Hybrid physics data-driven model-based fusion framework for machining tool wear prediction

Accurate tool wear prediction is of great significance to improve production efficiency, ensure product quality and reduce machining cost. This paper proposes a hybrid physics data-driven model-based fusion framework for tool wear prediction to improve low prediction accuracy of physical model and p...

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Veröffentlicht in:	International journal of advanced manufacturing technology 2024-05, Vol.132 (3-4), p.1481-1496
Hauptverfasser:	Gao, Tianhong, Zhu, Haiping, Wu, Jun, Lu, Zhiqiang, Zhang, Shaowen
Format:	Artikel
Sprache:	eng
Schlagworte:	CAE) and Design Computer-Aided Engineering (CAD Data mining Engineering Industrial and Production Engineering Machining Mechanical Engineering Media Management Model accuracy Original Article Parameter estimation Physics Root-mean-square errors Tool wear
Online-Zugang:	Volltext
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