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액중 방전 성형과 인공신경망 기법을 활용한 Cowper-Symonds 구성 방정식의 변형률 속도 파라메터 역추정
변한비,김정 한국소성∙가공학회 2022 소성가공 : 한국소성가공학회지 Vol.31 No.2
Numerical analysis and dynamic material properties are required to analyze the behavior of workpiece during an electrohydraulic forming (EHF) process. In this study, EHF experiments were conducted under three conditions (6, 7, 8 kV). Dynamic material properties of Al 5052-H34 were inversely estimated through an ANN (Artificial Neural Network) model constructed based on LS-Dyna analysis results. Parameters of Cowper-Symonds constitutive equation, C and p, were used to implement dynamic material properties. By comparing experimental results of three conditions with ANN model results, optimized parameters were obtained. To determine the reliability of the derived parameters, experimental results, LS-Dyna analysis results, and ANN results of three conditions were compared using MSE and SMAPE. Valid parameters were obtained because values of indicators were within confidence intervals.
유한요소해석을 통한 전자기 성형과 딥 드로잉 공정의 성형성 비교 분석
임미래,변한비,송윤준,박정수,김정 한국자동차공학회 2022 한국 자동차공학회논문집 Vol.30 No.8
Due to recent environmental regulations in the automobile industry and the expansion of the electric⋅hybrid vehicle market, vehicle weight reduction technology is emerging as a key factor influencing market competitiveness. Demand for aluminum alloy instead of steel is increasing driven by the demand to reduce vehicle weight. However, due to the low formability of aluminum alloys, the application area in the automotive industry is limited. To improve this limitation, electromagnetic forming(EMF) has been proposed. EMF is a high-speed forming technology that applies a strong electromagnetic field to a metal workpiece to form the workpiece at high speed and within a short time. In this paper, it was confirmed that formability was improved through electromagnetic forming using finite element analysis.
SMC 복합재료의 손상 파라미터 역추정을 위한 낙중 시험의 유한요소 모델
이상철(Sang Cheol Lee),변한비(Han Bi Byun),김정(Jeong Kim) 대한기계학회 2022 大韓機械學會論文集A Vol.46 No.2
SMC 복합재료는 자동차 산업에서 경량화를 위해 주로 사용되고 있다. 차량의 부품에 소재를 적용하기 위해서는 유한요소 해석을 통해 재료의 충돌 거동을 확인하는 것이 중요하다. 본 연구에서는 인공신경망 기법을 이용하여 SMC 복합재료의 손상 파라미터를 역추정하기 위한 낙중 시험의 유한요소 모델을 획득하였다. 두 가지 유한요소 모델을 선정한 후 LS-DYNA의 손상 파라미터에 따른 결과를 이용하여 두 개의 인공신경망 모델을 구성하였다. 낙중 시험 결과와 비교하여 두 유한요소 모델에 대한 최적의 파라미터를 역추정하고 신뢰성을 검증하였다. 그 결과, 치구와 클램프를 고려한 유한요소 모델이 실험 결과와 가장 작은 오차를 보였다. Sheet molding compound (SMC) is used in the automotive industry to reduce weight. To apply a material on certain parts of an automobile, it is vital to research the impact behavior of the materials using the finite element (FE) analysis. In this study, using artificial neural networks (ANN), an attempt is made to find a FE model for the drop weight test that can estimate inverse parameters in the damage model of a SMC. After selecting two FE models, two ANN models were constructed by using the results from the damage parameters in the LS-DYNA. Comparing with the results of the experiment, the parameters were optimized and verified for the two FE models. It was observed that the FE model which used a fixture and clamp showed the least error.