Bayesian-based uncertainty-aware tool-wear prediction model in end-milling process of titanium alloy

Author:

Kim GyeonghoORCID,Yang Sang Min,Kim Dong Min,Kim Sinwon,Choi Jae Gyeong,Ku MinjooORCID,Lim SunghoonORCID,Park Hyung WookORCID

Publisher

Elsevier BV

Subject

Software

Reference94 articles.

1. High performance corrosion and wear resistant Ti-6Al-4V alloy by the hybrid treatment method;Narayanan;Appl. Surf. Sci.,2020

2. Improvement of tool life via unique surface modification of a tungsten carbide tool using a large pulsed electron beam in Ti-6Al-4V machining;Yang;J. Manuf. Process.,2022

3. Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system;Rizal;Appl. Soft Comput.,2013

4. G. Johnson, A constitutive model and data for materials subjected to large strains, high strain rates, and high temperatures, in: Proc. 7th Int. Symp. Ballistics, 1983, pp. 541–547.

5. A review of tool wear estimation using theoretical analysis and numerical simulation technologies;Li;Int. J. Refractory Met. Hard Mater.,2012

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