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

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2020 by IJETT Journal
Volume-68 Issue-10
Year of Publication : 2020
Authors : Pradeep Kumar Singh
DOI :  10.14445/22315381/IJETT-V68I10P204


MLA Style: Pradeep Kumar Singh "Virtual Metallurgy: Alloy Formation And Prediction of Their Properties Through Artificial Neural Network" International Journal of Engineering Trends and Technology 68.10(2020):28-32. 

APA Style:Pradeep Kumar Singh. Virtual Metallurgy: Alloy Formation And Prediction of Their Properties Through Artificial Neural Network  International Journal of Engineering Trends and Technology, 68(10),28-32.

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.


[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 (
[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.

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