Modeling and Compensation for Five-Axis Machine Tool Errors

Abstract A neural network based compensation strategy for five-axis machine tool error correction has been developed. It is capable of significantly reducing geometric, kinematic and thermal errors between the tool tip and the workpiece. The error prediction ability of the neural network is demonstrated using a simulation of the compensation strategy. A two-stage training procedure for learning the weights of the neural network is proposed. The first stage uses a simulation model to train the dominant relationships between the errors and the sensors. The second stage of training would use actual measurements made on a five-axis machine tool to learn the remaining subtle relationships between the errors and the sensors. The simulation of the machine is based on Homogeneous Transformation Matrices (HTM). They model the position and orientation errors in each structural component. These error matrices are multiplied together to find the error between the tool and the workpiece. The coefficients required by the HTM model were selected so that the simulation model closely matched the measured performance of the five-axis machine tool. Thermocouples were used to measure temperature, and a laser interferometer, with the appropriate optics, was used for position and orientation error measurement. Preliminary simulation results based on an analysis of the spindle support arm and the Z column are available for the first stage of training. They indicate that a reduction in error from 100μm to 5μm is possible using a neural network model.