objective optimization and post-optimality analysis is to make SCI capable of accurately identifying a rank order for the required levels of improvement for a large number of system parameters which impede the performance of the entire system, in a single optimization run. At the same time, since SCI is basically built a top a simulation-based optimization approach, it is capable of handling large-scale, real-world system models with complicated process characteristics. Apart from introducing such a method, this paper provides some detailed validation results from applying SCI both in hypothetical examples that can easily be replicated as well as a complex, real-world industrial improvement project. The promising results compared to other existing bottleneck detection methods have demonstrated that SCI can provide valuable higher-level information to support confident decision-making in production systems improvement.

  • 7.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, Royal Institute of Technology, 100 44 Stockholm, 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.

  • 8.
    Wang, Lihui
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Adamson, Göran
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Holm, Magnus
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Moore, Philip
    Mechatronics Research Centre, De Montfort University, Leicester LE1 9BH, United Kingdom.
    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, review/survey (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.

  • 9.
    Wang, Lihui
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Feng, Hsi-Yung
    The University of British Columbia.
    Adaptive Manufacturing2011In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 30, no 3, p. 117-117Article in journal (Refereed)
  • 10.
    Zhang, Dan
    et al.
    University of Ontario Institute of Technology.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Gao, Zhen
    University of Ontario Institute of Technology.
    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.

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