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Engineering consistent machining forces in functionally graded materials
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DOI：10.1016/j.cirp.2026.04.083.png)
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Abstract
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Additive manufacturing enables multi-material functionally graded materials (FGMs) with expanded functionality, yet post-machining remains challenging because flow stress varies with composition. This paper presents a data-efficient, physics-guided Gaussian process (GP) model for predicting milling forces in SS316/IN718 FGMs without repeated force-coefficient identification. A physics-based milling model with mixture-law baselines is corrected using sparse force measurements, while the GP learns the residual as a smooth function of composition and parameters. The model reduces prediction error from 26.6 to 19.2% for feed force and from 19.8 to 10.6% for normal force, while adaptive feed scheduling lowers peak-force variation from ∼50 to ∼8%.
Keywords:
Milling
Force
Functionally graded materials
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