Journal
MM SCIENCE JOURNAL
Volume 2019, Issue -, Pages 3164-3171Publisher
MM SCIENCE
DOI: 10.17973/MMSJ.2019_11_2019066
Keywords
Thermal behavior; Compensation; Self-optimization; Machine learning
Categories
Funding
- Machine Tool Technologies Research Foundation (MTTRF)
- Swiss Innovation Agency (Innosuisse)
- Swiss National Science Foundation (SNSF)
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This paper presents a prediction and compensation approach for thermal errors of 5-axis machine tools, based on supervised online machine learning. Process-intermittent probing is used to identify and update a thermal autoregressive with exogenous input (ARX) model. The approach is capable of predicting and compensating thermal displacements of the tool center point based on changes in the environmental temperature, load-dependent changes and boundary condition changes and states, like dry or wet machining. The self-optimized machine tool shows very stable long-term behavior under drastically varying machining and boundary conditions. The implementation is validated on a set of thermal test pieces. The test pieces show that the major share of thermal workpiece errors are reduced by the thermal adaptive learning control.
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