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Qing, X, Kaid, T, Yildizand, C, Ates, F, Herencia, VZ, Wang, L, Mehmet, G and Ren, J (2023) Integrated Data-Led Studies of Electrical Resistance Spot Welded Joints. In: Intelligent Systems in Production Engineering and Maintenance III Conference Proceedings . pp. 120-130. (International Conference on Intelligent Systems in Production Engineering and Maintenance, 13-15 September 2023, Wrocław, Poland).
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Integrated Data led Studies of Electrical Resistance Spot Welded Joints .pdf - Accepted Version Download (521kB) | Preview |
Abstract
Welding techniques, such as electrical resistance spot welding, are widely used in automotive manufacturing. Welded joints of metals consist of complex material zones and regions of different shapes that need to be reflected in the modelling process. Parametric modelling is an efficient tool to develop data and knowledge for the understanding of mechanical behaviour and the effect of key design and material parameters for the applications and technological developments related to the welded joint. This paper presents the focus of an integrated data-led approach for studying electrical spot welding. The use of predictive modelling to evaluate the effects of key parameters of welded joints is analysed. Typical data from a parametric finite element model is presented and used to analyse the effects of sheet thickness on the deformation, stress and strain data. One approach is through comparing the data before fracture and the other approach involves analysing data at comparable displacements before the onset pf severe deformation stage. The use of the data-led predictive modelling in materials and process development, as well as training and application-specific research-technology integration, are discussed.
| Item Type: | Conference or Workshop Item (Paper) |
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| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Engineering |
| Publisher: | Springer Nature Switzerland |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 14 May 2024 11:12 |
| Last Modified: | 27 Sep 2024 00:50 |
| DOI or ID number: | 10.1007/978-3-031-44282-7_10 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/23243 |
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