1 |
M.Hassoun, Fundamentals of artificial networks, 1995, MIT press
|
2 |
www.scipy.org
|
3 |
https://www.tensorflow.org
|
4 |
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.
DOI
|
5 |
S. H. Lee, D. C. Kang, C. Lee, M. J. Kang, Structural Design of Artificial Neural Network using DOE, 1996, Proc. of spring Conf. on Korean Soc. Prec. Eng., pp. 536~540.
|
6 |
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.
DOI
|
7 |
https://pytorch.org
|
8 |
https://www.python.org/
|
9 |
www.numpy.org
|
10 |
www.sympy.org
|
11 |
https:// caffe.berkeleyvision.org/
|
12 |
H. B. Demuth, M. H. Beale, O. De Jess, & M. T. Hagan, 2014, Neural network design (2nd Edition. Martin Hagan.
|
13 |
D. H. Kim, D. J. Kim, B. M. Kim, J. C. Choi, 1997, Process Design of Multi-Step Drawing using Artificial Neural Network, Conf. on Trans. Mater. Process, pp. 144-147
|
14 |
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.
DOI
|
15 |
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.
DOI
|
16 |
F. Rosenblatt, A probabilistic model for information storage and organization in the brain, 2000, Psychological Review, Vol. 65, No. 3, pp. 383~408.
|
17 |
Ian J. Goodfellow, Yoshua Bengio and Aaron Couville, Deep learning, 2015, MIT Press
|
18 |
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.
DOI
|
19 |
A. J. Hong, K. D. Cheol, L. C. Joo, B. M. Kim, 2008, Springback Compensation of Sheet Metal Bending Process Based on DOE & ANN, Trans. Kor. Soc. Mech. Eng., Vol. 32, No. 11, pp. 990~996.
DOI
|
20 |
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.
DOI
|
21 |
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.
DOI
|
22 |
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.
DOI
|
23 |
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.
DOI
|
24 |
https://cs230.stanford.edu/files/C1M3.pdf
|
25 |
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.
DOI
|