Prediction of crater tool wear using artificial intelligence models in 7075 Al alloy machining

This paper reports how artificial intelligence (AI) tools are used to predict the evolution of crater tool wear. The choice of using AI methods for predicting tool wear in this study is driven by the inherent capabilities of AI algorithms to handle complex and non-linear relationships within the dat...

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Veröffentlicht in:	International journal on interactive design and manufacturing 2024-12, Vol.18 (10), p.7381-7390
1. Verfasser:	Gabsi, Abd El Hedi
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Alloys Aluminum base alloys Artificial intelligence CAE) and Design Carbide tools Computer-Aided Engineering (CAD Cutting parameters Cutting speed Cutting tools Cutting wear Deviation Electronics and Microelectronics Engineering Engineering Design Evolutionary algorithms Experiments Feed rate Industrial Design Instrumentation Machine learning Machining Manufacturing Mechanical Engineering Original Paper Performance indices Physical properties Predictions Process parameters Random variables Regression analysis Tool wear Tungsten carbide Wear rate
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