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S. K. Lee, S. M. Kim, S. B. Lee, B. M. Kim, 2010, Optimization of Process Variables of Shape Drawing for Steering Spline Shaft, Trans. Mater. Process, Vol. 19, No. 2, pp. 132-137.
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J. I. Choi, J. M. Lee, S. H. Baek, B. M. Kim, D. H. Kim, 2015, The Shoe Mold Design for Korea Standard Using Artificial Neural Network, Trans. Mater. Process, Vol. 24, No. 3, pp. 167-175.
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V. C. Do, Y. S. Kim, Effect of Hole Lancing on the Forming Characteristic of Single Point Incremental Forming, 2017, Proc. Eng., Vol. 184, pp. 35~42.
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Y. S. Kim, J. J. Kim, 2021, Basics of artificial neural network and its applications to plastic forming process analyses I, Trans. Mater. Processing, Vol. 30, No. 4, pp.
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S. H. Oh, X. Xiao, Y. S. Kim, 2021, Modeling of AA5052 Sheet Incremental Sheet Forming Process Using RSM-BPNN and Multi-optimization Using Genetic Algorithms, Trans. Mater. Process, Vol. 30, No. 3, pp. 125-133.
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S. Santurkar, D. Tsipras, A. Ilyas, A. Madry, 2018, How does batch normalization help optimization?, In Proceedings of the 32nd international conference on neural information processing systems, pp. 2488-2498.
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D. T. Nguyen, Y. S. Kim, D. W. Jung, Formability Predictions of Deep Drawing Process for Aluminum Alloy A1100-O Sheets by Using Combination FEM with ANN, 2012, Advanced Mat. Research, Vol. 472, pp. 781~786.
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M. A. Woo, S. M. Lee, K. H. Lee, W. J. Song, J. Kim, 2018, Application of an Artificial Neural Network Model to Obtain Constitutive Equation Parameters of Materials in High Speed Forming Process, Trans. Mater. Process, Vol. 27, No. 6, pp. 331-338.
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D. C. Yang, J. H. Lee, K. H. Yoon, J. S. Kim, 2020, A Study on the Prediction of Optimized Injection Molding Condition using Artificial Neural Network (ANN), Trans. Mater. Process, Vol. 29, No. 4, pp. 218-228.
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E. S. Noh, S. R. Yi, M. S. Kim, S. M. Hong, 2020, Identification of Bolt Coating Defects Using CNN and Grad-CAM, Trans. Kor. Soc. Mech. Eng. A, Vol. 44, No. 11, pp. 835-842.
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https://www.python.org/
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D. V. Cuong, D. C. Ahn, Y. S. Kim, Formability and effect of hole bridge in the single point incremental forming, 2017, Int. J. Pre. Eng. Mat., vol. 18, pp. 453-460
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M. J. Kwak, J. W. Park, K. T. Park, B. S. Kang., 2020, A Development of Longitudinal and Transverse Springback Prediction Model Using Artificial Neural Network in Multipoint Dieless Forming of Advanced High Strength Steel, Trans. Mater. Process, Vol. 29, No. 2, pp. 76-88.
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M. M. Waldrop, 2019, News Feature: What are the limits of deep learning?, PNAS, Vol. 116, No. 4, pp. 1074-1077.
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N. S. Keskar, D. Mudigere, J. Nocedal, M. Smelyanskiy, P. T. P. Tang, 2017, On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima, arXiv preprint arXiv:1609.04836.
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Y. J. Lee, 2008, Optimization of weights for good generalization performance of neural networks, Thesis in KAIST.
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M. J. Kwak, J. W. Park, K. T. Park, B. S. Kang, 2020, A Development of Optimal Design Model for Initial Blank Shape Using Artificial Neural Network in Rectangular Case Forming with Large Aspect Ratio, Trans. Mater. Process, Vol. 29, No. 5, pp. 272-281.
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S. C. Ma, E. P. Kwon, S. D. Moon, Y. Choi, 2020, Prediction of Springback after V-Bending of High-Strength Steel Sheets Using Artificial Neural Networks, Trans. Mater. Process, Vol. 29, No. 6, pp. 338~346.
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