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A comprehensive framework for computationally efficient system-level design optimization of machine tools
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DOI：10.1016/j.jmsy.2026.02.005.png)
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Abstract
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• Linear guide joint parameterization enables system-level design optimization. • Spindle nose FRFs reduced to a single energy term for computational efficiency. • FRF energies effectively guide optimization for maintained or improved stability. • Latin hypercube sampling with diversity subsampling minimizes train and test set size. • Allowing small degradations via relaxed constraints improves design exploration.
Keywords:
Machine tools
Optimization
Surrogate modeling
Machine learning
Multi-objective
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