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Erschienen in:
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2021 | OriginalPaper | Buchkapitel

A Model for Prediction and Optimization of Flank Wear in End Milling of AISI 316 Stainless Steel

verfasst von : Peter Babatunde Odedeyi, Khaled Abou-El-Hossein

Erschienen in: Proceedings of the 8th International Conference on Fracture, Fatigue and Wear

Verlag: Springer Singapore

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Abstract

Controlling Tool wear is a significant issue related with material removal process in machining. This current study focuses on flank wear end milling of AISI 316 stainless steel using PVD a high-performance TiAlN multilayer coated carbide tool. Three factors (cutting speed, feed and depth of cut) and three-level factorial experiment designs with Box Behnken and statistical analysis of variance were performed in order to investigate the effect of the cutting parameters on the tool and workpiece in terms of flank wear. The results show that flank wear is statistically significant influenced by feed and cutting speed. The model for the experiment explains 75% of the variation in the tool wear and the predicted effect of speed on the tool wear. Feed rate has shown to have highest effect on the tool wear. Accordingly, ANOVA and Multiple Regression were used to develop mathematical model for response, alongside with diverse diagnostic tests were also performed to test the validity and efficacy of the proposed model.

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Metadaten
Titel
A Model for Prediction and Optimization of Flank Wear in End Milling of AISI 316 Stainless Steel
verfasst von
Peter Babatunde Odedeyi
Khaled Abou-El-Hossein
Copyright-Jahr
2021
Verlag
Springer Singapore
Buch
Proceedings of the 8th International Conference on Fracture, Fatigue and Wear

Print ISBN: 978-981-15-9892-0

Electronic ISBN: 978-981-15-9893-7

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-981-15-9893-7_42

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