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A hybrid data- and model-based approach to process monitoring and control in sheet metal forming
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering.ORCID iD: 0000-0002-8601-6825
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering.ORCID iD: 0000-0002-7804-7306
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering.ORCID iD: 0000-0003-4875-391X
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering.ORCID iD: 0000-0002-9662-4576
2020 (English)In: Processes, ISSN 2227-9717, Vol. 8, no 1, article id 89Article in journal (Other academic) Published
Abstract [en]

The ability to predict and control the outcome of the sheet metal forming process demands holistic knowledge of the product/process parameter influences and their contribution in shaping the output product quality. Recent improvements in the ability to harvest in-line production data and the increased capability to understand complex process behaviour through computer simulations open up the possibility for new approaches to monitor and control production process performance and output product quality. This research presents an overview of the common process monitoring and control approaches while highlighting their limitations in handling the dynamics of the sheet metal forming process. The current paper envisions the need for a collaborative monitoring and control system for enhancing production process performance. Such a system must incorporate comprehensive knowledge regarding process behaviour and parameter influences in addition to the current-system-state derived using in-line production data to function effectively. Accordingly, a framework for monitoring and control within automotive sheet metal forming is proposed. The framework addresses the current limitations through the use of real-time production data and reduced process models. Lastly, the significance of the presented framework in transitioning to the digital manufacturing paradigm is reflected upon.

Place, publisher, year, edition, pages
MDPI, 2020. Vol. 8, no 1, article id 89
Keywords [en]
in-line measurement data; modelling and simulation; product quality; process performance; process monitoring and control; Industry 4.0; sheet metal forming
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:bth-19038DOI: 10.3390/pr8010089ISI: 000516825300077OAI: oai:DiVA.org:bth-19038DiVA, id: diva2:1380208
Projects
MD3S – Model Driven Development and Decision Support
Funder
Knowledge FoundationSwedish Agency for Economic and Regional GrowthEuropean Regional Development Fund (ERDF)
Note

Publisher's link: https://www.mdpi.com/2227-9717/8/1/89

Available from: 2019-12-18 Created: 2019-12-18 Last updated: 2020-04-02Bibliographically approved
In thesis
1. Sheet metal forming in the era of industry 4.0: using data and simulations to improve understanding, predictability and performance
Open this publication in new window or tab >>Sheet metal forming in the era of industry 4.0: using data and simulations to improve understanding, predictability and performance
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

A major issue within automotive Sheet Metal Forming (SMF) concerns ensuring desired output product quality and consistent process performance. This is fueled by complex physical phenomena, process fluctuations and complicated parameter correlations governing the dynamics of the production processes. The aim of the thesis is to provide a deeper understanding of the challenges and opportunities in this regard within automotive SMF. The research is conducted in collaboration with a global automotive manufacturer. 

The research shows that systematic investigations using process simulation models allow exploration of the product-process parameter interdependencies and their influence on the output product quality. Furthermore, it is shown that incorporating in-line measured data within process simulation models enhance model prediction accuracy. In this regard, automating the data processing and model configuration tasks reduces the overall modelling effort.

However, utilization of results from process simulations within a production line requires real-time computational performance. The research hence proposes the use of reduced process models derived from process simulations in combination with production data, i.e. a hybrid data- and model-based approach. Such a hybrid approach would benefit process performance by capturing the deviations present in the real process while also incorporating the enhanced process knowledge derived from process simulations. Bringing monitoring and control realms within the production process to interact synergistically would facilitate the realization of such a hybrid approach.

The thesis presents a procedure for exploring the causal relationship between the product-process parameters and their influence on output product quality in addition to proposing an automated approach to process and configure in-line measured data for incorporation within process simulations. Furthermore, a framework for enhancing output product quality within automotive SMF is proposed. Based on the thesis findings, it can be concluded that in-line measured data combined with process simulations hold the potential to unveil the convoluted interplay of process parameters on the output product quality parameters.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2019
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 18
Keywords
Modelling, Simulation, Industry 4.0, Sheet Metal Forming, Process Monitoring, Process Control, Automation, Finite Element Analysis, Smart Manufacturing, Mechanical Engineering
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:bth-18954 (URN)978-91-7295-394-9 (ISBN)
Presentation
2019-12-20, J1610, Blekinge Institute of technology, Karlskrona, 13:00 (English)
Opponent
Supervisors
Projects
Model Driven Development and Decision Support
Funder
Knowledge Foundation
Note

Related work:

1) http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14412

2) http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14388

3) http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18935

Available from: 2019-11-25 Created: 2019-11-25 Last updated: 2019-12-18Bibliographically approved

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Tatipala, SravanWall, JohanJohansson, ChristianLarsson, Tobias
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