Spatially-Aware Milling Surface Flatness Prediction Through Physics-Based Graph Neural Network

Abstract Milling operations are critical in the manufacturing sector, pivotal for achieving desired product specifications and surface quality. However, post-milling deformations, primarily induced by residual stress, can significantly affect the final flatness and surface integrity of workpieces, potentially deviating from manufacturing tolerances and standards. This issue is pronounced in the machining of A2024 aluminum, a material favored for its strength and lightweight properties but susceptible to such stress-related deformations. Finite element Method (FEM) can offer valuable insights but at the cost of extensive computational resources, making them less viable for real-time applications and rapid iteration across large parameter spaces. Addressing this challenge, our study introduces a spatially-aware predictive framework utilizing the physics-based Graph Neural Network (GNN). This framework leverages the spatial relationships between nodes on the milling surface and incorporates physics-guided insights from Jonson-Cook model to forecast post-milling surface flatness with crucial process inputs. A meticulously crafted FEM model drives our automated data generation pipeline, ensuring the creation of high-quality data. The fusion of FEM-derived data with specially tailored GNN modeling represents a paradigm shift in predictive machining results, enabling rapid and reliable flatness prediction to support adaptive manufacturing strategies. This framework is generic, and has the potential of being extended to other manufacturing processes.