Neural Network Error Compensation of Machine Tools and Coordinate Measuring Machines

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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.