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Improving Multi-Axis Contouring Accuracy Using Neural Networks


 The contouring accuracy of a multi-axis machine is affected by the presence of geometric errors, caused by structural imperfections of the machine links and guideways. In recent years, homogeneous transformation matrices and rigid body kinematics have been successfully utilized to model the effects of these errors and to predict the actual tool position in the machine workspace. However, for contouring applications the command coordinates provided to the motion controller must be corrected to attain the desired actual coordinates. This task requires solving the inverse kinematics of the machine error model. In this paper, a direct method for solving the inverse kinematics of a Coordinate Measuring Machine (CMM) is proposed using Radial Basis Functions (RBF) neural networks. By training the neural network to learn the inverse kinematics of the machine error model, the position error vector at any point with in the workspace can be predicted. A novel training strategy is implemented where the spatial deformation of the machine workspace is captured using Self-Organizing Feature Maps (SOFM) and incorporated into the design and training of the RBF network, thereby enhancing its accuracy.