초고강도 판재 다점성형공정에서의 인공신경망을 이용한 2중 곡률 스프링백 예측모델 개발 = A Development of Longitudinal and Transverse Springback Prediction Model Using Artificial Neural Network in Multipoint Dieless Forming of Advanced High Strength Steel

다국어 초록 (Multilingual Abstract)

The need for advanced high strength steel (AHSS) forming technology is increasing as interest in light weight and safe automobiles increases. Multipoint dieless forming (MDF) is a novel sheet metal forming technology that can create any desired longitudinal and transverse curvature in sheet metal. However, since the springback phenomenon becomes larger with high strength metal such as AHSS, predicting the required MDF to produce the exact desired curvature in two directions is more difficult. In this study, a prediction model using artificial neural network (ANN) was developed to predict the springback that occurs during AHSS forming through MDF. In order to verify the validity of model, a fit test was performed and the results were compared with the conventional regression model. The data required for training was obtained through simulation, then further random sample data was created to verify the prediction performance. The predicted results were compared with the simulation results. As a result of this comparison, it was found that the prediction of our ANN based model was more accurate than regression analysis. If a sufficient amount of data is used in training, the ANN model can play a major role in reducing the forming cost of high-strength steels.

참고문헌 (Reference)

1 M. K. Son, Daegu University 2019

2 Y. H. Jeon, Kangwon University 2018

3 박으뜸, "인공신경망을 사용한 섬유금속적층판의 온도에 따른유동응력에 대한 수치해석적 예측" 한국소성가공학회 27 (27): 227-235, 2018

4 우민아, "고속 성형 공정에서 재료의 구성 방정식 파라메터 획득을 위한인공신경망 모델의 적용" 한국소성가공학회 27 (27): 331-338, 2018

5 허성찬, "가변성형공정에서 성형성 향상을 위한 해석 및 실험적 연구" 한국소성가공학회 21 (21): 432-440, 2012

6 X. Glorot, "Understanding the difficulty of training deep feedforward neural networks" 249-256, 2010

7 N. Kardes Sever, "Springback Prediction in Bending of AHSS-DP 780" 40 : 1-10, 2012

8 J. W. Park, "Rapid prediction of longitudinal curvature obtained by flexibly reconfigurable roll forming using response surface methodology" 91 (91): 3371-3384, 2017

9 Z. Y. Cai, "Numerical investigation of multi-point forming process for sheet metal: wrinkling, dimpling and springback" 37 (37): 927-936, 2008

10 K. Mohammadi, "Comparison of regression, ARIMA and ANN models for reservoir inflow forecasting using snowmelt equivalent (a case study of Karaj)" 7 : 17-30, 2005

11 Tang. L, "Advanced high strength steel springback optimization by projectionbasedheuristic global search algorithm" 43 : 426-437, 2013

12 N. Baluch, "Advanced High Strength Steel in Auto Industry: an Overview" 4 (4): 686-689, 2014

13 D. P. Kingma, "Adam: A method for stochastic optimization"