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

Gabsi, Abd El Hedi

doi:10.1007/s12008-023-01505-3

Cited by 2 publications

(1 citation statement)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the literature, feed rate, depth of cut, corner radius and cutting speed were reported as the essential factors of tool wear. [13][14][15] The traditional methods used in these previous studies are a significant challenge for developing CNC machining. A considerable effort was made to determine the influence of the manufacturing conditions in crater tool wear in a vast space of machines, tools and materials.…”

Section: Introductionmentioning

confidence: 99%

An investigation on using artificial intelligence models to predict crater wear of tungsten carbide tool

Gabsi,

Mathlouthi,

Ben Aissa

2024

Tribology - Materials, Surfaces & Interfaces

View full text Add to dashboard Cite

In this study, artificial intelligence (AI) tools were utilised to predict and analyse the progression of crater wear in cutting tools made of tungsten carbide during machining of aluminium 7075 alloy with a CNC lathe. The study investigated the impact of corner radius, feed rate, cutting speeds, and cut depth on the wear of the tools. Thirty experiments were conducted, with 24 used for training 11 independent AI models and the remaining 6 used for testing. This study stands out for its novelty as it pioneers the evaluation of 11 distinct AI models for the prediction of tool wear. With a high level of accuracy and a lower average deviation, the most effective model identified in this study was the gradient boosting model. By integrating AI algorithms into manufacturing processes, the monitoring of tool wear becomes more efficient, leading to reduce experiments, minimise testing costs, predict tool life, prevent failures, and boost productivity.

show abstract

Section: Introductionmentioning

confidence: 99%