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Introductory study of sheet metal forming simulations to evaluate process robustness
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. ((Product Development Research Lab))ORCID iD: 0000-0002-8601-6825
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. Volvo Cars.ORCID iD: 0000-0002-6526-976X
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. Volvo Cars.ORCID iD: 0000-0002-7730-506X
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. ((Product Development Research Lab))ORCID iD: 0000-0002-7804-7306
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2018 (English)In: IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing (IOPP), 2018, Vol. 418, article id 012111Conference paper, Published paper (Refereed)
Abstract [en]

The ability to control quality of a part is gaining increased importance with desires to achieve zero-defect manufacturing. Two significant factors affecting process robustness in production of deep drawn automotive parts are variations in material properties of the blanks and the tribology conditions of the process. It is imperative to understand how these factors influence the forming process in order to control the quality of a formed part. This paper presents a preliminary investigation on the front door inner of a Volvo XC90 using a simulation-based approach. The simulations investigate how variation of material and lubrication properties affect the numerical predictions of part quality. To create a realistic lubrication profile in simulations, data of pre-lube lubrication amount, which is measured from the blanking line, is used. Friction models with localized friction conditions are created using TriboForm and is incorporated into the simulations. Finally, the Autoform-Sigmaplus software module is used to create and vary parameters related to material and lubrication properties within a user defined range. On comparing and analysing the numerical investigation results, it is observed that a correlation between the lubrication profile and the predicted part quality exists. However, variation in material properties seems to have a low influence on the predicted part quality. The paper concludes by discussing the relevance of such investigations for improved part quality and proposing suggestions for future work.

Place, publisher, year, edition, pages
Institute of Physics Publishing (IOPP), 2018. Vol. 418, article id 012111
Series
IOP Conference Series: Materials Science and Engineering, ISSN 1757-8981
Keywords [en]
Sheet Metal Forming, Friction Modelling, Process Robustness, Zero Defect Manufacturing, Industry 4.0, Digitization, Production Engineering.
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:bth-16613DOI: 10.1088/1757-899X/418/1/012111OAI: oai:DiVA.org:bth-16613DiVA, id: diva2:1222904
Conference
37th IDDRG Conference - Forming of High Performance Sheet Materials and Components, Waterloo, Canada.
Projects
Model Driven Development and Decision Support
Funder
Knowledge Foundation
Note

open access

Available from: 2018-06-23 Created: 2018-06-23 Last updated: 2019-11-25Bibliographically 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
Available from: 2019-11-25 Created: 2019-11-25 Last updated: 2019-12-02Bibliographically approved

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Tatipala, SravanPilthammar, JohanSigvant, MatsWall, Johan

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