Data-Based and Analytical Models for Strength Prediction of Mechanical Joints

Niels Vancraeynest; Mathias Jäckel; Sam Coppieters; Welf Guntram Drossel

doi:10.4028/p-a33855

Paper Titles

Experimental and Numerical Investigation on Manufacturing-Induced Pre-Strain on the Load-Bearing Capacity of Clinched Joints
p.1516

Analysis of Machine-Sided Influences during Clinching
p.1527

Influence of the Sheet Edge Condition on the Fracture Behavior of Riv-Bonded Aluminum-Magnesium Joints
p.1541

Preliminary Study of FSW Process of Dissimilar Aluminium Alloys in Battery Pack Assembly for Hybrid Vehicles Production
p.1549

Data-Based and Analytical Models for Strength Prediction of Mechanical Joints
p.1556

Functionality Study of an Optical Measurement Concept for Local Force Signal Determination in High Strain Rate Tensile Tests
p.1564

Characterization of Abrasion and Erosion Mechanisms during Abrasive Waterjet Machining of Hard Metals
p.1575

Influence of the Workpiece Material and Cooling Condition on the Texture Obtained through Ultrasonic Vibration-Assisted Machining
p.1581

Nitrogen Supersaturation into AISI420 Mold for Precise Machining
p.1591

HomeKey Engineering MaterialsKey Engineering Materials Vol. 926Data-Based and Analytical Models for Strength...

Data-Based and Analytical Models for Strength Prediction of Mechanical Joints

Abstract:

Currently the design of mechanical joining processes like self-pierce riveting with semi-tubular rivet (SPR-ST) and clinching for production is subject to complex and experimental test series in which process parameters such as the rivet or die geometry are varied iteratively and based on experience until a suitable joint contour and strength is achieved. To simplify the use of mechanical joining technologies these development cycles and thereby the effort for implementation into production must be reduced. In this paper, the numerical data-acquisition and the development of algorithm-based and analytical models for strength prediction for SPR-ST and Clinching is described. Therefore, an extensive experimental and numerical database regarding the SPR-ST process and strength of steel and aluminum joints with tensile strengths of the sheets between 200 - 1000 MPa was generated for the building of the models. This process data could then be used for the training and evaluation of different prediction models. The goal of the research presented here is to enable an immediate prediction of the quasi-static joint strength, based on the input parameters such as properties of the parts to be joined and process parameters.

By email Full Text Pdf

You have full access to the following eBook

Achievements and Trends in Material Forming

Read eBook

Info:

Periodical:

Key Engineering Materials (Volume 926)

Pages:

1556-1563

DOI:

https://doi.org/10.4028/p-a33855

Citation:

Cite this paper

Online since:

July 2022

Authors:

Niels Vancraeynest, Mathias Jäckel*, Sam Coppieters, Welf Guntram Drossel

Keywords:

Analytical Prediction, Machine Learning, Mechanical Joining, Numerical Simulation

Export:

RIS, BibTeX

Permissions:

Creative Commons CC BY 4.0

* - Corresponding Author

References

[1] Lambiase, F. u. Di Ilio, A.: Optimization of the Clinching Tools by Means of Integrated FE Modelling and Artificial Intelligence Techniques. Procedia CIRP 12, S. 163–168, (2014).

DOI: 10.1016/j.procir.2013.09.029

Google Scholar

[2] Oh, S., Kim, H. K., Jeong, T.-E., Kam, D.-H. u. Ki, H.: Deep-Learning-Based Predictive Architectures for Self-Piercing Riveting Process. IEEE Access 8, S. 116254–116267, (2020).

DOI: 10.1109/access.2020.3004337

Google Scholar

[3] Karathanasopoulos, N., Pandya, K. S., Mohr, D.: Self-piercing riveting process: Prediction of joint characteris-tics through finite element and neural network modeling. Journal of Advanced Joining Processes 3 100040, (2021).

DOI: 10.1016/j.jajp.2020.100040

Google Scholar

[4] Jäckel, M., Falk, T. & Drossel, WG. Algorithm-based design of mechanical joining processes. Prod. Eng. Res. Devel. 16, 285–293 (2022). https://doi.org/10.1007/s11740-022-01121-2.

DOI: 10.1007/s11740-022-01121-2

Google Scholar

[5] Jäckel M, Coppieters S, Vandermeiren N et al. (2020). Process-oriented Flow Curve Determination at Mechanical Joining. Procedia Manufacturing, Vol.47, 368-374.

DOI: 10.1016/j.promfg.2020.04.289

Google Scholar

[6] https://scikit-learn.org/stable/supervised_learning.html#supervised-learning, October 7th, (2021).

Google Scholar

[7] Jäckel M, Falk T, Georgi J, Drossel WG (2020) Gathering of Process Data through Numerical Simulation for the Application of Machine Learning Prognosis Algorithms. Procedia Manufacturing, 47, 608-614.

DOI: 10.1016/j.promfg.2020.04.186

Google Scholar

[8] E. Rubio, Analytical methods application to the study of tube drawing processes with fixed conical inner plug: Slab and upper bound methods.,, Journal of Achievements in Materials and Manufacturing Engineering., vol. 14, no. 1-2, pp.119-129, (2005).

Google Scholar

[9] L. Chan-Joo, K. Jae-Young, L. Sang-Kon, K. Dae-Cheol and K. Byung-Min, Design of mechanical clinching tools for joining of aluminium alloy sheets,, Materials & Design, vol. 31, no. 4, pp.1854-1861, (2010).

DOI: 10.1016/j.matdes.2009.10.064

Google Scholar

[10] Y. He and Z. Xu, Mechanical Analysis for AL5052 Joined by Mechanical Clinching.,, in International Conference on Sustainable Energy and Environmental Engineering, (2016).

DOI: 10.2991/icseee-15.2016.125

Google Scholar

[11] S. Coppieters, P. Lava, S. Baes, H. Sol, P. Houtte and D. Debruyne, Analytical method to predict the pull-out strength of clinched connections.,, Thin-Walled Structures, vol. 52, pp.42-52, (2012).

DOI: 10.1016/j.tws.2011.12.002

Google Scholar

[12] S. Coppieters, Experimental and numerical study of clinched connections, Ph.D. thesis,, KU Leuven, (2012).

Google Scholar

[13] X. C. He, A. F. Gao, H. Y. Yang and B. Xing, Mechanical Behavior of Clinched Sheet Material Joints and Strength Design Procedure.,, Acta Physica Polonica A, vol. 129, pp.698-700, (2016).

DOI: 10.12693/aphyspola.129.698

Google Scholar