Physics-informed neural networks for data-free surrogate modelling and engineering optimization – An example from composite manufacturing

Abstract

Engineering components require an optimization of design and manufacturing parameters to achieve maximum performance – usually involving numerous physics-based simulations. Optimizing these parameters is a resource-intensive endeavor, though, especially in high-dimensional scenarios or for complex materials like fiber reinforced plastics. Surrogate models are able to reduce the computational effort, however, data generation still proves to be resource-intensive. Additionally, their data-driven nature may lead to physically implausible results in limit cases. As a remedy, physics-informed neural networks (PINNs) include known physics into the training for enhanced surrogate reliability. This allows to cast a physically consistent, data- and mesh-free manufacturing surrogate for variable process conditions and material parameters. The paper demonstrates how PINNs can be embedded in a design-framework to enhance process understanding, to devise engineering-interpretable processing windows and to support time-efficient process optimization at the example of a thermochemical manufacturing process with fiber-reinforced composite materials. In this work, an over 500-fold speed up of the process optimization is achieved compared to conventional approaches.

Keywords

• Machine learning
• Physics-based modeling
• Mesh-free surrogate modeling
• Data-free surrogates
• Composite process optimization