@article{4963a4b1-cf52-4b94-bfc3-a0e4d9b0b241,
  abstract     = {{<p>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%.</p>}},
  author       = {{Jin, Xiaoliang and Kazemi, Farshad and Lu, Zhenghui and Clare, Adam T. and M'Saoubi, Rachid}},
  issn         = {{0007-8506}},
  keywords     = {{Force; Functionally graded materials; Milling}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{CIRP Annals}},
  title        = {{Engineering consistent machining forces in functionally graded materials}},
  url          = {{http://dx.doi.org/10.1016/j.cirp.2026.04.083}},
  doi          = {{10.1016/j.cirp.2026.04.083}},
  year         = {{2026}},
}