Numerical and data-driven uncertainty quantification of process parameters on clinching joint geometries

• Published in: Mechanics of advanced materials and structures 2024-11, Vol.31 (30), p.13083-13096
• Main Authors: Nguyen, Hoai Thanh, Nguyen, Duc Vinh, Nguyen, Minh Chien, Bui, Vuong Sy, Nguyen, Quang Ho, Wu, Yang-jiu, Lin, Pai-Chen, Tran, Xuan Van
• Format: Article
• Language: English
• Subjects: Bayesian analysis; Clinching; Computer simulation; Design optimization; Dynamic models; finite element method (FEM); identifying material parameters; Joint geometry; Mechanical joining; Parameter uncertainty; Process parameters; PyMC3; Statistical inference; uncertainty quantification; Workpieces; Yield strength
• ISSN: 1537-6494; 1537-6532
• DOI: 10.1080/15376494.2024.2332476

Clinching is a prevalent mechanical joining method involving clamping and locking two sheet work pieces using a punch and die. Numerical simulation offers a cost-effective alternative to time-consuming experiments for design. Our study employs an ABAQUS explicit dynamic model with remeshing to accurately replicate clinching joint geometry. However, the challenge of hard-to-measure parameters and the investigation of these parameters is limited leading to different simulation results compared to the millimeters-scale experiments. To address this, we apply PyMC3-based uncertainty quantification (UQ) to explore the material parameter effects on clinching dimensions. Our data-driven Bayesian inference models highlight friction and high-plastic-strain flow stress as significant geometry influencers. We propose estimating challenging-to-measure parameters from experiments, leveraging UQ for confident parameter intervals. Through PyMC3, our research offers insights into parameter impact on clinching dimensions, enhancing numerical simulations for process design optimization.