@misc{9235910,
  abstract     = {{Automated sorting and classification of scrap cemented carbide cutting inserts represent a critical technological frontier for sustainable industrial recycling. This master's thesis project successfully develops, evaluates, and methodologically deconstructs an artificial intelligence (AI)-based sensor framework utilizing Acoustic Emission (AE) signals to identify discrete material grades. The preliminary baseline classifiers operating under idealized, unconstrained data achieved an overfitted validation accuracy of 92.5%. Through a systematic suite of feature rankings via ANOVA and Shapley importance, and statistical manifolds mapped by PCA and MD, the interlocking relationships between physical confounding variables and statistical features were successfully decoupled.
Guided by these insights, the final classifier was successfully insulated from cross-geometry feature masking by enforcing a strict geometric consistency constraint, restricting the training domain exclusively to a singular geometry (SEKN). By eliminating cross-variable entanglement, the definitive model achieved a validation accuracy of 86.7% and a robust independent blind testing score of 83.2% on randomized out-of-distribution data. However, blind cross-geometry testing on alternative shapes (LFA and SNUN) resulted in a performance collapse to 41.8%, proving that the material composition fingerprints overlapped under their geometrical conditions. 
Ultimately, this thesis outlines the statistical boundaries of data-driven acoustic sensing, establishing a clear paradigm for future research to deploy advanced domain adaptation algorithms or multi-model ensemble architectures to achieve true geometric invariance in automated industrial recycling.}},
  author       = {{Yu, Sihong}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{AI-Based Insert Classification via Acoustic Emission Signal}},
  year         = {{2026}},
}