REFERENCES
1. George E. Totten. Quenching and distortion control. ASM Internaional; 1993.p.341.
2. Canale LDC, Totten GE. Overview of distortion and residual stress due to quench processing. Part I: factors affecting quench distortion. IJMPT 2005;24:4.
3. Zajusz M, Tkacz-śmiech K, Danielewski M. Modeling of vacuum pulse carburizing of steel. Surface and Coatings Technology 2014;258:646-51.
4. Uchida F, Shindo R, Kamada K, Goto S. Simulation of quenching crack generation in low alloy cast steel by using heat-treatment cae software. J Soc Mater Eng Resour Jpn 2004;17:8-12.
5. Todo S, Sueno H, Imataka H. Development of application technology for vacuum carburizing. Shinnittetsu Sumikin giho;2016.p.14–19. Available from: https://www.nipponsteel.com/en/tech/report/nssmc/pdf/116-04.pdf [Last accessed on 20 Apr 2023]
6. Inoue T, Watanabe Y, Okamura K, et al. Metallo-thermo-mechanical simulation of carburized quenching process by several codes - a benchmark project -. KEM 2007;340-341:1061-6.
7. Ju DY, Sahashi M, Omori T, Inoue T. Residual stresses and distortion of a ring in quenching-tempering process based on metallo-thermo-mechanics. Proceeding of 2nd International Conference on Quenching and the Control of Distortion; 1996 Dec 194-257. Available from: https://www.osti.gov/biblio/484269 [Last accessed on 20 Apr 2023].
8. Ju D. Simulation of thermo-mechanical behavior and interfacial stress of metal matrix composite under thermal shock process. Composite Structures 2000;48:113-8.
9. Ju D, Zhang W, Zhang Y. Modeling and experimental verification of martensitic transformation plastic behavior in carbon steel for quenching process. Materials Science and Engineering: A 2006;438-440:246-50.
10. Liu C, Yao K, Liu Z, Xu X. Ju D. -Y. Bainitic transformation kinetics and stress assisted transformation. Materials Science and Technology 2001;17:1229-37.
11. Wołowiec-korecka E. Modeling methods for gas quenching, low-pressure carburizing and low-pressure nitriding. Engineering Structures 2018;177:489-505.
12. Lisjak D, Matijević B. Determination of steel carburizing parameters by using neural network. Materials and Manufacturing Processes 2009;24:772-80.
13. Ju D. Simulation of carburizing of SCr420 by COSMAP and its verification. Quenching deformation simulation 60th Japan Heat Treatment Technology Association Lecture Summary; 2005.
15. Xavier G, Antoine B, Yoshua B. Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics; 2011 Feb PMLR 15:315-323. Available from: http://proceedings.mlr.press/v15/glorot11a [Last accessed on 20 Apr 2023].
16. Vinod N, Geoffrey H. Rectified linear units improve restricted boltzmann machines. ICML’10: Proceedings of the 27th International Conference on International Conference on Machine Learning; 2010 Jun 807–814. Available from: https://dl.acm.org/doi/abs/10.5555/3104322.3104425 [Last accessed on 20 Apr 2023].
17. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature 1986;323:533-6.
