[1D01-07] Surface Science / Structure (SS3)

4:00 PM - 4:15 PM JST(7:00 AM - 7:15 AM UTC)

[1D06] Interfacial mechanisms of diamond tool wear in iron machining: A machine learning molecular dynamics study

*Bao-Anh Nguyen-Trinh¹, John Isaac Guinto Enriquez¹, Harry Handoko Halim¹, Hiroyuki Ogiwara², Takahiro Yamasaki², Masato Michiuchi², Tamio Oguchi³, Yoshitada Morikawa¹ (1. Graduate School of Engineering, The University of Osaka (Japan), 2. Sumitomo Electric Industries, Ltd (Japan), 3. Center for Spintronics Research Network, The University of Osaka (Japan))

Diamond tools exhibit exceptional hardness and wear resistance but suffer rapid degradation during machining of ferrous materials. In this study, the atomistic mechanisms of diamond tool wear during iron cutting were investigated using machine-learning interatomic potential molecular dynamics (MLIP–MD) simulations. A highly accurate Fe–C potential enabled large-scale simulations for multiple tool orientations. The simulations reproduced experimentally observed orientation-dependent wear trends. Wear initiates at the tool tip, causing tip blunting and formation of an intermediate surface between the rake and flank faces. Subsequently, wear propagates along stepped edge regions through successive carbon removal events. Stable edge structures suppress wear propagation, whereas unstable edges promote accelerated material removal. The results show that wear is governed by the combined effects of edge bond orientation and local Fe flow near the cutting edge. These findings provide atomistic insight into diamond wear during ferrous machining.

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