Physics-Informed Neural ODE with Heterogeneous control Inputs (PINOHI) for quality prediction of composite adhesive joints

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Composite materials have long been used in various industries due to their superior properties such as high strength, light weight and corrosive resistance. Bonded composite joints are finding increasing applications, as they provide extensive structural benefits and design flexibility. On the other hand, the failure mechanism of composite adhesive joints is not fully understood. A model that bridges manufacturing parameters and final quality measures is highly desired for the design and optimization of the manufacturing process of composite adhesive joints. In this study, a novel framework of Physics-Informed Neural Ordinary Differential Equation (ODE) with Heterogeneous Control Input (PINOHI) is proposed, which links the heterogeneous manufacturing parameters to the final bonding quality of composite joints. The proposed model structure is heavily motivated by engineering knowledge, incorporating a calibrated mathematical physics model into the Neural ODE framework, which can significantly reduce the number of data samples required from costly experiments while maintaining high prediction accuracy. The proposed PINOHI model is implemented in the quality prediction of composite adhesive joints bonding problem. A set of experiments and associated data analytics are conducted to demonstrate the superior property of the PINOHI model by using both the leave-one-batch-out cross-validation and sensitivity analysis.