Virtual data-driven optimisation for zero defect composites manufacture

Abstract

Manufacture-induced defects are a critical issue in composites applications that result in high volumes of material waste and costly experimental trials to help mitigate this. Advances in process modelling techniques have enabled the prediction of defects. However, the high computational cost of these tools limits their usefulness in an industrial context as they often struggle with the ever-increasing size of industrial structures, and present significant challenges to undertaking large optimisation problems. In this work, a recently proposed homogenisation scheme is used to accurately simulate the autoclave processing of a composite part of industrial complexity in a fraction of the time of other state-of-the-art processes. Physical manufacture of a demonstrator part is used to evaluate the model in terms of accuracy. Based on the observed good agreement (discrepancy of less than 1.5% in the thicker sections and less than 7% in the thinner region of the part), a data-driven optimisation of the part’s caul plate has been conducted that aims to mitigate defects whilst keeping cost and material use low. Further manufacturing trials validate the proposed framework by demonstrating successful defect elimination and a 25% improvement in the maximum local deviation from ideal design.

Keywords

• Composites 4.0
• Machine Learning
• Zero Defect
• Consolidation
• Tooling