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Data-driven modelling in the era of Industry 4.0: A case study of friction modelling in sheet metal forming simulations
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. (MD3S – Model Driven Development and Decision Support)ORCID iD: 0000-0002-8601-6825
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. Volvo Cars.ORCID iD: 0000-0002-7730-506X
2018 (English)In: Journal of Physics: Conference Series 1063 (2018) 012135, Institute of Physics Publishing (IOPP), 2018, Vol. 1063Conference paper, Published paper (Refereed)
Abstract [en]

With growing demands on quality of produced parts, concepts like zero-defect manufacturing are gaining increasing importance. As one of the means to achieve this, industries strive to attain the ability to control product/process parameters through connected manufacturing technologies and model-based control systems that utilize process/machine data for predicting optimum system conditions without human intervention. Present work demonstrates an automated approach to process in-line measured data of tribology conditions and incorporate it within sheet metal forming (SMF) simulations to enhance the prediction accuracy while reducing overall modelling effort. The automated procedure is realized using a client-server model with an in-house developed application as the server and numerical computing platform/commercial CAD software as clients. Firstly, the server launches the computing platform for processing measured data from the production line. Based on this analysis, the client then executes CAD software for modifying the blank model thereby enabling assignment of localized friction conditions. Finally, the modified blank geometry and accompanied friction values is incorporated into SMF simulations. The presented procedure reduces time required for setting up SMF simulations as well as improves the prediction accuracy. In addition to outlining suggestions for future work, paper concludes by discussing the importance of the presented procedure and its significance in the context of Industry 4.0.

Place, publisher, year, edition, pages
Institute of Physics Publishing (IOPP), 2018. Vol. 1063
Keywords [en]
Sheet Metal Forming, Friction Modelling, Automation, Zero Defect Manufacturing, Industry 4.0, Digitization, Data Analytics, Production Engineering.
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:bth-16841DOI: 10.1088/1742-6596/1063/1/012135OAI: oai:DiVA.org:bth-16841DiVA, id: diva2:1237245
Conference
NUMISHEET 2018, Tokyo, Japan
Projects
MD3S – Model Driven Development and Decision Support
Funder
Knowledge Foundation
Note

Open access

Available from: 2018-08-08 Created: 2018-08-08 Last updated: 2020-02-26Bibliographically 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, ChristianSigvant, Mats
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