Adaptive thermal displacement compensation method based on deep learning

Abstract Temperature variation of the machine tool structure due to machine internal heat generation or heat exchange with the ambient environment causes thermal deformation. To compensate the deformed machines, various methods have been proposed. However, these methods failed to compensate machines in severe situations such as unexpected temperature change and sensor failure. In this paper, a novel thermal displacement compensation method using deep learning is proposed. In the proposed algorithm, reliability in thermal displacement prediction is evaluated based on deep learning to change the compensation weight adaptively. High-performance thermal displacement compensation result on turning center is presented.

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