Virtual Metallurgy: Alloy Formation And Prediction of Their Properties Through Artificial Neural Network

Pradeep Kumar Singh

doi:https://doi.org/10.14445/22315381/IJETT-V68I10P204

Research Article | Open Access | Download PDF

Volume 68 | Issue 10 | Year 2020 | Article Id. IJETT-V68I10P204 | DOI : https://doi.org/10.14445/22315381/IJETT-V68I10P204

Virtual Metallurgy: Alloy Formation And Prediction of Their Properties Through Artificial Neural Network

Pradeep Kumar Singh

Citation :

Pradeep Kumar Singh, "Virtual Metallurgy: Alloy Formation And Prediction of Their Properties Through Artificial Neural Network," International Journal of Engineering Trends and Technology (IJETT), vol. 68, no. 10, pp. 28-32, 2020. Crossref, https://doi.org/10.14445/22315381/IJETT-V68I10P204

Abstract

In the present paper a novel concept of metallurgy viz., ‘virtual–metallurgy’ is introduced. With virtual metallurgy, it is possible to make a large variety of potential alloys and its properties prediction could be done through Artificial Neural Network (ANN). Using the virtual-metallurgy concept, a few new alloys are found with interesting and improved properties viz. [i] Braonze (combination of Brass & Bronze) and [ii] Hc-Hss tool material (combination of high carbon steel & high speed steel). Properties of Brass & Bronze and that of High carbon steel & High speed steel are used as training data to train the ANN.
Virtual-metallurgy in broader sense, is virtual-chemistry, and it can find wide-ranging applications towards virtual-testing & development of composite materials, polymers, medicines etc. Virtual-metallurgy can be considered as eco-friendly metallurgical manufacturing process & testing, that avoids unnecessary hit and trial experiments for the alloy making, only the better ones predicted by ANN can actually be made.

Keywords

Virtual metallurgy, Artificial Neural Network, Alloys, Virtual Chemistry.

References

[1] Rao and Rao. “Neural Network and fuzzy logic”, BPH, 1995.
[2] M.H. Hassoun, “Artificial Neural Network”, Prentice hall, 1998
[3] L. Shigaki and H. Narazaki, “A Machine Learning Approach for a Sintering Process using Neural Network”, Proc 14th Int. Cont. in Production Research (ICPR), Vol-II, Osaka, Aug` 1997.
[4] R.E. Loge´, & Y.B. Chastel “Coupling the thermal and mechanical fields to metallurgical evolutions within a finite element description of a forming process”, Comput. Methods Appl. Mech. Engrg, 195 (2006) 6843–6857.
[5] Mike D. Adams “Towards a virtual metallurgical plant 2: Application of mineralogical data”, Minerals Engineering, 20 (2007) 472–479.
[6] A Kumar, K Sharma, & AR Dixit “Carbon nanotube-and graphene-reinforced multiphase polymeric composites: review on their properties and applications”, Journal of Materials Science, 55 (2020) 2682–2724.
[7] MK Shukla, & K Sharma “Effect of carbon nanofillers on the mechanical and interfacial properties of epoxy based nanocomposites”: A review, Polymer Science, Series A, 61 (4) (2019) 439-460.
[8] A Kumar, K Sharma, & AR Dixit “A review on the mechanical and thermal properties of graphene and graphene-based polymer nanocomposites: understanding of modelling and MD simulation”, Molecular Simulation, 46 (2) (2020) 136-154
[9] IWONA POKORSKA “Modeling of powder metallurgy processes”, Advanced Powder Technol., 18 (5) (2007) 503–539.
[10] PK Singh, K Sharma, A Kumar, & M Shukla “Effects of functionalization on the mechanical properties of multiwalled carbon nanotubes: A molecular dynamics approach”, Journal of Composite Materials, 51 (5) (2017) 671-680.
[11] PK Singh, & K Sharma “Mechanical and Viscoelastic Properties of In-situ Amine Functionalized Multiple Layer Grpahene/epoxy Nanocomposites”, Current Nanoscience, 14 (3) (2018) 252-262.
[12] Singh PK, & Sharma K Molecular “Dynamics Simulation of Glass Transition Behaviour of Polymer based Nanocomposites”, Journal of Scientific & Industrial Research, 77 (10) (2018) 592-595.
[13] A Yadav, A Kumar, PK Singh, & K Sharma “Glass transition temperature of functionalized graphene epoxy composites using molecular dynamics simulation”, Integrated Ferroelectrics, 186 (1) (2018) 106-114.
[14] M. Bambacha, J. Buhl, T. Hart-Rawung, M. Lechner, & M. Merklein “Towards virtual deformation dilatometry for the design of hot stamping process”, Procedia Engineering, 207 (2017) 1821–1826.
[15] A. Erulanova, & A. Muslimova “Development of Conceptual Model for Virtualization of Bulk Materials Dispensing Technological Process”, Procedia Engineering, 206 (2017) 752–759.
6] Yan-Lin He, Ping-Jiang Wang, Ming-Qing Zhang, Qun-Xiong Zhu, & Yuan Xu “A novel and effective nonlinear interpolation virtual sample generation method for enhancing energy prediction and analysis on small data problem: A case study of Ethylene industry”, Energy, 147 (2018) 418-427.
[17] O.M. Ivasishin, P.E. Markovsky, D.G. Savvakin, O.O. Stasiuk, M. Norouzi Rad, & S.V. Prikhodko “Multi-layered structures of Ti-6Al-4V alloy and TiC and TiB composites on its base fabricated using blended elemental powder metallurgy”, Journal of Materials Processing Tech., 269 (2019) 172–181.
[18] Ruey Kei Chiu, Renee Y. Chen, Shin-An Wang, Yen-Chun Chang, & Li-Chien Chen “Intelligent Systems Developed for the Early Detection of Chronic Kidney Disease”, Advances in Artificial Neural Systems, (2013) Article ID 539570 (http://dx.doi.org/10.1155/2013/539570).
[19] Sagar K G, Suresh P M, "Finite Element Analysis for Material and Geometrical Nonlinearity in Powder Compact Components" IJETT International Journal of Mechanical Engineering 5.2 (2018): 1-4.