his.sePublications
Change search
Refine search result
1 - 26 of 26
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Adamson, Göran
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. De Montfort University, UK.
    A Novel Method for Adaptive Control of Manufacturing Equipment in Cloud Environments2018Doctoral thesis, monograph (Other academic)
    Abstract [en]

    The ability to adaptively control manufacturing equipment, both in local and distributed environments, is becoming increasingly more important for many manufacturing companies.

    One important reason for this is that manufacturing companies are facing increasing levels of changes, variations and uncertainty, caused by both internal and external factors, which can negatively impact their performance. Frequently changing consumer requirements and market demands usually lead to variations in manufacturing quantities, product design and shorter product life-cycles. Variations in manufacturing capability and functionality, such as equipment breakdowns, missing/worn/broken tools and delays, also contribute to a high level of uncertainty. The result is unpredictable manufacturing system performance, with an increased number of unforeseen events occurring in these systems. Events which are difficult for traditional planning and control systems to satisfactorily manage.

    For manufacturing scenarios such as these, the use of real-time manufacturing information and intelligence is necessary to enable manufacturing activities to be performed according to actual manufacturing conditions and requirements, and not according to a pre-determined process plan. Therefore, there is a need for an event-driven control approach to facilitate adaptive decision-making and dynamic control capabilities.

    Another reason driving the move for adaptive control of manufacturing equipment is the trend of increasing globalization, which forces manufacturing industry to focus on more cost-effective manufacturing systems and collaboration within global supply chains and manufacturing networks. Cloud Manufacturing is evolving as a new manufacturing paradigm to match this trend, enabling the mutually advantageous sharing of resources, knowledge and information between distributed companies and manufacturing units. One of the crucial objectives for Cloud Manufacturing is the coordinated planning, control and execution of discrete manufacturing operations in collaborative and networked environments. Therefore, there is also a need that such an event-driven control approach supports the control of distributed manufacturing equipment.

    The aim of this research study is to define and verify a novel and comprehensive method for adaptive control of manufacturing equipment in cloud environments.

    The presented research follows the Design Science Research methodology. From a review of research literature, problems regarding adaptive manufacturing equipment control have been identified. A control approach, building on a structure of event-driven Manufacturing Feature Function Blocks, supported by an Information Framework, has been formulated. The Function Block structure is constructed to generate real-time control instructions, triggered by events from the manufacturing environment. The Information Framework uses the concept of Ontologies and The Semantic Web to enable description and matching of manufacturing resource capabilities and manufacturing task requests in distributed environments, e.g. within Cloud Manufacturing. The suggested control approach has been designed and instantiated, implemented as prototype systems for both local and distributed manufacturing scenarios, in both real and virtual applications. In these systems, event-driven Assembly Feature Function Blocks for adaptive control of robotic assembly tasks have been used to demonstrate the applicability of the control approach. The utility and performance of these prototype systems have been tested, verified and evaluated for different assembly scenarios.

    The proposed control approach has many promising characteristics for use within both local and distributed environments, such as cloud environments. The biggest advantage compared to traditional control is that the required control is created at run-time according to actual manufacturing conditions.

    The biggest obstacle for being applicable to its full extent is manufacturing equipment controlled by proprietary control systems, with native control languages. To take the full advantage of the IEC Function Block control approach, controllers which can interface, interpret and execute these Function Blocks directly, are necessary.

  • 2.
    Adamson, Göran
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Holm, Magnus
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Moore, Philip
    Academy of Innovation & Research, Falmouth University, Cornwall, United kingdom.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH Royal Institute of Technology, Stockholm, Sweden.
    A Cloud Service Control Approach for Distributed and Adaptive Equipment Control in Cloud Environments2016In: Research and Innovation in Manufacturing: Key Enabling Technologies for the Factories of the Future - Proceedings of the 48th CIRP Conference on Manufacturing Systems / [ed] Roberto Teti, Elsevier, 2016, Vol. 41, p. 644-649Conference paper (Refereed)
    Abstract [en]

    A developing trend within the manufacturing shop-floor domain is the move of manufacturing activities into cloud environments, as scalable, on-demand and pay-per-usage cloud services. This will radically change traditional manufacturing, as borderless, distributed and collaborative manufacturing missions between volatile, best suited groups of partners will impose a multitude of advantages. The evolving Cloud Manufacturing (CM) paradigm will enable this new manufacturing concept, and on-going research has described many of its anticipated core virtues and enabling technologies. However, a major key enabling technology within CM which has not yet been fully addressed is the dynamic and distributed planning, control and execution of scattered and cooperating shop-floor equipment, completing joint manufacturing tasks.

    In this paper, the technological perspective for a cloud service-based control approach is described, and how it could be implemented. Existing manufacturing resources, such as soft, hard and capability resources, can be packaged as cloud services, and combined to create different levels of equipment or manufacturing control, ranging from low-level control of single machines or devices (e.g. Robot Control-as-a-Service), up to the execution of high level multi-process manufacturing tasks (e.g. Manufacturing-as-a-Service). A multi-layer control approach, featuring adaptive decision-making for both global and local environmental conditions, is proposed. This is realized through the use of a network of intelligent and distributable decision modules such as event-driven Function Blocks, enabling run-time manufacturing activities to be performed according to actual manufacturing conditions. The control system’s integration to the CM cloud service management functionality is also described.

  • 3.
    Adamson, Göran
    et al.
    University of Skövde, School of Technology and Society.
    Holm, Magnus
    University of Skövde, School of Technology and Society.
    Wang, Lihui
    University of Skövde, School of Technology and Society.
    Event-Driven Adaptability using IEC 61499 in Manufacturing Systems2012In: Proceedings of The 5th International Swedish Production Symposium, SPS12 / [ed] Mats Björkman, Linköping: The Swedish Production Academy , 2012, p. 453-460Conference paper (Refereed)
    Abstract [en]

    Different kinds of uncertainty, such as variations in manufacturing capability and functionality, as well as changes in demand, make up a dynamically changing environment for many manufacturing systems of today. The ability to adapt to these unforeseen changes, through dynamic decision-making as well as dynamic control capabilities based on the use of real-time manufacturing information and intelligence, is vital to be able to perform at a competitive level while reducing unscheduled downtime. The event-driven Function Block (FB) model of the IEC 61499 standard, as opposed to the time-triggered and data-driven concept of IEC 61331, supports this approach, making it possible to handle, in a responsive and adaptive way, different kinds of uncertainty. Our objective 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 FBs. The implementation and testing of FB-based control for manufacturing equipment has been successfully realized in prototype systems, with control of both CNC machining and robotic assembly operations. The approach of using IEC 61499 FBs for adaptive control in other applications is also investigated, as an adaptive decision support system for operators at manufacturing facilities is under development. We strongly believe that IEC 61499 will play a major role in the shift to adaptive manufacturing systems.

  • 4.
    Adamson, Göran
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Holm, Magnus
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Moore, Philip
    University College Falmouth, United Kingdom.
    Adaptive Assembly Feature Based Function Block Control of Robotic Assembly Operations2012In: The 13th Mechatronics Forum International Conference Proceedings: Vol. 1/3 / [ed] Rudolf Scheidl & Bernhard Jakoby, Linz: TRAUNER Verlag, 2012, p. 8-13Conference paper (Refereed)
    Abstract [en]

    Many manufacturing systems are exposed to a variety of unforeseen changes, negatively restricting their performances. External variations depending on market demand (e.g. changes in design, quantity and product mix) and internal variations in production capability and flexibility (e.g. equipment breakdowns, missing/worn/broken tools, delays and express orders) all contribute to an environment of uncertainty. In these dynamically changing environments, adaptability is a key feature for manufacturing systems to be able to perform at a maximum level, while keeping unscheduled downtime to a minimum. Targeting manufacturing equipment adaptability, this paper reports an assembly feature (AF) based approach for robotic assembly, using IEC 61499 compliant Function Blocks (FBs). Through the use of a network of event-driven FBs, an adaptive controller system for an industrial gantry robot’s assembly operations has been designed, implemented and tested. Basic assembly operations have been mapped as AFs into Assembly Feature Function Blocks (AF-FBs). Through their combination in FB networks, they can be aggregated to perform higher level assembly tasks. The AF-FBs dynamic execution and behavior can be adaptively controlled through embedded eventdriven algorithms, enabling the ability of adaptive decisions to handle unforeseen changes in the runtime environment.

  • 5.
    Adamson, Göran
    et al.
    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. Department of Production Engineering Royal Institute of Technology 100 44 Stockholm, Sweden.
    Holm, Magnus
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    The state of the art of cloud manufacturing and future trends2013In: Proceedings of the ASME 2013 International Manufacturing Science and Engineering Conference MSEC2013 June 10-14, 2013, Madison, Wisconsin, USA, ASME - The American Society of Mechanical Engineers , 2013, Vol. 2, p. Article number MSEC2013-1123-Conference paper (Refereed)
    Abstract [en]

    Cloud manufacturing has emerged as a new manufacturing paradigm, which combines technologies (such as Internet of Things, Cloud computing, semantic Web, virtualisation and service-oriented technologies) with advanced manufacturing models, information and communication technologies. It aims to be networked, intelligent, service-oriented, knowledge-based and energy efficient, and promises a variety of benefits and advantages by providing fast, reliable and secure on-demand services for users. It is envisioned that companies in all sectors of manufacturing will be able to package their resources and know-hows in the Cloud, making them conveniently available for others through pay-as-you-go, which is also timely and economically attractive. Resources, e.g. manufacturing software tools, applications, knowledge and fabrication capabilities, will then be made accessible to presumptive consumers on a worldwide basis. After surveying a vast array of available publications, this paper presents an up-to-date literature review together with future trends and research directions in Cloud manufacturing.

  • 6.
    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. Royal Institute of Technology, Stockholm, Sweden.
    Holm, Magnus
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Moore, Philip
    Falmouth University, Cornwall, UK.
    Adaptive Robot Control as a Service in Cloud Manufacturing2015In: ASME 2015 International Manufacturing Science and Engineering Conference: Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing, ASME Press, 2015, Vol. 2, p. Paper No. MSEC2015-9479-Conference paper (Refereed)
    Abstract [en]

    The interest for implementing the concept of Manufacturing-as-a-Service is increasing as concepts for letting the manufacturing shop-floor domain take advantage of the cloud appears. Combining technologies such as Internet of Things, Cloud Computing, Semantic Web, virtualisation and service-oriented technologies with advanced manufacturing models, information and communication technologies, Cloud Manufacturing (CM) is emerging as a new manufacturing paradigm. The ideas of on-demand, scalable and pay-for-usage resource-sharing in this concept will move manufacturing towards distributed and collaborative missions in volatile partnerships. This will require a control approach for distributed planning and execution of cooperating manufacturing activities. Without control based on both global and local environmental conditions, the advantages of CM will not be fulfilled.

    By utilising smart and distributable decision modules such as event-driven FBs, run-time manufacturing operations in a distributed environment may be adjusted to prevailing manufacturing conditions. Packaged in a cloud service for manufacturing equipment control, it will satisfy the control needs in CM. By combining different resource types, such as hard, soft and capability resources, the cloud service Robot Control-as-a-Service can be realised.

    This paper describes the functional perspective and enabling technologies for a control approach for robotic assembly tasks in CM, and describes a scenario for its implementation.

  • 7.
    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. Kungliga Tekniska Högskolan, Stockholm (KTH).
    Holm, Magnus
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Moore, Philip
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Academy for Innovation & Research, Falmouth University, UK.
    Adaptive Robotic Control in Cloud Environments2014In: Proceedings of the 24th International Conference on Flexible Automation and Intelligent Manufacturing / [ed] F. Frank Chen, The University of Texas at San Antonio, U.S.A., Lancaster, Pennsylvania, USA: DEStech Publications, Inc , 2014, p. 37-44Conference paper (Refereed)
    Abstract [en]

    The increasing globalization is a trend which forces manufacturing industry of today to focus on more cost-effective manufacturing systems and collaboration within global supply chains and manufacturing networks. Cloud Manufacturing (CM) is evolving as a new manufacturing paradigm to match this trend, enabling the mutually advantageous sharing of resources, knowledge and information between distributed companies and manufacturing units. Providing a framework for collaboration within complex and critical tasks, such as manufacturing and design, it increases the companies’ ability to successfully compete on a global marketplace. One of the major, crucial objectives for CM is the coordinated planning, control and execution of discrete manufacturing operations in a collaborative and networked environment. This paper describes the overall concept of adaptive Function Block control of manufacturing equipment in Cloud environments, with the specific focus on robotic assembly operations, and presents Cloud Robotics as “Robot Control-as-a-Service” within CM.

  • 8.
    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, KTH Royal Institute of Technology, Stockholm, Sweden.
    Holm, Magnus
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Moore, Philip
    Academy for Innovation & Research, Falmouth University, Cornwall, United Kingdom.
    Cloud Manufacturing: A Critical Review of Recent Development and Future Trends2017In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 30, no 4-5, p. 347-380Article in journal (Refereed)
    Abstract [en]

    There is an on-going paradigm shift in manufacturing, in which modern manufacturing industry is changing towards global manufacturing networks and supply chains. This will lead to the flexible usage of different globally distributed, scalable and sustainable, service-oriented manufacturing systems and resources. Combining recently emerged technologies, such as Internet of Things, Cloud Computing, Semantic Web, service-oriented technologies, virtualisation and advanced high-performance computing technologies, with advanced manufacturing models and information technologies, Cloud Manufacturing is a new manufacturing paradigm built on resource sharing, supporting and driving this change.

    It is envisioned that companies in all sectors of manufacturing will be able to package their resources and know-hows in the Cloud, making them conveniently available for others through pay-as-you-go, which is also timely and economically attractive. Resources, e.g. manufacturing software tools, applications, knowledge and fabrication capabilities and equipment, will then be made accessible to presumptive consumers on a worldwide basis.

    Cloud Manufacturing has been in focus for a great deal of research interest and suggested applications during recent years, by both industrial and academic communities. After surveying a vast array of available publications, this paper presents an up-to-date literature review together with identified outstanding research issues, and future trends and directions within Cloud Manufacturing.

  • 9.
    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. Royal Institute of Technology, Stockholm, Sweden.
    Holm, Magnus
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Moore, Philip
    Falmouth University, Cornwall, United Kingdom.
    Feature-Based Adaptive Manufacturing Equipment Control for Cloud Environments2016In: Proceedings of the ASME 11th International Manufacturing Science and Engineering Conference, 2016, vol 2, American Society of Mechanical Engineers (ASME) , 2016, article id UNSP V002T04A019Conference paper (Refereed)
    Abstract [en]

    The ideas of on-demand, scalable and pay-for-usage resource-sharing in Cloud Manufacturing are steadily attracting more interest. For implementing the concept of Manufacturing as-a-Service in a cloud environment, description models and implementation language for resources and their capabilities are required. A standardized approach for systemived virtualization, servisilisation, retrieval, selection and composition into higher levels of functionality is necessary. For the collaborative sharing and use of networked manufacturing resources there is also a need for a control approach for distributed manufacturing equipment. In this paper, the technological perspective for an adaptive cloud service-based control approach is described, and a supporting information model for its implementation. The control is realized through the use of a network of intelligent and distributable Function Block decision modules, enabling run-time manufacturing activities to be performed according to actual manufacturing conditions. The control system's integration to the cloud service management functionality is described, as well as a feature-level capability model and the use of ontologies and the Semantic Web.

  • 10.
    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. Kungliga Tekniska Högskolan, Stockholm (KTH).
    Holm, Magnus
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Moore, Philip
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Academy for Innovation & Research, Falmouth University, UK.
    Function Block Approach for Adaptive Robotic Control in Virtual and Real Environments2014In: Proceedings of the 14th Mechatronics Forum International Conference / [ed] Leo J. De Vin and Jorge Solis, Karlstad: Karlstads universitet, 2014, p. 473-479Conference paper (Refereed)
    Abstract [en]

    Many manufacturing companies are facing an increasing amount of changes and uncertainty, caused by both internal and external factors. Frequently changing customer and market demands lead to variations in manufacturing quantities, product design and shorter product life-cycles, and variations in manufacturing capability and functionality contribute to a high level of uncertainty. The result is unpredictable manufacturing system performance, with an increased number of unforeseen events occurring in these systems. Such events are difficult for traditional planning and control systems to satisfactorily manage. For scenarios like these, with a dynamically changing manufacturing environment, adaptive decision making is crucial for successfully performing manufacturing operations. Relying on real-time information of manufacturing processes and operations, and their enabling resources, adaptive decision making can be realized with a control approach combining IEC 61499 event-driven Function Blocks (FBs) with manufacturing features. These FBs are small decision-making modules with embedded algorithms designed to generate the desired equipment control code. When dynamically triggered by event inputs, parameter values in their data inputs are forwarded to the appropriate algorithms, which generate new events and data output as control instructions. The data inputs also include monitored real-time information which allows the dynamic creation of equipment control code adapted to the actual run-time conditions on the shop-floor. Manufacturing features build on the concept that a manufacturing task can be broken down into a sequence of minor basic operations, in this research assembly features (AFs). These features define atomic assembly operations, and by combining and implementing these in the event-driven FB embedded algorithms, automatic code generation is possible. A test case with a virtual robot assembly cell is presented, demonstrating the functionality of the proposed control approach.

  • 11.
    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.

  • 12.
    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 Function Block Control Framework for Manufacturing Equipment in Cloud Environments2018In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 57, no 12, p. 3954-3974Article in journal (Refereed)
    Abstract [en]

    The ability to adaptively control manufacturing equipment in cloud environments is becoming increasingly more important. Industry 4.0, supported by Cyber Physical Systems and the concept of on-demand, scalable and pay-for-usage resource-sharing in cloud environments offers many promises regarding effective and flexible manufacturing. For implementing the concept of manufacturing services in a cloud environment, a cloud control approach for the sharing and control of networked manufacturing resources is required. This paper presents a cloud service-based control approach which has a product perspective and builds on the combination of event-driven IEC 61499 Function Blocks and product manufacturing features. Distributed control is realised through the use of a networked control structure of such Function Blocks as decision modules, enabling an adaptive run-time behaviour. The control approach has been developed and implemented as prototype systems for both local and distributed manufacturing scenarios, in both real and virtual applications. An application scenario is presented to demonstrate the applicability of the control approach. In this scenario, Assembly Feature-Function Blocks for adaptive control of robotic assembly tasks have been used.

  • 13.
    Givehchi, Mohammad
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Holm, Magnus
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Adamson, Göran
    University of Skövde, School of Engineering Science.
    Wang, Lihui
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Web-based Real-time Monitoring and Control of a Robot2011In: / [ed] Jan-Eric Ståhl, 2011Conference paper (Refereed)
    Abstract [en]

    In order to enhance production in today’s uncertain manufacturing environments, real-time monitoring and dynamic control capabilities that are responsive and adaptive to rapid changes of production capability and functionality are vital. Targeting the dynamic issue, this paper presents a virtual production aid, a Wise-ShopFloor(Web-based integrated sensor-driven e-ShopFloor) prototype that can integrate Web-based sensor-driven virtual models with a real shop floor.

    The Wise-ShopFloor utilizes Java technologies (e.g., Java 3D and Java Servlet) for system implementation which allows the users to monitor and control distant shop floor operations based on runtime information from the shop floor. Particularly, remote monitoring and control of an industrial robot is chosen as a case study to demonstrate the approach towards web-based adaptive manufacturing. It is envisioned that this approach not only can bridge the gap between virtual and real manufacturing but also can largely enhance manufacturing performance and profitability via remote instant assistance

  • 14.
    Holm, Magnus
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Adamson, Göran
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Moore, Philip
    Academy of Innovation & Research, Falmouth University, Cornwall, United kingdom.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH Royal Institute of Technology, Stockholm, Sweden.
    Why I want to be a future Swedish shop-floor operator2016In: Research and Innovation in Manufacturing: Key Enabling Technologies for the Factories of the Future - Proceedings of the 48th CIRP Conference on Manufacturing Systems / [ed] Roberto Teti, Elsevier, 2016, Vol. 41, p. 1101-1106Conference paper (Refereed)
    Abstract [en]

    When looking in rear view mirrors the Swedish as well as the international production industries can overview several years of progress covering all aspects of production. Production methodologies and machines etc. have changed and evolved, and so has the environment of the shop-floor operator. The demands on the shop-floor operators have grown from simple monotonic tasks with low complexity to pro-active team work requiring flexibility, continuous improvements and a holistic approach. With a base in a study where production and HR-managers at six Swedish manufacturing industries have been interviewed this paper identifies the role of today’s and the future Swedish shop-floor operator. The response to the described role of the future operator is compiled from the ones who will become the future Swedish shop-floor operators – today’s teenagers attending technical high-school. Their views of the environment of the future shop-floor operator are described by accuracy, development, a good working environment and team work. The paper also reveals what the offer should include to make these teenagers say: I want to be a future Swedish shop-floor operator.

  • 15.
    Holm, Magnus
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Adamson, Göran
    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. Royal Institute of Technology, Stockholm, Sweden.
    Enhancing Adaptive Production Using IEC 61499 Event-Driven Function Blocks2013In: Proceedings of the North American Manufacturing Research Institution of SME, vol. 41, 10-14 June, University of Wisconsin-Madison, Society of Manufacturing Engineers, North American Manufacturing Research Institute of SME , 2013, p. 420-429Conference paper (Refereed)
    Abstract [en]

    Reduction of production costs and the ability to continuously improve is a must for every manufacturer. High availability in a dynamic and complex production environment demands adaptability to recurring changes. Each device within the production systems holds more and more intelligence and computing power which supports an approach implementing the standard of IEC 61499 to enhance adaptive production by enabling a distributed automation system with improved productivity. Research approaching IEC 61499 is investigated and reported in this paper, covering both control of manufacturing equipment and adaptive process planning. The objective is to develop methodologies for process planning as well as machine control and monitoring for machining and assembly operations in a dynamic, adaptive and distributed environment using event-driven function blocks.

  • 16.
    Holm, Magnus
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Adamson, Göran
    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.
    IEC 61499 - Enabling Control of Distributed Systems Beyond IEC 61131-32012In: Proceedings of the SPS12 conference 2012, Swedish Production Academy , 2012, p. 37-44Conference paper (Refereed)
  • 17.
    Holm, Magnus
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Adamson, Göran
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Moore, Philip
    University College Falmouth, United Kingdom.
    An IEC 61499 Function Block based Approach for CNC Machining Operations2012In: The 13th Mechatronics Forum International Conference Proceedings: Vol. 1/3 / [ed] Rudolf Scheidl & Bernhard Jakoby, Linz: TRAUNER Verlag, 2012, p. 115-121Conference paper (Refereed)
    Abstract [en]

    In order to create an adaptive and interoperable CNC control system to explore the full functionality of CNC machine tools and to surpass the shortcomings and restrictions of the current CNC control standard using G-codes, an IEC 61499 function block based control system model has been developed. Basic machining operations are identified and classified as machining features, which are wrapped into Machining Feature Function Blocks (MF-FBs) with algorithms. For the machining of a part, the required MF-FBs are selected and combined into a Composite Function Block, comprising the correct control instructions for machining the part.

    The event-driven nature of these function blocks enables the run-time selection of appropriate algorithms and control of their correct behavior and dynamic execution, supporting the system’s ability to act in response to actual conditions and manufacturing requirements. Being truly adaptive makes it possible that different available machine tools be selected to machine a part with the appropriate control code generated at runtime. This eliminates the tedious CNC programming effort, and therefore no predefined, machine-specific control code has to be generated in advance. The use of generic function blocks for encapsulation of machining know-how in algorithms makes machines and CNC systems independent and therefore portable, reusable and interoperable.

  • 18.
    Holm, Magnus
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Adamson, Göran
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Moore, Philip
    Academy of Innovation & Research, Falmouth University, United Kingdom.
    Framework for an adaptive decision support system for industrial shop-floor operators2014Conference paper (Refereed)
    Abstract [en]

    Today’s shop-floor operators’ working tasks often stretches over a broad spectra of jobs; from ordinary production assignments to handling errors and performing maintenance. Demands for enhanced skills and knowledge are constantly raised to limit the consequences of tool breakage, machine down time and other stochastic events negatively affecting the production.

    To be able to meet these increasing demands a framework for a distributed and adaptive decision support system is proposed. It will help the shop-floor operator to distinguish between decision options and minimize time to consider appropriate actions to maximize productivity both during normal production and when facing unexpected or unscheduled events.

    In what ways is it possible to support operators in making decisions for optimal productivity?” was the opening question from the industry partner when beginning this research. Targeting this question a novel framework for an adaptive decision support system (DSS) enabled by event-driven function blocks, based on decision logics is proposed. Its ability to adapt to the actual conditions on the shop-floor is compared to a currently used voice message system in a test case.

  • 19.
    Holm, Magnus
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Adamson, Göran
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Moore, Philip
    Academy of Innovation & Research, Falmouth University, United Kingdom.
    The Future Swedish Shop-Floor Operator – Interviews with Production Managers2014In: Proceedings of the sixth Swedish Production Symposium, 2014, 2014Conference paper (Refereed)
    Abstract [en]

    This paper is based on a study in which production and HR managers at six Swedish manufacturing industries have been interviewed about the role of the shop-floor operator, taking off in today’s situation in trying to identify the future one. As well as the production methods and the machines etc. in the production system continuously evolve, so does the environment of the shop-floor operator. Increasing complexity in the production systems raises demands on the operators’ ability to handle ICT-tools to gain decision support and knowledge needed in the future shop-floor environment. 

  • 20.
    Holm, Magnus
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Cordero Garcia, Aimar
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Adamson, Göran
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Adaptive decision support for shop-floor operators in automotive industry2014In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 17, p. 440-445Article in journal (Refereed)
    Abstract [en]

    Today's operators on factory shop-floors are often not stationed, dealing with a single or few tasks but have increasing responsibilities demanding enhanced skills and knowledge in a production environment where any disturbance must be settled with adequate actions without delay to keep optimum output. To be able to respond to these demands, the operators need dynamic, distributed and adaptive decision support in real-Time, helping them to distinguish decision options and maximizing productivity despite incoming stochastic events. The minimum of time and option for operators to consider appropriate action both during normal production and when facing unexpected or unscheduled events point out the need of adaptive decision support for operators. When initiating this research project the question from the industry partner was the following: In what ways is it possible to support operators in making decisions for optimal productivity? By targeting this problem this paper introduces a novel framework for an adaptive decision-support system enabled by event-driven function blocks and based on decision logics. The proposed decision support systems' ability to adapt to the actual conditions on the shop-floor is validated through a case study, and its capability is compared to the voice message system installed on-site.

  • 21.
    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.

  • 22.
    Wang, Lihui
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Adamson, Göran
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Holm, Magnus
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Moore, Philip
    De Montfort University.
    Current Status of Function Blocks for Process Planning and Execution Control of Manufacturing Equipment2011In: Proceedings of the 21st International Conference on Flexible Automation and Intelligent Manufacturing, Feng Chia University , 2011, p. 963-973Conference paper (Refereed)
    Abstract [en]

    Manufacturing in a job-shop environment is often characterized by a large variety of products in small batch size, requiring real-time monitoring for dynamic distributed decision making, as well as dynamic control capabilities that are able to handle, in a responsive and adaptive 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 a 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.

  • 23.
    Wang, Lihui
    et al.
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Givehchi, Mohammad
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Adamson, Göran
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    Holm, Magnus
    University of Skövde, School of Technology and Society. University of Skövde, The Virtual Systems Research Centre.
    A sensor-driven 3D model-based approach to remote real-time monitoring2011In: CIRP annals, ISSN 0007-8506, E-ISSN 1726-0604, Vol. 60, no 1, p. 493-496Article in journal (Refereed)
    Abstract [en]

    This paper presents an integrated approach for remote real-time monitoring of manufacturing operations. It is enabled by using virtual 3D models driven by real sensor data. The objectives of this research are twofold: (1) to significantly reduce network traffic for real-time monitoring over the Internet: and (2) to increase the controllability of manufacturing systems from anywhere in a decentralised environment. Particularly, this paper covers the principle of the approach, system architecture, prototype implementation, and a case study of remote control of a robotic assembly cell. Compared with camera-based monitoring systems, our approach only consumes less than 1% of its network bandwidth, feasible and practical as a web-based portable solution. (C) 2011 CIRP.

  • 24.
    Wang, Lihui
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Givehchi, Mohammad
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Schmidt, Bernard
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Adamson, Göran
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Robotic Assembly Planning and Control with Enhanced Adaptability2012In: 45th CIRP Conference on Manufacturing Systems 2012 / [ed] G. Chryssolouris, D. Mourtzis, Elsevier, 2012, Vol. 3, p. 173-178Conference paper (Refereed)
    Abstract [en]

    The dynamic market today requires manufacturing companies to possess high degree of adaptability and flexibility in order to deal with shop-floor uncertainties. Such uncertainties as missing tools, part shortage, job delay, rush-order and unavailability of resources, etc. happen more often in assembly operations. Targeting this problem, this research proposes a function block enabled approach to achieving adaptability and flexibility in assembly planning and control. In particular, this paper presents our latest development using a robotic mini assembly cell for testing and validation of a function block enabled system capable of assembly and robot trajectory planning and control. It is expected that a better adaptability can be achieved by this approach.

  • 25.
    Wang, Lihui
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Holm, Magnus
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Adamson, Göran
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Embedding a process plan in function blocks for adaptive machining2010In: CIRP annals, ISSN 0007-8506, E-ISSN 1726-0604, Vol. 59, no 1, p. 433-436Article in journal (Refereed)
    Abstract [en]

    This paper presents a function block enabled approach towards adaptive process planning and machining. A two-layer structure of supervisory planning and operation planning is proposed to separate generic data from machine-specific ones. The supervisory planning is only performed once, in advance, at the shop level to generate machine-neutral process plans, whereas the operation planning is carried out at runtime at the machine level to determine machine-specific operations. Such adaptive decision making is facilitated by event-driven algorithms embedded in the function blocks. It is expected that the new approach can greatly enhance the dynamism of fluctuating job-shop machining operations.

  • 26.
    Wang, Lihui
    et al.
    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.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Givehchi, Mohammad
    Department of Production Engineering, Royal Institute of Technology, Stockholm, Sweden.
    Adamson, Göran
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Robotic Assembly Planning and Control with Enhanced Adaptability through Function Blocks2015In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 77, no 1-4, p. 705-715Article in journal (Refereed)
    Abstract [en]

    Manufacturing companies today need to maintain a high level of flexibility and adaptability to deal with uncertainties on dynamic shop floors, including e.g. cutting tool shortage, part supply interruption, urgent job insertion or delay, and machine unavailability. Such uncertainties are characteristic in component assembly operations. Addressing the problem, we propose a new method using function blocks to achieve much improved adaptability in assembly planning and robot control. In this paper, we propose to use event-driven function blocks for robotic assembly, aiming to plan trajectory and execute assembly tasks in real-time. It is envisioned that this approach will achieve better adaptability if applied to real-world applications.

1 - 26 of 26
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf