Neural Network Error Compensation of Machine Tools and Coordinate Measuring Machines

该奖项支持涉及人工神经网络在机床和坐标测量机定位误差确定和补偿问题中的适用性的研究。本文将探讨的研究问题有：(1)使用神经网络将工作空间位置映射到工具点误差向量，并利用机器测量精确物理工件获得的训练数据预测和补偿几何诱导误差。(2)利用少量策略性位置的温度探头作为扩展神经网络的额外输入，预测和补偿热诱导误差，并对机器的不同热状态进行人工测量。(3)生成物理工件设计规则，保证神经网络误差补偿技术的最优性能。(4)研究了利用加工零件尺寸检测结果更新神经网络训练集的可能性；从而闭合过程输出质量和过程输入变量之间的循环，从而在自动化工厂环境中实现持续的过程改进。(5)研究了混合神经网络机床控制器的结构特征和计算能力。

This award supports research involving a study of the applicability of Artificial Neural Networks to the problem of determination and compensation of positioning errors in machine tools and coordinate measuring machines. Research issues which will be explored are: (1) Prediction and compensation of geometrically induced errors using a neural network to map workspace positions into tool point error vectors with training data obtained from machine measurement of a precision physical artifact. (2) Prediction and compensation of thermally induced errors using a small number of strategically located temperature probes as additional input to an expanded neural net, and performing the artifact measurement for differing thermal states of the machine. (3) Generation of design rules for the design of the physical artifact to insure optimal performance of the neural net error compensation technique. (4) Investigation of the possibility of using the results of dimensional inspection of manufactured parts to update the training set for the neural network; thus closing the loop between process output quality and process input variables, resulting in continuous process improvement in an automated factory environment. (5) Investigation into the necessary architectural features and computational capabilities of a hybrid neural net machine tool controller.