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- 저자 : 유현정(Hyeonjeong You) (검색결과 6 건)
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머신러닝을 이용한 레이저 용접부의 모델링 Part Ⅱ: 고강도강 겹치기 레이저용접부의 형상 및 기계적 거동
유현정(Hyeonjeong You),강민정(Minjung Kang),이성(Sung Yi),현승균(Soongkeun Hyun),김철희(Cheolhee Kim) 대한용접·접합학회 2021 대한용접·접합학회지 Vol.39 No.1
In accordance with the requirements of lightweight automobiles, the application of high-strength steel sheets to car bodies is continuously increasing. The strength of the laser overlap welds is determined by the strength distribution of weldments and the bead width at the faying surface. In the case of high-strength steel sheets, it is difficult to predict the fracture load and fracture mode during the tensile shear test of the weldment owing to the high strength of the base material, softening of the heat affected zone (HAZ), and small bead width. In this study, we investigated machine learning algorithms, including artificial neural networks, to develop a fracture mode classification model and regression models for joint strength and bead width. Machine learning algorithms have shown excellent performance in predicting mechanical behaviors during tensile shear tests. Among the machine learning regression algorithms, Gaussian process regression showed the best regression ability. The R² values for the bead width and fracture load models were 0.98 and 0.99, respectively. Several machine learning models, including shallow neural networks, have shown perfect estimates for fracture locations.
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저항 점용접부 형상 및 파단 거동에 대한 머신러닝 모델 및 하이퍼파라미터 최적화
유현정(Hyeonjeong You),김철희(Cheolhee Kim) 대한용접·접합학회 2021 대한용접·접합학회지 Vol.39 No.6
The welding process parameters of resistance spot welding are determined by quality indicators, such as nugget diameter, and tensile shear test behavior, such as failure load and location. In this study, deep-learning models were investigated to predict the quality indicators from base materials and process parameter information. For each model, hyperparameters, such as the number of hidden layers, number of nodes in the hidden layer, learning rate of the optimizer, and number of epochs, were optimized based on the model performance. The regression models for nugget diameter and failure load showed coefficients of determination of 0.90 and 0.95, respectively. Two models were developed to classify failure location: a 1-step model that estimates the failure location from the base material information and process parameters, and a 2-step model that estimates the failure location from the base material information and the nugget diameter as predicted by the developed regression model. The classification models for failure location showed similar accuracies of approximately 90%.
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Al/Fe 이종재의 마이크로 마찰교반 맞대기용접 적용성 평가
유현정(Hyeonjeong You),안영남(Youngnam Ahn),이성(Sung Yi),현승균(Soongkeun Hyun),김철희(Cheolhee Kim) 대한용접·접합학회 2021 대한용접·접합학회지 Vol.39 No.3
In the automobile industry, there is an increasing demand for Al/Fe dissimilar metal joining. Friction stir welding (FSW) is an efficient solid-state welding method to achieve high-quality Al/Fe dissimilar metal welding. Here, we reviewed the previous studies on butt FSW of thin Al/Fe sheets and conducted feasibility tests to investigate the applicability of micro FSW with a base material thickness of 1 mm or less. Most of the past literature, except for one study that adopted 1.12 mm-thick specimens, has worked with a base metal thickness of 1.5 mm or more. Selecting appropriate parameters can lead to a weld strength that is more than 90% of the base metal strength. Through feasibility tests on 2 mm-thick specimens, we could derive the welding conditions to obtain sound welds and the required joint strength. An adequate range (0.5-0.75 mm/rev) of advance per revolution was recommended to ensure the weld strength. A feasibility test on 1 mm-thick specimens revealed the possibility of melting of Al base metal during FSW of 1 mm-thin sheets; moreover, a low tool rotation speed was found to be crucial in ensuring the weld joint strength. The maximum weld strength for 1 mm-thick specimens was 200 MPa, which is 117% of the required weld strength.
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유현정(Hyeonjeong You),이태현(Taehyun Lee),강민정(Minjung Kang),김철희(Cheolhee Kim) 대한용접·접합학회 2021 대한용접·접합학회지 Vol.39 No.6
Titanium alloys have high specific strength and excellent high-temperature properties. However, Ti alloys have limited weldability with other metals due to the formation of brittle intermetallic compounds. Moreover, when steel is a counterpart of dissimilar metal joining, the soundness of weld is hardly achieved due to weld defects. Numerous studies have been conducted to achieve joint strength by minimizing the effects of Fe-Ti intermetallic compounds. In most studies, pure titanium or Ti-6Al-4V alloy were selected as the Ti base metal, whereas stainless steel, low carbon steel, or alloy steel were selected as the steel base metal. To date, joining processes such as diffusion bonding, brazing, fusion welding, and solid-state joining have been investigated. In this study, the characteristics of each of these joining processes were reviewed. More specifically, the formation of intermetallic compounds was analyzed when Ti alloy and steel were directly joined without using interlayer materials.
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고출력의 레이저를 활용한 초고강도 TRIP강의 레이저 용접 및 열처리 특성에 관한 연구
남상우(Sangwoo Nam),유현정(Hyeonjeong You),이형원(Hyung Won Lee),김영민(Yonung-Min Kim) 대한용접·접합학회 2021 대한용접학회 특별강연 및 학술발표대회 개요집 Vol.2021 No.5
최근 자동차 업계에서는 환경 및 충돌법규 강화에 대응하여 고안전 경량차체를 개발하고 있으며, GIGA급 초고강도 강판 적용 비율을 지속적으로 증가시키고 있다. 자동차용 소재는 안전성 및 내구성을 확보하기 위해 높은 비강도뿐만 아니라 성형성을 위한 높은 연성을 동시에 확보해야 한다. TRIP강은 준안정상인 잔류오스테나이트에 응력을 가하면 변형 중에 마르텐사이트 변태가 일어나는 변태유기소성 현상에 의하여 우수한 강도 및 연성을 동시에 나타내는 소재이다. 또한, 레이저는 고밀도의 집속된 열에너지를 열원으로 하는 기술로서, 타 용접기술에 비해 속도가 빠르고 비접촉으로 사용이 가능하여 생산성이 높다. 따라서, 다양한 초고강도강에 대하여 최근 레이저를 활용한 용접 및 열처리 기술이 기대되고 있으나, 초고강도 TRIP강에 대한 레이저 기술은 체계적인 연구가 부족한 실정이다. 따라서, 본 연구에서는 1.0 - 1.2 GPa의 TRIP강과 3 kW 급의 고출력 레이저를 활용하여 다양한 초점 거리, 용접 속도 및 출력에 대하여 Bead on plate 방식으로 실험을 진행하였다. 경도 및 강도를 측정하였으며, 인장 시험에서 레이저 공정 전후의 에너지 흡수를 계산하여 논의하였다. 또한, 파단면 및 열영향부의 미세조직을 관찰 및 분석하였다. 고출력 레이저를 통하여 용접 뿐만 아니라 국부연화를 통한 인성 향상이 가능함을 확인하였다. 따라서, 연구 결과는 초고강도 TRIP강의 추가적인 성형성 확보로 부품의 설계자유도를 향상시켜 차체 경량화에 기여할 것으로 판단된다.
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머신 러닝을 이용한 레이저 용접의 용입깊이 모델링 및 공정변수 맵
고범수(Bum-su Go),유현정(Hyeonjeong You),방희선(Hee-seon Bang),김철희(Cheolhee Kim) 대한용접·접합학회 2021 대한용접·접합학회지 Vol.39 No.4
Penetration control is an important factor in determining the weld quality in keyhole mode laser welding, which enables deep penetration. In this study, machine learning models and neural network models were developed by using 380 published welding data which were constructed for steel base metals under the following welding conditions: a laser power of 0.3-16.7 kW, a welding speed of 0.3-20.0 m/min, and a bead diameter of 0.05-0.78 mm. A machine learning model SVM (supported vector machine) could accurately predict the penetration depth with a coefficient of determination, R² of 0.95. A shallow neural network model with five nodes in only one hidden layer was developed with a slightly improved accuracy with R² of 0.98. It was confirmed that neither model was overfitted, and process parameters (welding speed and beam diameter) maps with penetration depth contours were provided for a laser power of 2-8 kW.
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