Numerical analysis of fault detection in additive manufacturing based on sustainable automation techniques-Reference-Cited by-同舟云学术

Numerical analysis of fault detection in additive manufacturing based on sustainable automation techniques

Published:2023-11-18 Issue: Volume: Page:
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Kong Lin,Wang Zhe,He Qi

Funder

Hierarchical Teaching System oriented by vocational post Ability Cultivation

University-level ideological and Political Demonstration Cultivation

Publisher

Springer Science and Business Media LLC

Subject

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

Link

https://link.springer.com/content/pdf/10.1007/s00170-023-12624-2.pdf

Reference20 articles.

1. Harsha PS, Chohan JS, Gill HS (2022) "Machine learning based comparative analysis and prediction for sustainable additive manufacturing," 2022 Seventh international conference on parallel, distributed and grid computing (PDGC), Solan, Himachal Pradesh, India, pp. 558–563. https://doi.org/10.1109/PDGC56933.2022.10053152

2. Qin J, Hu F, Liu Y, Witherell P, Wang CC, Rosen DW, Tang Q (2022) Research and application of machine learning for additive manufacturing. Addit Manuf 52:102691

3. Li W, Zhang H, Wang G, Xiong G, Zhao M, Li G, Li R (2023) Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing. Robot Comput Integr Manuf 80:102470

4. Fu Y, Downey AR, Yuan L, Zhang T, Pratt A, Balogun Y (2022) Machine learning algorithms for defect detection in metal laser-based additive manufacturing: a review. J Manuf Process 75:693–710

5. Alassery F, Alhazmi L (2022) Optimal fuzzy attention deep learning enabled rotating machine fault diagnosis for sustainable manufacturing. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-022-10512-9