Fast optimisation of the formability of dry fabric preforms: A Bayesian approach

Abstract

A new framework for optimising the process of forming dry textile materials using finite element (FE) analysis and Gaussian Process (GP) regression is explored in this work. FE models were generated to simulate the double diaphragm forming process of non-crimp fabric over a hemisphere tool. A GP emulator was developed to regress the dataset generated by FE model, then used to optimise the forming process. Importantly the FE simulations can capture the formation of wrinkles during the process under different forming configurations and boundary conditions. Rigid blocks (risers) were introduced to the forming process to affect the defects generation by controlling the block positions. Several indices abstracted from FE output files were used to assess the wrinkle level of the forming simulations and compared, as the model output. A small dataset was generated by Latin hypercubic sampling (LHS) to train an initial GP surrogate model. Then, the prediction error of the model was reduced to an acceptable level (<10 %) through a Bayesian active learning method. The trained surrogate model was then used to optimise a forming process using only tens of simulations, rather than hundreds or even thousands, as required by traditional optimisation methods.

Keywords

• Dry Fabric
• Forming Process
• Gaussian Process Regression
• Surrogate Model
• Sequential Design