Springback prediction using machine learning: an application for simplified automotive body-in-white structures

Abstract

Abstract Sheet metal stamped and welded assemblies, such as the ones used in automotive body-in-white (BIW) structures, have various sources of manufacturing variations during stamping and assembly processes. One of the major contributors to these variations is the springback on clamping release due to elastic recovery. Mitigating these variations requires expert knowledge of mechanical behavior, tooling, and process design. No analytical models can be used for the variety of geometries. Nonlinear FEA is also being used to predict springback, but it is time-consuming and requires specialized expertise, which makes it difficult to use in design exploration. Machine learning holds the promise of democratizing such complex analyses. This paper presents several case studies for data curation/generation, ML training, and validation. The prediction and quantification of the effects of springback are done on two levels: (i) low granularity, which involves predicting variations in certain parameters that are critical to measuring and understand spring back, and (ii) high granularity, predicting the shape of the component while taking into account the effects of springback and the stresses in the components. The data required to train, test, and validate the ML models were generated previously using an automated, integrated multi-stage simulation approach that was necessary to produce large datasets. Stamping simulations were validated against NUMISHEET benchmarks and also compared to test results published by other researchers. Subsequently, machine learning models were trained on the curated dataset to predict 2D stamped component shapes after springback and stress distributions across these shapes. For the assembly dataset, parameters such as unconstrained planar minimum zone magnitudes, angles between component planes, and twist angles are predicted using machine learning models, including linear and polynomial regression, decision trees, gradient boosting regression, support vector regression, and fully connected neural networks, and compared for their performance using consistent metrics. Hyper-parameter tuning is performed to optimize model performance, with artificial neural networks demonstrating promising capabilities in understanding variations in forming and multi-stage assembly processes.

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