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Ki, Hyungson
Laser Processing & Multiphysics Systems Laboratory
Research Interests
  • Laser materials processing
  • Laser material interaction

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Deep-Learning-Based Predictive Architectures for Self-Piercing Riveting Process

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dc.contributor.author Oh, Sehyeok ko
dc.contributor.author Kim, Hyun Kyung ko
dc.contributor.author Jeong, Taek-Eon ko
dc.contributor.author Kam, Dong-Hyuck ko
dc.contributor.author Ki, Hyungson ko
dc.date.available 2020-07-30T07:51:46Z -
dc.date.created 2020-07-29 ko
dc.date.issued 2020-06 ko
dc.identifier.citation IEEE ACCESS, v.8, pp.116254 - 116267 ko
dc.identifier.issn 2169-3536 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/47386 -
dc.description.abstract Deep-learning architectures were developed for the self-piercing riveting (SPR) process to predict the cross-sectional shape from the scalar input of the punch force. Traditionally, the SPR process is studied using a physic-based approach, including finite element modeling, but in this study, a data-driven approach consisting of two supervised deep-learning models was proposed. The first model was used for data transformation from an optical microscopic image to a material segmentation map, which characterizes the shape and location of the two sheets and the rivet by applying a convolutional neural network (CNN)-based deep-learning structure. To validate the developed models, two types of sheet combinations were tested, namely, carbon-fiber-reinforced plastic (CFRP) and galvanized dual-phase steel (GA590DP) sheets, and steel alloy (SPFC590DP) and aluminum alloy (Al5052-H32) sheets. The transformation was performed with a mean intersection-over-union of 98.50% and a mean pixel accuracy of 99.78%. The next model, which was a novel generative model based on a CNN and conditional generative adversarial network with residual blocks, was then trained to predict the cross-sectional shape from the input punch force. The predicted cross-sectional shapes were compared with the experimental results of SPR. The overall accuracy was 94.20% for CFRP-GA590DP and 96.31% for SPFC590DP-Al5052, with respect to three key geometrical indexes, namely, rivet head height, interlock length, and bottom thickness. ko
dc.language 영어 ko
dc.publisher IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC ko
dc.title Deep-Learning-Based Predictive Architectures for Self-Piercing Riveting Process ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-85087833827 ko
dc.identifier.wosid 000547512100001 ko
dc.type.rims ART ko
dc.identifier.doi 10.1109/ACCESS.2020.3004337 ko
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