Adaptive thermal displacement compensation method based on deep learning
Makoto Fujishima, Koichiro Narimatsu, Naruhiro Irino, Masahiko Mori, Soichi Ibaraki
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.
Keywords: Machine tool; Thermal error; Compensation
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