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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 millin
