Neural Network Error Compensation of Machine Tools and Coordinate Measuring Machines

该奖项支持涉及研究的研究， 人工神经网络在解决 机床定位误差的测定与补偿 工具和坐标测量机。 研究问题， 将探讨的是：（1）预测和补偿 使用神经网络映射几何诱导误差 通过训练将工作空间位置转化为工具点误差向量 从精密物理机械测量中获得的数据 神器(2)热致振动的预测和补偿 错误使用少量的战略定位的温度 探测器作为扩展神经网络的额外输入，以及 针对不同的热状态执行所述伪影测量 的机器。(3)设计规则的生成 物理伪影，以确保神经系统的最佳性能 净误差补偿技术(4)调查 使用尺寸检查结果的可能性 制造的部件来更新神经网络的训练集。 网络;从而闭合过程输出质量和 过程输入变量，导致连续过程 改善自动化工厂环境。（五） 调查必要的建筑特征， 混合神经网络机床的计算能力 controller.

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.