A multi-domain mixture density network for tool wear prediction under multiple machining conditions-Reference-Cited by-同舟云学术

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

Published:2023-12-06 Issue: Volume: Page:1-20
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Kim Gyeongho¹,Yang Sang Min²,Kim Sinwon²,Kim Do Young³,Choi Jae Gyeong¹,Park Hyung Wook²,Lim Sunghoon¹⁴⁵

Affiliation:

1. Department of Industrial Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea

2. Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea

3. Department of Mechatronics Engineering, Chungnam National University, Daejeon, Republic of Korea

4. Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea

5. Industry Intelligentization Institute, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea

Funder

Ministry of Trade, Industry and Energy (MOTIE) of Korea

National Research Foundation of Korea

Korea government

Institute of Information & communications Technology Planning & Evaluation

Artificial Intelligence Graduate School Program

Ministry of Science and ICT

Ministry of Science, ICT

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Strategy and Management

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2023.2289076

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