File Download

There are no files associated with this item.

  • Find it @ UNIST can give you direct access to the published full text of this article. (UNISTARs only)
Related Researcher

임성훈

Lim, Sunghoon
Industrial Intelligence Lab.
Read More

Views & Downloads

Detailed Information

Cited time in webofscience Cited time in scopus
Metadata Downloads

Tool Wear Prediction in the End Milling Process of Ti-6Al-4V using Bayesian Learning

Author(s)
Kim, GyeonghoYang, Sang MinKim, SinwonKim, Dong MinLim, SunghoonPark, Hyung Wook
Issued Date
2022-12-18
URI
https://scholarworks.unist.ac.kr/handle/201301/74912
Citation
2022 International Conference on Advanced Mechatronic Systems
Publisher
Toyama Prefectural University (Toyama Station Campus)

qrcode

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.