AI-driven predictive modelling of residual stress of HVOF thermal sprayed carbon-based composite coatings using physics-informed neural networks
TL;DR
A combined Physics-Informed Neural Network (PINN) model is recommended to predict HVOF thermal sprayed carbon-based composite coatings residual stress distributions and achieves a better accuracy and much lower cost of computation than traditional ANN and FEA-driven surrogate models.
Abstract
The residual stress is an important factor affecting the performance, durability and failure behaviour of High Velocity Oxy-Fuel (HVOF) sprayed based composite coating of carbon. Finite element analysis (FEA) and other conventional numerical methods are computationally demanding and constrained by assumptions, whereas machine learning (ML) models without physical interpretation can be trained entirely on less assumptions and purely using only data. This paper recommends a combined Physics-Informed Neural Network (PINN) model to predict HVOF thermal sprayed carbon-based composite coatings residual stress distributions. The model incorporates herein governing thermo-mechanical equations, such as heat transfer and elastic plastic deformation as a direct part of the neural network loss. Training and validation are done using experimental and simulated datasets. The suggested PINN model achieves a better accuracy (R2 > 0.96) and much lower cost of computation than traditional ANN and FEA-driven surrogate models. The model gives a generalizable, scalable method of predictive modelling of coating integrity and optimization of the parameters of HVOF process.