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Schmidt, B. & Wang, L. (2018). Cloud-enhanced predictive maintenance. The International Journal of Advanced Manufacturing Technology, 99(1-4), 5-13
Open this publication in new window or tab >>Cloud-enhanced predictive maintenance
2018 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 99, no 1-4, p. 5-13Article in journal (Refereed) Published
Abstract [en]

Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and makes decisions upon this prediction. The main aim of the present research is to achieve an improvement in predictive condition-based maintenance decision making through a cloud-based approach with usage of wide information content. For the improvement, it is crucial to identify and track not only condition related data but also context data. Context data allows better utilisation of condition monitoring data as well as analysis based on a machine population. The objective of this paper is to outline the first steps of a framework and methodology to handle and process maintenance, production, and factory related data from the first lifecycle phase to the operation and maintenance phase. Initial case study aims to validate the work in the context of real industrial applications.

Place, publisher, year, edition, pages
Springer, 2018
Keywords
Predictive maintenance, Condition-based maintenance, Context awareness, Cloud manufacturing
National Category
Reliability and Maintenance
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-12331 (URN)10.1007/s00170-016-8983-8 (DOI)000445800600002 ()2-s2.0-85061379865 (Scopus ID)
Available from: 2016-06-07 Created: 2016-06-07 Last updated: 2019-02-22Bibliographically approved
Schmidt, B., Gandhi, K. & Wang, L. (2018). Diagnosis of machine tools: assessment based on double ball-bar measurements from a population of similar machines. Paper presented at 51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018. Procedia CIRP, 72, 1327-1332
Open this publication in new window or tab >>Diagnosis of machine tools: assessment based on double ball-bar measurements from a population of similar machines
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 1327-1332Article in journal (Refereed) Published
Abstract [en]

The presented work is toward population-based predictive maintenance of manufacturing equipment with consideration of the automaticselection of signals and processing methods. This paper describes an analysis performed on double ball-bar measurement from a population ofsimilar machine tools. The analysis is performed after aggregation of information from Computerised Maintenance Management System,Supervisory Control and Data Acquisition, NC-code and Condition Monitoring from a time span of 4 years. Economic evaluation is performedwith use of Monte Carlo simulation based on data from real manufacturing setup.

Keywords
population based maintenance, condition monitoring, automatic signal selection
National Category
Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15117 (URN)10.1016/j.procir.2018.03.208 (DOI)2-s2.0-85049577336 (Scopus ID)
Conference
51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018
Available from: 2018-05-02 Created: 2018-05-02 Last updated: 2018-10-31Bibliographically approved
Wang, L., Mohammed, A., Wang, X. V. & Schmidt, B. (2018). Energy-efficient robot applications towards sustainable manufacturing. International journal of computer integrated manufacturing (Print), 31(8), 692-700
Open this publication in new window or tab >>Energy-efficient robot applications towards sustainable manufacturing
2018 (English)In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 31, no 8, p. 692-700Article in journal (Refereed) Published
Abstract [en]

The cloud technology provides sustainable solutions to the modern industrial robotic cells. Within the context, the objective of this research is to minimise the energy consumption of robots during assembly in a cloud environment. Given a robot path and based on the inverse kinematics and dynamics of the robot from the cloud, a set of feasible configurations of the robot can be derived, followed by calculating the desirable forces and torques on the joints and links of the robot. Energy consumption is then calculated for each feasible configuration along the path. The ones with the lowest energy consumption are chosen. Since the energy-efficient robot configurations lead to reduced overall energy consumption, this approach becomes instrumental and can be applied to energy-efficient robotic assembly. This cloud-based energy-efficient approach for robotic applications can largely enhance the current practice as demonstrated by the results of three case studies, leading towards sustainable manufacturing.

Place, publisher, year, edition, pages
Taylor & Francis, 2018
Keywords
energy-efficiency, robot configuration, trajectory, robotic assembly, cloud manufacturing
National Category
Robotics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-14146 (URN)10.1080/0951192X.2017.1379099 (DOI)000436966300003 ()2-s2.0-85029705478 (Scopus ID)
Available from: 2017-09-22 Created: 2017-09-22 Last updated: 2018-09-25Bibliographically 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, 57(12), 3954-3974
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 1366-588X, Vol. 57, no 12, p. 3954-3974Article in journal (Refereed) Published
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)000474250800011 ()2-s2.0-85057893093 (Scopus ID)
Available from: 2018-12-21 Created: 2018-12-21 Last updated: 2019-08-06Bibliographically approved
Danielsson, O., Syberfeldt, A., Holm, M. & Wang, L. (2018). Operators perspective on augmented reality as a support tool in engine assembly. Paper presented at 51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018. Procedia CIRP, 72, 45-50
Open this publication in new window or tab >>Operators perspective on augmented reality as a support tool in engine assembly
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 45-50Article in journal (Refereed) Published
Abstract [en]

Augmented Reality (AR) has shown its potential in supporting operators in manufacturing. AR-glasses as a platform both in industrial use are emerging markets, thereby making portable and hands-free AR more and more feasible. An important aspect of integrating AR as a support tool for operators is their acceptance of the technology. This paper presents the results of interviewing operators regarding their view on AR technology in their field and observing them working in automotive engine assembly and how they interact with current instructions. The observations and follow-up questions identified three main aspects of the information that the operators looked at: validating screw torque, their current assembly time, and if something went wrong. The interviews showed that a large amount of the operators were positive towards using AR in assembly. This has given an insight in both the current information interaction the operators do and their view on the potential in using AR. Based on these insights we suggest a mock-up design of an AR-interface for engine assembly to serve as a base for future prototype designs.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
augmented reality, engine assembly, operator, förstärkt verklighet, motormontering, operatör
National Category
Communication Systems
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-15937 (URN)10.1016/j.procir.2018.03.153 (DOI)2-s2.0-85049604095 (Scopus ID)
Conference
51st CIRP Conference on Manufacturing Systems, Stockholm, May 16-18, 2018
Available from: 2018-07-06 Created: 2018-07-06 Last updated: 2018-10-31Bibliographically approved
Schmidt, B. & Wang, L. (2018). Predictive Maintenance of Machine Tool Linear Axes: A Case from Manufacturing Industry. Paper presented at 28th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2018) June 11-14, 2018, Columbus, OH, USA. Procedia Manufacturing, 17, 118-125
Open this publication in new window or tab >>Predictive Maintenance of Machine Tool Linear Axes: A Case from Manufacturing Industry
2018 (English)In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 17, p. 118-125Article in journal (Refereed) Published
Abstract [en]

In sustainable manufacturing, the proper maintenance is crucial to minimise the negative environmental impact. In the context of Cloud Manufacturing, Internet of Things and Big Data, amount of available information is not an issue, the problem is to obtain the relevant information and process them in a useful way. In this paper a maintenance decision support system is presented that utilises information from multiple sources and of a different kind. The key elements of the proposed approach are processing and machine learning method evaluation and selection, as well as estimation of long-term key performance indicators (KPIs) such as a ratio of unplanned breakdowns or a cost of maintenance approach. Presented framework is applied to machine tool linear axes. Statistical models of failures and Condition Based Maintenance (CBM) are built based on data from a population of 29 similar machines from the period of over 4 years and with use of proposed processing approach. Those models are used in simulation to estimate the long-term effect on selected KPIs for different strategies. Simple CBM approach allows, in the considered case, a cost reduction of 40% with the number of breakdowns reduced 6 times in respect to an optimal time-based approach.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
predictive maintenance, condition monitoring, machine tool
National Category
Reliability and Maintenance
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-16410 (URN)10.1016/j.promfg.2018.10.022 (DOI)000471035200015 ()2-s2.0-85060444616 (Scopus ID)
Conference
28th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2018) June 11-14, 2018, Columbus, OH, USA
Available from: 2018-11-19 Created: 2018-11-19 Last updated: 2019-06-27Bibliographically approved
Holm, M., Frantzén, M., Aslam, T., Moore, P. & Wang, L. (2017). A methodology facilitating knowledge transfer to both research experienced companies and to novice SMEs. International Journal of Enterprise Network Management, 8(2), 123-140, Article ID IJENM0080202.
Open this publication in new window or tab >>A methodology facilitating knowledge transfer to both research experienced companies and to novice SMEs
Show others...
2017 (English)In: International Journal of Enterprise Network Management, ISSN 1748-1252, Vol. 8, no 2, p. 123-140, article id IJENM0080202Article in journal (Refereed) Published
Abstract [en]

In this paper, knowledge transfer is defined as a process of disseminating both technological and theoretical understanding as well as enhancing both industrial and academic knowledge through conducted research to project partners collaborating within a research project. To achieve this, a new methodology called 'user groups' is introduced. It facilitates knowledge transfer between project participants in collaborative research programs engaging both experienced and unexperienced partners regardless of level of input. The introduced methodology 'user groups' provides tools for collaborating with several research partners even though their levels of engagement in the project and prior research experience may vary without dividing them into separate groups. It enables all project partners to gain new knowledge and by so doing extending the knowledge society. The case study shows that the eight engaged companies are able to cooperate, achieve their own objectives and, both jointly and individually, contribute to the overall project goals.

Place, publisher, year, edition, pages
InderScience Publishers, 2017
Keywords
methodology facilitating knowledge transfer, technology transfer, SME, small and medium enterprises, knowledge society
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-13999 (URN)10.1504/IJENM.2017.10006499 (DOI)2-s2.0-85027189530 (Scopus ID)
Funder
Knowledge Foundation, 20130303Vinnova, 2014-05220
Available from: 2017-08-17 Created: 2017-08-17 Last updated: 2019-05-13Bibliographically approved
Holm, M., Danielsson, O., Syberfeldt, A., Moore, P. & Wang, L. (2017). Adaptive instructions to novice shop-floor operators using Augmented Reality. Journal of Industrial and Production Engineering, 34(5), 362-374
Open this publication in new window or tab >>Adaptive instructions to novice shop-floor operators using Augmented Reality
Show others...
2017 (English)In: Journal of Industrial and Production Engineering, ISSN 2168-1015, Vol. 34, no 5, p. 362-374Article in journal (Refereed) Published
Abstract [en]

This paper presents a novel system using Augmented Reality and Expert Systems to enhance the quality and efficiency of shop-floor operators. The novel system proposed provides an adaptive tool that facilitates and enhances support on the shop-floor, due to its ability to dynamically customize the instructions displayed, dependent upon the competence of the user. A comparative study has been made between an existing method of quality control instructions at a machining line in an automotive engine plant and this novel system. It has been shown that the new approach outcompetes the existing system, not only in terms of perceived usability but also with respect to two other important shop-floor variables: quality and productivity. Along with previous research, the outcomes of these test cases indicate the value of using Augmented Reality technology to enhance shop-floor operators’ ability to learn and master new tasks.

Place, publisher, year, edition, pages
Taylor & Francis, 2017
Keywords
Adaptive instructions, Augmented reality, Shop-floor operators, Expert systems, Shop-floor support
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-13565 (URN)10.1080/21681015.2017.1320592 (DOI)000409142300004 ()2-s2.0-85018720899 (Scopus ID)
Projects
SYMBIO-TIC [637107] & YOU2 [20130303]
Funder
Knowledge Foundation, 20130303EU, Horizon 2020, 637107
Available from: 2017-05-31 Created: 2017-05-31 Last updated: 2019-05-13Bibliographically approved
Danielsson, O., Syberfeldt, A., Brewster, R. & Wang, L. (2017). Assessing Instructions in Augmented Reality for Human-Robot Collaborative Assembly by Using Demonstrators. In: Mitchell M. Tseng, Hung-Yin Tsai, Yue Wang (Ed.), Manufacturing Systems 4.0 - Proceedings of the 50th CIRP Conference on Manufacturing Systems: . Paper presented at The 50th CIRP Conference on Manufacturing Systems, Taichung City, Taiwan on May 3rd – 5th, 2017 (pp. 89-94). Elsevier, 63
Open this publication in new window or tab >>Assessing Instructions in Augmented Reality for Human-Robot Collaborative Assembly by Using Demonstrators
2017 (English)In: Manufacturing Systems 4.0 - Proceedings of the 50th CIRP Conference on Manufacturing Systems / [ed] Mitchell M. Tseng, Hung-Yin Tsai, Yue Wang, Elsevier, 2017, Vol. 63, p. 89-94Conference paper, Published paper (Refereed)
Abstract [en]

Robots are becoming more adaptive and aware of their surroundings. This has opened up the research area of tight human-robot collaboration,where humans and robots work directly interconnected rather than in separate cells. The manufacturing industry is in constant need ofdeveloping new products. This means that operators are in constant need of learning new ways of manufacturing. If instructions to operatorsand interaction between operators and robots can be virtualized this has the potential of being more modifiable and available to the operators.Augmented Reality has previously shown to be effective in giving operators instructions in assembly, but there are still knowledge gapsregarding evaluation and general design guidelines. This paper has two aims. Firstly it aims to assess if demonstrators can be used to simulatehuman-robot collaboration. Secondly it aims to assess if Augmented Reality-based interfaces can be used to guide test-persons through apreviously unknown assembly procedure. The long-term goal of the demonstrator is to function as a test-module for how to efficiently instructoperators collaborating with a robot. Pilot-tests have shown that Augmented Reality instructions can give enough information for untrainedworkers to perform simple assembly-tasks where parts of the steps are done with direct collaboration with a robot. Misunderstandings of theinstructions from the test-persons led to multiple errors during assembly so future research is needed in how to efficiently design instructions.

Place, publisher, year, edition, pages
Elsevier, 2017
Series
Procedia CIRP, E-ISSN 2212-8271 ; 63
Keywords
Augmented Reality, Human Robot Collaboration, Assembly
National Category
Robotics
Research subject
Production and Automation Engineering; INF201 Virtual Production Development
Identifiers
urn:nbn:se:his:diva-14023 (URN)10.1016/j.procir.2017.02.038 (DOI)000418465500015 ()2-s2.0-85028641292 (Scopus ID)
Conference
The 50th CIRP Conference on Manufacturing Systems, Taichung City, Taiwan on May 3rd – 5th, 2017
Projects
SYMBIO-TIC
Available from: 2017-08-22 Created: 2017-08-22 Last updated: 2019-01-24Bibliographically 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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8679-8049

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