This paper discusses the compensation of tool paths for machining flexible parts. Despite various research published on the topic, machining in practice nowadays remains limited to tool path planning based on only the geometric models of the parts and tools. This is mainly because that tool path compensation methods usually require accurate physical information of the systems and rely on analytical or finite element simulations, which are often not available to the end-users. In regards to this problem, this paper presents data-oriented nonparametric learning methods that require solely the geometric measurements of the trial machined contour(s). The physical parameters of the parts and tools as well as simulations of the machining process are not required. Two algorithms are developed based on Gaussian Process Regression and Artificial Neural Network respectively. Experimental tests are conducted. A plan of further improving the results using an auxiliary real-time vision sensor is also discussed.

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