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  • 1.
    Adamson, Göran
    et al.
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Wang, Lihui
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. Department of Production Engineering, Royal Institute of Technology, Stockholm, Sweden.
    Moore, Philip
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Feature-based control and information framework for adaptive and distributed manufacturing in cyber physical systems2017Inngår i: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 43, s. 305-315Artikkel i tidsskrift (Fagfellevurdert)
    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, IN, USA.
    Wang, Lihui
    Högskolan i Skövde, Institutionen för teknik och samhälle. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Optimization of machining processes from the perspective of energy consumption: A case study2012Inngår i: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 31, nr 4, s. 420-428Artikkel i tidsskrift (Fagfellevurdert)
    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.
    Högskolan i Skövde, Forskningscentrum för Virtuella system. Högskolan i Skövde, Institutionen för teknik och samhälle.
    Ng, Amos
    Högskolan i Skövde, Institutionen för teknik och samhälle. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Wang, Lihui
    Högskolan i Skövde, Institutionen för teknik och samhälle. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Evolutionary optimization of robotic assembly operation sequencing with collision-free paths2011Inngår i: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 30, nr 4, s. 196-203Artikkel i tidsskrift (Fagfellevurdert)
    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
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    The future shop-floor operators, demands, requirements and interpretations2018Inngår i: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 47, s. 35-42Artikkel i tidsskrift (Fagfellevurdert)
    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.

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  • 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
    Högskolan i Skövde, Institutionen för teknik och samhälle. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Adaptive tool-path generation of rapid prototyping for complex product models2011Inngår i: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 30, nr 3, s. 154-164Artikkel i tidsskrift (Fagfellevurdert)
    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.
    Kumbhar, Mahesh
    et al.
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningsmiljön Virtuell produkt- och produktionsutveckling.
    Ng, Amos H. C.
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningsmiljön Virtuell produkt- och produktionsutveckling. Division of Industrial Engineering and Management, Department of Civil and Industrial Engineering, Uppsala University, Sweden.
    Bandaru, Sunith
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningsmiljön Virtuell produkt- och produktionsutveckling.
    A digital twin based framework for detection, diagnosis, and improvement of throughput bottlenecks2023Inngår i: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 66, s. 92-106Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Digitalization through Industry 4.0 technologies is one of the essential steps for the complete collaboration, communication, and integration of heterogeneous resources in a manufacturing organization towards improving manufacturing performance. One of the ways is to measure the effective utilization of critical resources, also known as bottlenecks. Finding such critical resources in a manufacturing system has been a significant focus of manufacturing research for several decades. However, finding a bottleneck in a complex manufacturing system is difficult due to the interdependencies and interactions of many resources. In this work, a digital twin framework is developed to detect, diagnose, and improve bottleneck resources using utilization-based bottleneck analysis, process mining, and diagnostic analytics. Unlike existing bottleneck detection methods, this novel approach is capable of directly utilizing enterprise data from multiple levels, namely production planning, process execution, and asset monitoring, to generate event-log which can be fed into a digital twin. This enables not only the detection and diagnosis of bottleneck resources, but also validation of various what-if improvement scenarios. The digital twin itself is generated through process mining techniques, which can extract the main process map from a complex system. The results show that the utilization can detect both sole and shifting bottlenecks in a complex manufacturing system. Diagnosing and managing bottleneck resources through the proposed approach yielded a minimum throughput improvement of 10% in a real factory setting. The concept of a custom digital twin for a specific context and goal opens many new possibilities for studying the strong interaction of multi-source data and decision-making in a manufacturing system. This methodology also has the potential to be exploited for multi-objective optimization of bottleneck resources.

    Fulltekst (pdf)
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  • 7.
    Ma, Andrew
    et al.
    University of Bristol, United Kingdom.
    Frantzén, Marcus
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningsmiljön Virtuell produkt- och produktionsutveckling.
    Snider, Chris
    University of Bristol, United Kingdom.
    Nassehi, Aydin
    University of Bristol, United Kingdom.
    Anarchic manufacturing: Distributed control for product transition2020Inngår i: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 56, s. 1-10Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Manufacturers are poorly equipped to manage product transition scenarios, when moving from one product to another. Most tools consider a mature system, yet during transition and ramp up disturbances and inefficiency are common. The traditional methods, using centralised planning and control structures are too rigid and often resort to simple dispatch heuristics in this highly dynamic environment. Distributed systems have been proposed to leverage their self-organising and flexible traits to manage highly volatile and complex scenarios. Anarchic manufacturing, a free market based distributed planning and control system, delegates decision-making authority and autonomy to system elements at the lowest level; this system has previously been shown to manage job and flowshop style problems. The system has been adapted to use a dynamic batching mechanism, where jobs cooperate to benefit from economies of scale. The batch enables a direct economic viability assessment within the free market architecture, whether an individual machine should changeover production to another product type. This profitability assessment considers the overall system state and an agent's individual circumstance, which in turn reduces system myopia. Four experiments, including a real-world automotive case study, evaluate the anarchic manufacturing system against two centralised systems, using three different ramp-up curves. Although not always best performing against centralised systems, the anarchic manufacturing system is shown to manage transition scenarios effectively, displaying self-organising and flexible characteristics. The hierarchical system was shown to be impeded by its simplifying structure, as a result of structural rigidity. © 2020 The Society of Manufacturing Engineers

  • 8.
    Mahmoodi, Ehsan
    et al.
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningsmiljön Virtuell produkt- och produktionsutveckling.
    Fathi, Masood
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningsmiljön Virtuell produkt- och produktionsutveckling. Division of Industrial Engineering and Management, Uppsala University, Sweden.
    Tavana, Madjid
    Business Systems and Analytics Department, Distinguished Chair of Business Analytics, La Salle University, Philadelphia, USA ; Business Information Systems Department, Faculty of Business Administration and Economics, University of Paderborn, Germany.
    Ghobakhloo, Morteza
    Division of Industrial Engineering and Management, Uppsala University, Sweden.
    Ng, Amos H. C.
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningsmiljön Virtuell produkt- och produktionsutveckling. Division of Industrial Engineering and Management, Uppsala University, Sweden.
    Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing2024Inngår i: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 72, s. 287-307Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Data-driven simulation (DDS) is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of DDS for resource allocation (RA) in high-mix, low-volume smart manufacturing systems with mixed automation levels. A DDS-based decision support system (DDS-DSS) is developed by incorporating two RA strategies: simulation-based bottleneck analysis (SB-BA) and simulation-based multi-objective optimization (SB-MOO). To enhance the performance of SB-MOO, a unique meta-learning mechanism featuring memory, dynamic orthogonal array, and learning rate is integrated into the NSGA-II, resulting in a modified version of the NSGA-II with meta-learning (i.e., NSGA-II-ML). The proposed DSS also benefits from a post-optimality analysis that leverages a clustering algorithm to derive actionable insights. A real-life marine engine manufacturing application study is presented to demonstrate the applicability and exhibit efficacy of the proposed DSS and NSGA-II-ML. To this aim, NSGA-II-ML was tested against the original NSGA-II and differential evolution (DE) algorithm across a set of test problems. The results revealed that NSGA-II-ML surpassed the other two in terms of the number of non-dominated solutions and hypervolume, particularly in medium and large-sized problems. Furthermore, NSGA-II-ML achieved a 24% improvement in the best throughput found in the real case problem, outperforming SB-BA, NSGA-II, and DE. The post-optimality analysis led to the extraction of valuable knowledge about the key, influencing decision variables on the throughput.

    Fulltekst (pdf)
    fulltext
  • 9.
    Pehrsson, Leif
    et al.
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Ng, Amos H. C.
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Bernedixen, Jacob
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Automatic identification of constraints and improvement actions in production systems using multi-objective optimization and post-optimality analysis2016Inngår i: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 39, s. 24-37Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 10.
    Schmidt, Bernard
    et al.
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Wang, Lihui
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
    Depth camera based collision avoidance via active robot control2014Inngår i: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 33, nr 4, s. 711-718Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 11.
    Wang, Lihui
    et al.
    Högskolan i Skövde, Institutionen för teknik och samhälle. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Adamson, Göran
    Högskolan i Skövde, Institutionen för teknik och samhälle. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Holm, Magnus
    Högskolan i Skövde, Institutionen för teknik och samhälle. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    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 equipment2012Inngår i: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 31, nr 3, s. 269-279Artikkel, forskningsoversikt (Fagfellevurdert)
    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.

  • 12.
    Wang, Lihui
    et al.
    Högskolan i Skövde, Institutionen för teknik och samhälle. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Feng, Hsi-Yung
    The University of British Columbia.
    Adaptive Manufacturing2011Inngår i: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 30, nr 3, s. 117-117Artikkel i tidsskrift (Fagfellevurdert)
  • 13.
    Zhang, Dan
    et al.
    University of Ontario Institute of Technology.
    Wang, Lihui
    Högskolan i Skövde, Institutionen för teknik och samhälle. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Gao, Zhen
    University of Ontario Institute of Technology.
    An integrated approach for remote manipulation of a high-performance reconfigurable parallel kinematic machine2010Inngår i: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 29, nr 4, s. 164-172Artikkel i tidsskrift (Fagfellevurdert)
    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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