his.sePublications
Change search
Link to record
Permanent link

Direct link
BETA
Publications (10 of 26) Show all publications
Adamson, G. (2018). A Novel Method for Adaptive Control of Manufacturing Equipment in Cloud Environments. (Doctoral dissertation). United Kingdom: De Montfort University
Open this publication in new window or tab >>A Novel Method for Adaptive Control of Manufacturing Equipment in Cloud Environments
2018 (English)Doctoral 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.

Place, publisher, year, edition, pages
United Kingdom: De Montfort University, 2018. p. 232
Keywords
Function Block, Adaptive control, Robot-Control-as-a-Service, Cloud Manufacturing
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16523 (URN)
Opponent
Supervisors
Available from: 2019-01-18 Created: 2018-12-21 Last updated: 2019-01-18Bibliographically approved
Adamson, G., Wang, L. & Moore, P. (2018). Feature-based Function Block Control Framework for Manufacturing Equipment in Cloud Environments. International Journal of Production Research
Open this publication in new window or tab >>Feature-based Function Block Control Framework for Manufacturing Equipment in Cloud Environments
2018 (English)In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588XArticle in journal (Refereed) Epub ahead of print
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.

Place, publisher, year, edition, pages
Taylor & Francis, 2018
Keywords
manufacturing feature, function block, cloud, adaptive, control framework
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16525 (URN)10.1080/00207543.2018.1542178 (DOI)
Available from: 2018-12-21 Created: 2018-12-21 Last updated: 2019-02-11Bibliographically approved
Adamson, G., Wang, L., Holm, M. & Moore, P. (2017). Cloud Manufacturing: A Critical Review of Recent Development and Future Trends. International journal of computer integrated manufacturing (Print), 30(4-5), 347-380
Open this publication in new window or tab >>Cloud Manufacturing: A Critical Review of Recent Development and Future Trends
2017 (English)In: 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) Published
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.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2017
Keywords
Cloud Manufacturing, Resource Sharing, Service Orientation
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Technology; Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-11281 (URN)10.1080/0951192X.2015.1031704 (DOI)000396794500002 ()2-s2.0-84927720769 (Scopus ID)
Funder
Knowledge Foundation, 20130303
Available from: 2015-07-02 Created: 2015-07-02 Last updated: 2019-01-24Bibliographically approved
Adamson, G., Wang, L. & Moore, P. (2017). Feature-based control and information framework for adaptive and distributed manufacturing in cyber physical systems. Journal of manufacturing systems, 43, 305-315
Open this publication in new window or tab >>Feature-based control and information framework for adaptive and distributed manufacturing in cyber physical systems
2017 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 43, p. 305-315Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
Cyber physical system, Adaptive manufacturing, Feature-based control, Feature-level capability model
National Category
Other Mechanical Engineering
Research subject
Technology; Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-13262 (URN)10.1016/j.jmsy.2016.12.003 (DOI)000401390700010 ()2-s2.0-85009518088 (Scopus ID)
Funder
Knowledge Foundation, 20130303
Available from: 2016-12-27 Created: 2016-12-27 Last updated: 2019-01-24Bibliographically approved
Adamson, G., Holm, M., Moore, P. & Wang, L. (2016). A Cloud Service Control Approach for Distributed and Adaptive Equipment Control in Cloud Environments. In: Roberto Teti (Ed.), Research and Innovation in Manufacturing: Key Enabling Technologies for the Factories of the Future - Proceedings of the 48th CIRP Conference on Manufacturing Systems. Paper presented at CIRP CMS 2015, 48th CIRP Conference on Manufacturing Systems, Research and Innovation in Manufacturing: Key Enabling Technologies for the Factories of the Future, 24-26 June 2015, Ischia (Naples), Italy (pp. 644-649). Elsevier, 41
Open this publication in new window or tab >>A Cloud Service Control Approach for Distributed and Adaptive Equipment Control in Cloud Environments
2016 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Elsevier, 2016
Series
Procedia CIRP, ISSN 2212-8271 ; 41
Keywords
cloud manufacturing, cloud service, adaptive manufacturing, robot control
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-11372 (URN)10.1016/j.procir.2015.12.020 (DOI)000379247600110 ()2-s2.0-84968752806 (Scopus ID)
Conference
CIRP CMS 2015, 48th CIRP Conference on Manufacturing Systems, Research and Innovation in Manufacturing: Key Enabling Technologies for the Factories of the Future, 24-26 June 2015, Ischia (Naples), Italy
Funder
Knowledge Foundation
Available from: 2015-08-19 Created: 2015-08-19 Last updated: 2018-03-29Bibliographically approved
Adamson, G., Wang, L., Holm, M. & Moore, P. (2016). Feature-Based Adaptive Manufacturing Equipment Control for Cloud Environments. In: Proceedings of the ASME 11th International Manufacturing Science and Engineering Conference, 2016, vol 2: . Paper presented at ASME 2016 11th International Manufacturing Science and Engineering Conference (MSEC 2016), Blacksburg, USA, June 27–July 1, 2016. American Society of Mechanical Engineers (ASME), Article ID UNSP V002T04A019.
Open this publication in new window or tab >>Feature-Based Adaptive Manufacturing Equipment Control for Cloud Environments
2016 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
American Society of Mechanical Engineers (ASME), 2016
Keywords
Cloud manufacturing, adaptive robot control, ontology
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-13213 (URN)10.1115/MSEC2016-8771 (DOI)000388159400054 ()2-s2.0-84991619741 (Scopus ID)978-0-7918-4990-3 (ISBN)
Conference
ASME 2016 11th International Manufacturing Science and Engineering Conference (MSEC 2016), Blacksburg, USA, June 27–July 1, 2016
Available from: 2016-12-08 Created: 2016-12-08 Last updated: 2018-03-28Bibliographically approved
Holm, M., Adamson, G., Moore, P. & Wang, L. (2016). Why I want to be a future Swedish shop-floor operator. In: Roberto Teti (Ed.), Research and Innovation in Manufacturing: Key Enabling Technologies for the Factories of the Future - Proceedings of the 48th CIRP Conference on Manufacturing Systems. Paper presented at CIRP CMS 2015, 48th CIRP Conference on Manufacturing Systems, Research and Innovation in Manufacturing: Key Enabling Technologies for the Factories of the Future, 24-26 June 2015, Ischia (Naples), Italy (pp. 1101-1106). Elsevier, 41
Open this publication in new window or tab >>Why I want to be a future Swedish shop-floor operator
2016 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Elsevier, 2016
Series
Procedia CIRP, ISSN 2212-8271 ; 41
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-11335 (URN)10.1016/j.procir.2015.12.057 (DOI)000379247600189 ()2-s2.0-84968867064 (Scopus ID)
Conference
CIRP CMS 2015, 48th CIRP Conference on Manufacturing Systems, Research and Innovation in Manufacturing: Key Enabling Technologies for the Factories of the Future, 24-26 June 2015, Ischia (Naples), Italy
Funder
Knowledge Foundation
Available from: 2015-08-11 Created: 2015-08-11 Last updated: 2018-03-29Bibliographically approved
Adamson, G., Wang, L., Holm, M. & Moore, P. (2015). Adaptive Robot Control as a Service in Cloud Manufacturing. In: ASME 2015 International Manufacturing Science and Engineering Conference: Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing. Paper presented at ASME 2015 International Manufacturing Science and Engineering Conference, MSEC2015 June 8-12, 2015, Charlotte, North Carolina, USA (pp. Paper No. MSEC2015-9479). ASME Press, 2
Open this publication in new window or tab >>Adaptive Robot Control as a Service in Cloud Manufacturing
2015 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
ASME Press, 2015
Keywords
cloud manufacturing, cloud service, adaptive manufacturing, robot control
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-11375 (URN)10.1115/MSEC2015-9479 (DOI)000365147200041 ()2-s2.0-84945151417 (Scopus ID)978-0-7918-5683-3 (ISBN)
Conference
ASME 2015 International Manufacturing Science and Engineering Conference, MSEC2015 June 8-12, 2015, Charlotte, North Carolina, USA
Funder
Knowledge Foundation
Available from: 2015-08-19 Created: 2015-08-19 Last updated: 2018-03-29Bibliographically approved
Wang, L., Schmidt, B., Givehchi, M. & Adamson, G. (2015). Robotic Assembly Planning and Control with Enhanced Adaptability through Function Blocks. The International Journal of Advanced Manufacturing Technology, 77(1-4), 705-715
Open this publication in new window or tab >>Robotic Assembly Planning and Control with Enhanced Adaptability through Function Blocks
2015 (English)In: 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) Published
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.

Place, publisher, year, edition, pages
Springer London, 2015
Keywords
Adaptability, Robotic assembly, Trajectory planning, Function block
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-10466 (URN)10.1007/s00170-014-6468-1 (DOI)000350117300055 ()2-s2.0-84925461308 (Scopus ID)
Note

Published online: 22 October 2014

Available from: 2014-12-19 Created: 2014-12-19 Last updated: 2018-03-29Bibliographically approved
Holm, M., Cordero Garcia, A., Adamson, G. & Wang, L. (2014). Adaptive decision support for shop-floor operators in automotive industry. Paper presented at Variety Management in Manufacturing — Proceedings of the 47th CIRP Conference on Manufacturing Systems, CMS, Windsor, ON, Canada, 28 April 2014 through 30 April 2014. Procedia CIRP, 17, 440-445
Open this publication in new window or tab >>Adaptive decision support for shop-floor operators in automotive industry
2014 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 17, p. 440-445Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2014
Keywords
Decision support, Adaptability, Shop-floor operators
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-9381 (URN)10.1016/j.procir.2014.01.085 (DOI)000345458000075 ()2-s2.0-84904470152 (Scopus ID)
Conference
Variety Management in Manufacturing — Proceedings of the 47th CIRP Conference on Manufacturing Systems, CMS, Windsor, ON, Canada, 28 April 2014 through 30 April 2014
Funder
Knowledge Foundation, 20130303
Note

Edited by Hoda ElMaraghy

Available from: 2014-06-09 Created: 2014-06-09 Last updated: 2018-05-08Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-1265-8451

Search in DiVA

Show all publications