NEURAL NETWORK APPLICATION FOR FINE-BLANKED EDGE QUALITY IN ROLLED STEEL SHEETS
Issued Date
2023-01-01
Resource Type
ISSN
21862982
Scopus ID
2-s2.0-85184770457
Journal Title
International Journal of GEOMATE
Volume
25
Issue
112
Start Page
107
End Page
114
Rights Holder(s)
SCOPUS
Bibliographic Citation
International Journal of GEOMATE Vol.25 No.112 (2023) , 107-114
Suggested Citation
Nakkhlai T., Chookaew W., Phromjan J., Suvanjumrat C., Rugsaj R. NEURAL NETWORK APPLICATION FOR FINE-BLANKED EDGE QUALITY IN ROLLED STEEL SHEETS. International Journal of GEOMATE Vol.25 No.112 (2023) , 107-114. 114. doi:10.21660/2023.112.G13370 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/97258
Title
NEURAL NETWORK APPLICATION FOR FINE-BLANKED EDGE QUALITY IN ROLLED STEEL SHEETS
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Abstract
The backing plate plays a crucial role in drum brakes, pushing the brake shoe against the drum for effective vehicle braking and road safety. Manufactured from steel sheets, it exhibits properties akin to a composite material, with characteristics varying based on directionality. The backing plate's profile features a continuous series of arcs, making the fine-blanked edge quality contingent on the arc radius. This research delved into fineblanked manufacturing through a combination of experimentation and simulation to discern the impact of the arc radius on edge quality. Employing a scanning electron microscope for edge quality investigation and benchmarking the finite element model against it, the study ensured a comprehensive understanding. The wellaligned finite element model served as input data for training an artificial neural network (ANN), specifically engineered to accurately estimate backing plate edge quality. This ANN is anticipated to be instrumental in designing future steel plate profiles boasting multiple arcs, offering precision in the manufacturing process for enhanced edge quality.