Prediction of Blank Thickness Variation in a Deep Drawing Process Using Deep Neural Network

심층 신경망 기반 딥 드로잉 공정 블랭크 두께 변화율 예측

  • 박근태 (부산대학교 항공우주공학과) ;
  • 박지우 (부산대학교 부품소재산학협력연구소) ;
  • 곽민준 (부산대학교 항공우주공학과) ;
  • 강범수 (부산대학교 항공우주공학과)
  • Received : 2019.12.19
  • Accepted : 2020.03.06
  • Published : 2020.04.01


The finite element method has been widely applied in the sheet metal forming process. However, the finite element method is computationally expensive and time consuming. In order to tackle this problem, surrogate modeling methods have been proposed. An artificial neural network (ANN) is one such surrogate model and has been well studied over the past decades. However, when it comes to ANN with two or more layers, so called deep neural networks (DNN), there is distinct a lack of research. We chose to use DNNs our surrogate model to predict the behavior of sheet metal in the deep drawing process. Thickness variation is selected as an output of the DNN in order to evaluate workpiece feasibility. Input variables of the DNN are radius of die, die corner and blank holder force. Finite element analysis was conducted to obtain data for surrogate model construction and testing. Sampling points were determined by full factorial, latin hyper cube and monte carlo methods. We investigated the performance of the DNN according to its structure, number of nodes and number of layers, then it was compared with a radial basis function surrogate model using various sampling methods and numbers. The results show that our DNN could be used as an efficient surrogate model for the deep drawing process.


Supported by : 한국에너지기술평가원, 한국연구재단


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