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The research aims to develop a predictive model for thermal errors in CNC machine tool electric spindles under variable conditions.

Research on thermal error modeling of machine tool electric spindle based on SSA-NAR-LSTM

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Research demonstrates improved prediction of thermal errors in machine tool spindles using SSA-NAR-LSTM, indicating superior modeling efficiency.

The research aims to develop a predictive model for thermal errors in CNC machine tool electric spindles under variable conditions.
Established a theoretical heat transfer model for the spindle.
Utilized fuzzy C-means clustering and uncertainty coefficient to identify temperature sensitive points.
Developed a nonlinear autoregressive long short-term memory model optimized by Sparrow search algorithm.
Achieved thermal error prediction accuracy improved by over 50% compared to traditional models.
Predicted thermal errors ranged from 0 to 65 micrometers under complex machining conditions.
Comprehensive predictive residuals for SSA-NAR-LSTM, LSTM, and BP models were 3, 6.8, and 8 micrometers respectively.

Chen et al. (2026) studied this question.

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Research demonstrates improved prediction of thermal errors in machine tool spindles using SSA-NAR-LSTM, indicating superior modeling efficiency.