A hybrid tool life prediction scheme in cloud architecture

Haw Ching Yang, Yu Yung Li, Min Nan Wu, Fan Tien Cheng

研究成果: Conference contribution

4 引文 斯高帕斯(Scopus)

摘要

This paper presents a cloud service scheme to predict tool wear. When lacking sufficient historical tool wear data for building a data-driven model, predicting tool life is challenging while under various cutting conditions with different tools and machines. On the basis of a hybrid tool wear model with dynamic neural network, this paper proposes a tool life prediction scheme for predicting tool wear by given cutting conditions and relevant tool wear features which extracted from sensing segment data. Experimental results show that the proposed scheme can assist factory users to predict various tool lifetimes well in the cloud-service environment while with the first tool samples for modeling.

原文English
主出版物標題2016 IEEE International Conference on Automation Science and Engineering, CASE 2016
發行者IEEE Computer Society
頁面1160-1165
頁數6
ISBN(電子)9781509024094
DOIs
出版狀態Published - 2016 十一月 14
事件2016 IEEE International Conference on Automation Science and Engineering, CASE 2016 - Fort Worth, United States
持續時間: 2016 八月 212016 八月 24

出版系列

名字IEEE International Conference on Automation Science and Engineering
2016-November
ISSN(列印)2161-8070
ISSN(電子)2161-8089

Other

Other2016 IEEE International Conference on Automation Science and Engineering, CASE 2016
國家United States
城市Fort Worth
期間16-08-2116-08-24

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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