The use of wavelet transform to evaluate the sensitivity of acoustic emission signals attributes to variation of cutting parameters in milling aluminum alloys-Reference-Cited by-同舟云学术

The use of wavelet transform to evaluate the sensitivity of acoustic emission signals attributes to variation of cutting parameters in milling aluminum alloys

Published:2023-03-28 Issue:7-8 Volume:126 Page:3039-3052
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Asadi Reza,Niknam Seyed Ali,Anahid Mohamad Javad,Ituarte Iñigo Flores

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00170-023-11305-4.pdf

Reference46 articles.

1. Asadi R, Yeganefar A, Niknam SA (2019) Optimization and prediction of surface quality and cutting forces in the milling of aluminum alloys using ANFIS and interval type 2 neuro fuzzy network coupled with population-based meta-heuristic learning methods. Int J Adv Manuf Technol 105:2271–2287

2. Sugihara T, Nishimoto Y, Enomoto T (2015) On-machine tool resharpening process for dry machining of aluminum alloys employing LME phenomenon. Precis Eng 40:241–248

3. Guo J, Zhang J, Wang H, Liu K, Kumar AS (2018) Surface quality characterisation of diamond cut V-groove structures made of rapidly solidified aluminium RSA-905. Precis Eng 53:120–133

4. Gomes MC, Brito LC, da Silva MB, Duarte MAV (2021) Tool wear monitoring in micromilling using support vector machine with vibration and sound sensors. Precis Eng 67:137–151

5. Twardowski P, Tabaszewski M, Wiciak–Pikuła M, Felusiak-Czyryca A (2021) Identification of tool wear using acoustic emission signal and machine learning methods. Precis Eng 72:738–744

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