A multi-input parallel convolutional attention network for tool wear monitoring

IF 3.7 3区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

International Journal of Computer Integrated Manufacturing Pub Date : 2023-12-20 DOI:10.1080/0951192x.2023.2294440

Qiang Liu, Dingkun Li, Jing Ma, Xudong Wei, Zhengyan Bai

引用次数: 0

Abstract

Effective tool wear monitoring is of great importance for machining process. With existing deep learning-based methods, the end-to-end model is often combined with sensor data to predict the state ...

查看原文本刊更多论文

用于刀具磨损监测的多输入并行卷积注意力网络

有效监测刀具磨损对加工过程至关重要。在现有的基于深度学习的方法中，端到端模型通常与传感器数据相结合，以预测刀具磨损的状态。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

International Journal of Computer Integrated Manufacturing 工程技术-工程：制造

CiteScore

9.00

自引率

9.80%

发文量

审稿时长

10 months

期刊介绍： International Journal of Computer Integrated Manufacturing (IJCIM) reports new research in theory and applications of computer integrated manufacturing. The scope spans mechanical and manufacturing engineering, software and computer engineering as well as automation and control engineering with a particular focus on today’s data driven manufacturing. Terms such as industry 4.0, intelligent manufacturing, digital manufacturing and cyber-physical manufacturing systems are now used to identify the area of knowledge that IJCIM has supported and shaped in its history of more than 30 years. IJCIM continues to grow and has become a key forum for academics and industrial researchers to exchange information and ideas. In response to this interest, IJCIM is now published monthly, enabling the editors to target topical special issues; topics as diverse as digital twins, transdisciplinary engineering, cloud manufacturing, deep learning for manufacturing, service-oriented architectures, dematerialized manufacturing systems, wireless manufacturing and digital enterprise technologies to name a few.