Reducing Mesh Dependency in Dataset Generation for Machine Learning Prediction of Constitutive Parameters in Sheet Metal Forming

Given the extensive use of sheet metal-forming processes in the industry and the constant emergence of new materials, the accurate prediction of material constitutive models and their parameters is extremely important to enhance and optimise these processes. Machine learning techniques have proven to be highly promising for predicting these parameters using data obtained either experimentally or through numerical simulations. However, ML models are often constrained by the limited dataset coverage from numerical simulations, which restricts their predictive capability to specific finite element meshes, leading to potential dependency on the discretisation scheme. To address this challenge, a new approach is proposed that integrates ML with inter-extrapolation of strain data to a grid of points within the specimen domain, expanding the dataset coverage and reducing dependency on discrete mesh points. The current work explores this approach by interpolating and extrapolating manipulated data obtained from a Finite Element Analysis, considering a biaxial tensile test on a cruciform-shaped sample. Models are trained and evaluated for performance and robustness. The results show the high accuracy of the interpolated data, along with the excellent performance metrics and robustness of the trained models, ensuring the successful implementation of this approach.