Singularity-free PINN-based path planning method for the table-tilt five-axis machine*

The traditional five-axis machining path is typically planned in the workpiece coordinate system (WCS). However, when this path is mapped to the machine coordinate system (MCS), challenges such as singularities and abrupt movements of the rotary axis may arise, significantly impacting machining quality and efficiency. To tackle these issues, we propose a path planning method based on Physics-Informed Neural Networks (PINN) that generates an efficient tool path with iso-scallop height and free from singularities. In this approach, the surface is discretized into a triangular mesh, using its vertices as the input for the PINN, while the output are four scalar fields. To train the proposed PINN, we formulate four loss functions aimed at optimizing the iso-scallop height, ensuring path smoothness, addressing the kinematics of the machine tool, and guaranteeing the feasibility of the machining process. The tool path generated by our PINN demonstrates iso-scallop height and singularity-free performance, exhibiting significantly superior smoothness in both the tool path and the rotary motion of the machine tool compared to the existing benchmark.