Benchmarking Deep Neural Network Architectures for Machining Tool Anomaly Detection-Reference-Cited by-同舟云学术

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Benchmarking Deep Neural Network Architectures for Machining Tool Anomaly Detection

Published:2020-06-11 Issue:2 Volume:4 Page:20190039
ISSN:2520-6478
Container-title:Smart and Sustainable Manufacturing Systems
language:en
Short-container-title:Smart Sustain. Manuf. Syst.

Author:

Puranik Tejas¹,Gharbi Aroua¹,Bagdatli Burak¹,Fischer Olivia Pinon²,Mavris Dimitri N.¹

Affiliation:

1. Georgia Institute of Technology, Daniel Guggenheim School of Aerospace Engineering, 620 Cherry St., ESM G-10, Atlanta, GA 30332-0150, USA, https://orcid.org/0000-0002-4701-0674 (T.P.), https://orcid.org/0000-0002-0056-8996 (A.G.), https://orcid.org/0000-0002-0349-6406 (B.B.)

2. Georgia Institute of Technology, Daniel Guggenheim School of Aerospace Engineering, 620 Cherry St., ESM G-10, Atlanta, GA 30332-0150, USA (Corresponding author) e-mail: olivia.pinon@asdl.gatech.edu, https://orcid.org/0000-0002-2233-1391

Publisher

ASTM International

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Control and Systems Engineering

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Data-Driven Usage Profiling and Anomaly Detection in Support of Sustainable Machining Processes;2022 IEEE International Conference on Data Mining Workshops (ICDMW);2022-11

2. Digitalisation and servitisation of machine tools in the era of Industry 4.0: a review;International Journal of Production Research;2021-08-30

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