Developing a DSS for Enhancing Weldment Defect Detection, Classification, and Remediation Using HDR Images and Adaptive MDCBNet Neural Network
Author:
Publisher
Springer Science and Business Media LLC
Subject
Mechanical Engineering,Mechanics of Materials
Link
https://link.springer.com/content/pdf/10.1007/s10921-023-01027-8.pdf
Reference41 articles.
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2. Sadeghian, A., Iqbal, N.: A review on dissimilar laser welding of steel-copper, steel-aluminum, aluminum-copper, and steel-nickel for electric vehicle battery manufacturing. Opt. Laser Technol. 146, 107595 (2022). https://doi.org/10.1016/j.optlastec.2021.107595
3. Sharma, P., et al.: Recent developments in the design, development, and analysis of the influence of external magnetic-field on gas-metal arc welding of non-ferrous alloys: review on optimization of arc-structure to enhance the morphology, and mechanical properties of welded. Heliyon 8(12), e11812 (2022). https://doi.org/10.1016/j.heliyon.2022.e11812
4. Bacioiu, D., Melton, G., Papaelias, M., Shaw, R.: Automated defect classification of aluminium 5083 TIG welding using HDR camera and neural networks. J. Manuf. Process 45(August), 603–613 (2019). https://doi.org/10.1016/j.jmapro.2019.07.020
5. Jolly, M.R., et al.: Review of non-destructive testing (NDT) techniques and their applicability to Thick Walled composites. Procedia CIRP. 38, 129–136 (2015). https://doi.org/10.1016/j.procir.2015.07.043
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