Deep learning approaches for force feedback based void defect detection in friction stir welding-Reference-Cited by-同舟云学术

Deep learning approaches for force feedback based void defect detection in friction stir welding

Published:2022-06 Issue: Volume:5 Page:100087
ISSN:2666-3309
Container-title:Journal of Advanced Joining Processes
language:en
Short-container-title:Journal of Advanced Joining Processes

Author:

Rabe P.,Schiebahn A.,Reisgen U.

Funder

Deutsche Forschungsgemeinschaft

Publisher

Elsevier BV

Subject

Mechanical Engineering,Mechanics of Materials,Engineering (miscellaneous),Chemical Engineering (miscellaneous)

Reference43 articles.

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2. Friction stir welding of aluminum to copper–an overview;Sharma;Trans. Nonferrous Met. Soc. China,2017

3. Robot-based friction stir welding for e-mobility and general applications;Richter;Biul. Inst. Spaw.,2017

4. Investigation of nondestructive testing methods for friction stir welding;Taheri;Metals,2019

5. A review on sensor based monitoring and control of friction stir welding process and a roadmap to Industry 4.0;Mishra;J. Manuf. Process.,2018

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1. A Review of Optimization and Measurement Techniques of the Friction Stir Welding (FSW) Process;Journal of Manufacturing and Materials Processing;2023-10-07

2. The response of force characteristic to weld-forming process in friction stir welding assisted by machine learning;International Journal of Mechanical Sciences;2023-09

3. Role of expert systems to optimize the friction stir welding process parameters using numerical modelling: a review;International Journal on Interactive Design and Manufacturing (IJIDeM);2023-07-18

4. Analysis of friction stir welds using numerical modelling approach: a comprehensive review;International Journal on Interactive Design and Manufacturing (IJIDeM);2023-05-06

5. In Line Nondestructive Testing for Sheet Metal Friction Stir Welding;SAE Technical Paper Series;2023-04-11