Physics-integrated Neural Differentiable (PiNDiff) Model for Composites Manufacturing

Abstract

Various manufacturing technologies are being developed to improve the manufacturing of composites owing to their low weight and high performance. The mechanical properties of the composites depend on various variables and parameters of the manufacturing process, which are challenging, if not impossible, to determine and optimize experimentally. Traditional first-principle modeling approaches are not accessible due to the complex physics involved. A hybrid model that combines incomplete physics knowledge with available measurement data within a differentiable programming framework opens up new avenues to tackle the challenges. In this work, a physics-integrated neural differentiable (PiNDiff) model is developed, where the partially known physics is integrated into the recurrent network architecture to enable effective learning and generalization. The merit and potential of the proposed method have been demonstrated in modeling the curing process of thick thermoset composite laminates, whose governing physics is partially given. The proposed PiNDiff model shows the capability to learn unknown physics from the limited, indirect data and, meanwhile, can be used to infer unobserved variables and parameters. The performance of the PiNDiff model has been compared with two state-of-the-art (SOTA) black-box deep learning models, and its advantages over the purely data-driven models and first-principles physics-based models have been discussed in detail. The demonstrated PiNDiff strategy may provide a general strategy to model phenomena where physics is only partially known and sparse, indirect data are available.