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Sustainable manufacturing using Zero Defect Manufacturing

A multi-domain mixture density network for tool wear prediction under multiple machining conditions

Gyeongho Kima Department of Industrial Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of KoreaView further author information
,
Sang Min Yangb Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of KoreaView further author information
,
Sinwon Kimb Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of KoreaView further author information
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Do Young Kimc Department of Mechatronics Engineering, Chungnam National University, Daejeon, Republic of KoreaView further author information
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Jae Gyeong Choia Department of Industrial Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of KoreaView further author information
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Hyung Wook Parkb Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of KoreaView further author information
&
Sunghoon Lima Department of Industrial Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea;d Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea;e Industry Intelligentization Institute, Ulsan National Institute of Science and Technology, Ulsan, Republic of KoreaCorrespondence[email protected]
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Received 03 Jul 2023, Accepted 20 Nov 2023, Published online: 06 Dec 2023

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Read on this site (2)

Pengfei Ding, Xianzhen Huang & Yuxiong Li. Time-varying dynamic modeling of micro-milling considering tool wear and the process parameter identification. Mechanics Based Design of Structures and Machines 0:0, pages 1-27.
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Biao Lu & Yumei Luo. A dynamic condition-based maintenance policy for heterogeneous-wearing tools with considering product quality deterioration. International Journal of Production Research 0:0, pages 1-18.
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Articles from other publishers (1)

Gyeongho Kim, Soyeon Park, Jae Gyeong Choi, Sang Min Yang, Hyung Wook Park & Sunghoon Lim. (2024) Developing a data-driven system for grinding process parameter optimization using machine learning and metaheuristic algorithms. CIRP Journal of Manufacturing Science and Technology 51, pages 20-35.
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