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

Kim, Gyeongho , Yang, Sang Min , Kim, Dong Min , Kim, Sinwon , Choi, Jae Gyeong , Ku, Minjoo , Lim, Sunghoon , Park, Hyung Wook

Article Issue Date2023-11 View33
Deep Learning-based Tool Wear Prediction under Multiple Machining Conditions

Kim, Gyeongho , Yang, Sang Min , Kim, Sin Won , Kim, Do Young , Choi, Jae Gyeong , Park, Hyung Wook , Lim, Sunghoon

Conference Paper Issue Date2023-07-05 View22
Developing a data-driven system for grinding process parameter optimization using machine learning and metaheuristic algorithms File

Kim, Gyeongho , Park, Soyeon , Choi, Jae Gyeong , Yang, Sang Min , Park, Hyung Wook , Lim, Sunghoon

Article Issue Date2024-07 View0
Improvement of tool life via unique surface modification of a tungsten carbide tool using a large pulsed electron beam in Ti-6Al-4V machining File

Yang, Sang Min , Choe, Joon-Hyeok , Kim, Jisoo , Park, Hyung Wook , Kim, Do Young

Article Issue Date2022-11 View9
Tool wear evolution and chip formation of the Ti-6Al-4V end milling under cryogenic cooling and minimum quantity lubrication conditions File

Joo, Jihun , Kim, Jisoo , Yang, Sang Min , Park, Hyung Wook , Kim, Do Young

Article Issue Date2024-01 View19
Tool Wear Prediction in the End Milling Process of Ti-6Al-4V using Bayesian Learning

Kim, Gyeongho , Yang, Sang Min , Kim, Sinwon , Kim, Dong Min , Lim, Sunghoon , Park, Hyung Wook

Conference Paper Issue Date2022-12-18 View6
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