Adaptive input selection for thermal error compensation models

Abstract The presented method selects optimal inputs for compensation models based on the Thermal Adaptive Learning Control methodology. The number of inputs and the individual inputs for each considered thermal error are automatically adapted. The intelligent combination of k-means clustering and Time Series Cluster Kernel enables the approach to handle time series of thermal error measurements with missing data due to operational reasons. The results show that the adaptive sensor selection approach, tested on a 5-axis-machine tool, significantly increases the robustness of the used compensation model. The productivity loss due to on-machine measurements is reduced by approximately 40 percent.

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