A General Model for Predicting Machining Deformation Fields in Structural Components with Varying Geometries Using a Geometry-Oriented Neural Operator

Abstract

Controlling machining deformations resulting from unbalanced stress fields inside structural components is a significant challenge in the manufacturing industry. Prediction of machining deformation fields is fundamental for deformation control and requires numerous iterations to optimize the machining process. Conventional prediction methods such as numerical analysis are tailored to a fixed geometry, making them time-consuming and inefficient for components with various geometries. In this study, a general data-driven model is proposed for predicting machining deformation fields in components with varying geometries and stress fields. This model is based on a geometry-oriented neural operator that incorporates global geometry information into the function space, modeling the relationship between the input function (stress fields) and the output function (deformation fields). Global geometric information is extracted using a graph neural network applied to a geometric graph and embedded into the input and output function space through an encoder-query framework. The proposed model achieved low root-mean-squared errors ranging from 0.001 to 0.016 mm, with maximum prediction errors between 0.003and 0.047 mm across different types of components, including beams and frames. The main contribution of this research is the significant advancement in the application of neural operators to the development of general models for predicting machining deformation. The underlying principles of the proposed model provide an important reference for wider applications related to the control of machining deformation in the context of digital and intelligent manufacturing.