Data-driven prediction of tool wear using Bayesian regularized artificial neural networks-Reference-Cited by-同舟云学术

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Data-driven prediction of tool wear using Bayesian regularized artificial neural networks

Published:2024-10 Issue: Volume:238 Page:115303
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Truong Tam T.,Airao Jay^ORCID,Hojati Faramarz,Ilvig Charlotte F.,Azarhoushang Bahman^ORCID,Karras Panagiotis,Aghababaei Ramin^ORCID

Funder

Ministerium fur Wirtschaft Arbeit und Wohnungsbau Baden-Wurttemberg

Villum Fonden

Publisher

Elsevier BV

Reference64 articles.

1. Analytical modeling of machining forces and friction characteristics in ultrasonic-assisted turning process;Airao;J. Manuf. Sci. Eng.,2022

2. On the Art of Cutting Metals;Taylor,1906

3. A wear relationship for turning, milling and grinding: machining economics;Colding,1959

4. Machining Theory and Practice;Gilbert,1950

5. T. Ko, Y. Koren, Cutting Force Model for Tool Wear Estimation, vol. 1001, Ann Arbor, 1989, p. 48109.

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1. An optimized hybrid finite element analyses - Artificial neural networks technique for estimating in-plane orthotropic mechanical properties of printed circuit boards;Results in Engineering;2024-09

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