| description abstract | Abstract. Residual stresses (RS) arise in a wide range of manufacturing processes, including additive manufacturing, welding, forming, grinding, and machining. Accurate characterization and prediction of RS are crucial for optimizing functional performance and structural integrity, as tensile stresses reduce fatigue strength while compressive stresses enhance it. Traditional finite element methods provide detailed insights into RS distributions but are computationally expensive for real-time use. To overcome this limitation, we propose a physics-informed neural network (PINN) framework that embeds the Prandtl–Reuss constitutive equations for elastoplasticity directly into the loss function, enabling mesh-free forward simulation of RS distribution and inverse identification of parameters under Hertzian contact loading. The inverse formulation simultaneously reconstructs stress fields and identifies key parameters, namely the effective friction coefficient and normalized load factor, from sparse data, addressing the nonuniqueness and instability of traditional inverse methods. Validation against high-fidelity Runge–Kutta–Gill reference solutions shows that residual stress prediction errors remain below 8% across a wide parameter range, while parameter identification errors converge to below 1%. The PINN predictions were compared with representative experimental trends for Ti–6Al–4V under burnishing and orthogonal cutting, confirming consistency across chip-generating and chipless processes. By enabling real-time parameter updates from minimal data, the proposed framework can accelerate the development of digital twins for manufacturing, supporting predictive modeling and process optimization. This advancement provides physics-based rapid RS analysis for critical applications, including bearing contacts and machining process optimization, significantly improving speed and usability over traditional approaches. | |
