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A hybrid data- and model-based approach to process monitoring and control in sheet metal forming
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för maskinteknik.ORCID-id: 0000-0002-8601-6825
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för maskinteknik.ORCID-id: 0000-0002-7804-7306
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för maskinteknik.ORCID-id: 0000-0003-4875-391X
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för maskinteknik.ORCID-id: 0000-0002-9662-4576
2020 (Engelska)Ingår i: Processes, ISSN 2227-9717, Vol. 8, nr 1, artikel-id 89Artikel i tidskrift (Övrigt vetenskapligt) 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.

Ort, förlag, år, upplaga, sidor
MDPI, 2020. Vol. 8, nr 1, artikel-id 89
Nyckelord [en]
in-line measurement data; modelling and simulation; product quality; process performance; process monitoring and control; Industry 4.0; sheet metal forming
Nationell ämneskategori
Maskinteknik
Identifikatorer
URN: urn:nbn:se:bth-19038DOI: 10.3390/pr8010089ISI: 000516825300077OAI: oai:DiVA.org:bth-19038DiVA, id: diva2:1380208
Projekt
MD3S – Model Driven Development and Decision Support
Forskningsfinansiär
KK-stiftelsenTillväxtverketEuropeiska regionala utvecklingsfonden (ERUF)
Anmärkning

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

Tillgänglig från: 2019-12-18 Skapad: 2019-12-18 Senast uppdaterad: 2020-04-02Bibliografiskt granskad
Ingår i avhandling
1. Sheet metal forming in the era of industry 4.0: using data and simulations to improve understanding, predictability and performance
Öppna denna publikation i ny flik eller fönster >>Sheet metal forming in the era of industry 4.0: using data and simulations to improve understanding, predictability and performance
2019 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Karlskrona: Blekinge Tekniska Högskola, 2019
Serie
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 18
Nyckelord
Modelling, Simulation, Industry 4.0, Sheet Metal Forming, Process Monitoring, Process Control, Automation, Finite Element Analysis, Smart Manufacturing, Mechanical Engineering
Nationell ämneskategori
Maskinteknik
Identifikatorer
urn:nbn:se:bth-18954 (URN)978-91-7295-394-9 (ISBN)
Presentation
2019-12-20, J1610, Blekinge Institute of technology, Karlskrona, 13:00 (Engelska)
Opponent
Handledare
Projekt
Model Driven Development and Decision Support
Forskningsfinansiär
KK-stiftelsen
Anmärkning

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

Tillgänglig från: 2019-11-25 Skapad: 2019-11-25 Senast uppdaterad: 2019-12-18Bibliografiskt granskad

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