Prediction of Hardness of Copper-based Nanocomposites Fabricated by Ball-milling using Artificial Neural Network

Document Type : Original Article

Authors

1 Department of Materials & Metallurgy, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

2 College of Science and Engineering, James Cook University, Townsville, Queensland, Australia

3 Department of Computer Science and Automation, Technical University of Ilmenau, Germany

Abstract

Copper-based alloys are one of the most popular materials in the power distribution, welding industry, hydraulic equipment, industrial machinery, etc. Among different methods for the fabrication of Cu alloys, mechanical alloying (MA) is the major approach due to the fact that this approach is simple, inexpensive, suitable for mass production, and has a high capacity for homogeneous distribution of the second phase. However, the prediction of the hardness of products is very difficult in MA because of a lot of effective parameters. In this work, we designed a feed-forward back propagation neural network (FFBPNN) to predict the hardness of copper-based nanocomposites. First, some of the most common nanocomposites of copper including Cu-Al, Cu-Al2O3, Cu-Cr, and Cu-Ti were synthesized by mechanical alloying of copper at varying weight percentages (1, 3, and 6). Next, the alloyed powders were compacted by a cold press (12 tons) and subjected to heat treatment at 650˚C. Then, the strength of the alloys was measured by the Vickers microscopy test. Finally, to anticipate the micro-hardness of Cu nanocomposites, the significant variables in the ball milling process including hardness, size, and volume of the reinforcement material, vial speed, the ball-to-powder-weight-ratio (BPR), and milling time; were determined as the inputs, and hardness of nanocomposite was assumed as an output of the artificial neural network (ANN). For training the ANN, many different ANN architectures have been employed and the optimal structure of the model was obtained by regression of 0.9914. The network was designed with two hidden layers. The first and second hidden layer includes 12 and 8 neurons, respectively. The comparison between the predicted results of the network and the experimental values showed that the proposed model with a root mean square error (RMSE) of 3.7 % can predict the micro-hardness of the nanocomposites. 

Keywords

Main Subjects

References

  1. Saindane, U., Soni, S. and Menghani, J., "Dry sliding behavior of carbon-based brake pad materials", International Journal of Engineering, Vol. 34, No. 11, (2021), 2517-2524. doi: 10.5829/IJE.2021.34.11B.14.
  2. Saindane, U., Soni, S. and Menghani, J., "Performance evaluation of brake pads developed using two different manufacturing methods", Materialwissenschaft und Werkstofftechnik, Vol. 54, No. 2, (2023), 186-195. https://doi.org/10.1002/mawe.202100407
  3. Asgharian Marzabad, M., Jafari, B. and Norouzi, P., "Determination of riboflavin by nanocomposite modified carbon paste electrode in biological fluids using fast fourier transform square wave voltammetry", International Journal of Engineering, Transactions C: Aspects, Vol. 33, No. 9, (2020), 1696-1702. doi: 10.5829/IJE.2020.33.09C.01.
  4. Pellizzari, M. and Cipolloni, G., "Tribological behaviour of cu based materials produced by mechanical milling/alloying and spark plasma sintering", Wear, Vol. 376, (2017), 958-967. https://doi.org/10.1016/j.wear.2016.11.050
  5. Torabi, A., Babaheydari, R., Akbari, G. and Mirabootalebi, S., "Optimizing of micro-hardness of nanostructured cu–cr solid solution produced by mechanical alloying using ann and genetic algorithm", SN Applied Sciences, Vol. 2, No. 11, (2020), 1-9. https://doi.org/10.1007/s42452-020-03722-x
  6. Fathy, A., Wagih, A. and Abu-Oqail, A., "Effect of zro2 content on properties of cu-zro2 nanocomposites synthesized by optimized high energy ball milling", Ceramics International, Vol. 45, No. 2, (2019), 2319-2329. https://doi.org/10.1016/j.ceramint.2018.10.147
  7. Abu-Oqail, A., Samir, A., Essa, A., Wagih, A. and Fathy, A., "Effect of gnps coated ag on microstructure and mechanical properties of cu-fe dual-matrix nanocomposite", Journal of Alloys and Compounds, Vol. 781, (2019), 64-74. https://doi.org/10.1016/j.jallcom.2018.12.042
  8. Eze, A.A., Jamiru, T., Sadiku, E.R., Durowoju, M.Ọ., Kupolati, W.K., Ibrahim, I.D., Obadele, B.A., Olubambi, P.A. and Diouf, S., "Effect of titanium addition on the microstructure, electrical conductivity and mechanical properties of copper by using sps for the preparation of cu-ti alloys", Journal of Alloys and Compounds, Vol. 736, (2018), 163-171. https://doi.org/10.1016/j.jallcom.2017.11.129.
  9. Babaheydari, R., Mirabootalebi, S. and Akbari, G., "Investigation on mechanical and electrical properties of cu-ti nanocomposite produced by mechanical alloying", International Journal of Engineering, Transactions C: Aspects, Vol. 33, No. 9, (2020), 1759-1765. doi: 10.5829/IJE.2020.33.09C.09.
  10. Alex, S., Chattopadhyay, K., Barshilia, H.C. and Basu, B., "Thermally evaporated cu–al thin film coated flexible glass mirror for concentrated solar power applications", Materials Chemistry and Physics, Vol. 232, (2019), 221-228. https://doi.org/10.1016/j.matchemphys.2019.04.078
  11. Hussain, M., Khan, U., Jangid, R. and Khan, S., "Hardness and wear analysis of cu/Al2O3 composite for application in edm electrode", in IOP Conference Series: Materials Science and Engineering, IOP Publishing. Vol. 310, (2018), 012044.
  12. De Marco, V., Grazioli, A. and Sglavo, V.M., "Production of planar copper-based anode supported intermediate temperature solid oxide fuel cells cosintered at 950 c", Journal of Power Sources, Vol. 328, (2016), 235-240. https://doi.org/10.1016/j.jpowsour.2016.08.025
  13. Saha, S., Abd Hamid, S.B. and Ali, T.H., "Catalytic evaluation on liquid phase oxidation of vanillyl alcohol using air and H2O2 over mesoporous cu-ti composite oxide", Applied Surface Science, Vol. 394, (2017), 205-218. https://doi.org/10.1016/j.apsusc.2016.10.070
  14. Shen, D., Zhu, Y., Yang, X. and Tong, W., "Investigation on the microstructure and properties of cu-cr alloy prepared by in-situ synthesis method", Vacuum, Vol. 149, (2018), 207-213. https://doi.org/10.1016/j.vacuum.2017.12.035
  15. Gustmann, T., Dos Santos, J., Gargarella, P., Kühn, U., Van Humbeeck, J. and Pauly, S., "Properties of cu-based shape-memory alloys prepared by selective laser melting", Shape Memory and Superelasticity, Vol. 3, No. 1, (2017), 24-36. https://doi.org/10.1007/s40830-016-0088-6.
  16. Li, J., Chang, L., Li, S., Zhu, X. and An, Z., "Microstructure and properties of as-cast cu-cr-zr alloys with lanthanum addition", Journal of Rare Earths, Vol. 36, No. 4, (2018), 424-429. https://doi.org/10.1016/j.jre.2017.11.006
  17. Vorotilo, S., Loginov, P.A., Churyumov, A.Y., Prosviryakov, A.S., Bychkova, M.Y., Rupasov, S.I., Orekhov, A.S., Kiryukhantsev-Korneev, P.V. and Levashov, E.A., "Manufacturing of conductive, wear-resistant nanoreinforced cu-ti alloys using partially oxidized electrolytic copper powder", Nanomaterials, Vol. 10, No. 7, (2020), 1261. https://doi.org/10.3390/nano10071261
  18. Agboola, P.O., Shakir, I., Almutairi, Z.A. and Shar, S.S., "Hydrothermal synthesis of cu-doped cos2@ nf as high performance binder free electrode material for supercapacitors applications", Ceramics International, Vol. 48, No. 6, (2022), 8509-8516. https://doi.org/10.1016/j.ceramint.2021.12.061
  19. Rodak, K., "Cu-cr and cu-fe alloys processed by new severe plastic deformation: Microstructure and properties", Severe Plastic Deformation Techniques, (2017), 115. doi: 10.5772/intechopen.68954.
  20. Awadallah, O. and Cheng, Z., "Formation of sol-gel based cu2znsns4 thin films using ppm-level hydrogen sulfide", Thin Solid Films, Vol. 625, (2017), 122-130. https://doi.org/10.1016/j.tsf.2017.01.054
  21. Liau, L.C.-K. and Huang, J.-S., "Energy-level variations of cu-doped zno fabricated through sol-gel processing", Journal of Alloys and Compounds, Vol. 702, (2017), 153-160. https://doi.org/10.1016/j.jallcom.2017.01.174
  22. Hosseini, M., Pardis, N., Manesh, H.D., Abbasi, M. and Kim, D.-I., "Structural characteristics of cu/ti bimetal composite produced by accumulative roll-bonding (ARB)", Materials & Design, Vol. 113, (2017), 128-136. https://doi.org/10.1016/j.matdes.2016.09.094
  23. Cheng, J., Cai, Q., Zhao, B., Yang, S., Chen, F. and Li, B., "Microstructure and mechanical properties of nanocrystalline al-zn-mg-cu alloy prepared by mechanical alloying and spark plasma sintering", Materials, Vol. 12, No. 8, (2019), 1255. https://doi.org/10.3390/ma12081255
  24. Mirabootalebi, S. and Akbari, G., "Phase transformation in carbon during mechanical alloying of graphite: Experimental and molecular dynamic study", Journal of Materials Engineering and Performance, (2022), 1-11. https://doi.org/10.1007/s11665-022-07706-3
  25. Mirabootalebi, S.O., "Experimental and molecular dynamic studies of phase transformation and amorphization in silicon during high-energy ball milling", Materials Today: Proceedings, Vol. 76, (2023), 607-615. https://doi.org/10.1016/j.matpr.2022.12.098.
  26. Mirabootalebi, S. and Babaheydari, R., "Prediction length of carbon nanotubes in cvd method by artificial neural network", Iran JOC, Vol. 11, No. 4, (2019), 2731.
  27. Majumder, H. and Maity, K., "Multi-response optimization of wedm process parameters using taguchi based desirability function analysis", in IOP Conference Series: Materials Science and Engineering. IOP Publishing. (2018).
  28. Saindane, U., Soni, S. and Menghani, J., "Friction and wear performance of brake pad and optimization of manufacturing parameters using grey relational analysis", International Journal of Engineering, Transactions C: Aspects, Vol. 35, No. 3, (2022), 552-559. doi: 10.5829/IJE.2022.35.03C.07.
  29. Shaikh, R., Shirazian, S. and Walker, G.M., "Application of artificial neural network for prediction of particle size in pharmaceutical cocrystallization using mechanochemical synthesis", Neural Computing and Applications, Vol. 33, No. 19, (2021), 12621-12640. https://doi.org/10.1007/s00521-021-05912-z
  30. Babaheydari, R. and Mirabootalebi, S., "Prediction micro-hardness of al-based composites by using artificial neural network in mechanical alloying", Journal of Environmental Friendly Materials, Vol. 4, No. 1, (2020), 31-35.
  31. Petrudi, A.M. and Rahmani, M., "Validation and optimization of thermophysical properties for thermal conductivity and viscosity of nanofluid engine oil using neural network", Journal of Modeling and Simulation of Materials, Vol. 3, No. 1, (2020), 53-60. https://doi.org/10.21467/jmsm.3.1.53-60
  32. Da Silva, I.N., Spatti, D.H., Flauzino, R.A., Liboni, L.H.B. and dos Reis Alves, S.F., Artificial neural network architectures and training processes, in Artificial neural networks. 2017, Springer.21-28.
  33. Mirahmadi Babaheydari, R., Mirabootalebi, S.O. and Akbari Fakhrabadi, G.H., "Effect of alloying elements on hardness and electrical conductivity of cu nanocomposites prepared by mechanical alloying", Iranian Journal of Materials Science and Engineering, Vol. 18, No. 1, (2021), 1-11. doi: 10.22068/ijmse.18.1.1.
  34. Mirabootalebi, S., "A new method for preparing buckypaper by pressing a mixture of multi-walled carbon nanotubes and amorphous carbon", Advanced Composites and Hybrid Materials, Vol. 3, (2020), 336-343. https://doi.org/10.1007/s42114-020-00167-z
  35. Safi, S. and Akbari, G., "Evaluation of synthesizing al2o3 nano particles in copper matrix by mechanical alloying of cu-1% al and copper oxide", Journal of Advanced Materials in Engineering (Esteghlal), Vol. 36, No. 1, (2017), 71-85. doi: 10.18869/ACADPUB.JAME.36.1.71.
  36. Suryanarayana, C., "Mechanical alloying and milling", Progress in Materials Science, Vol. 46, No. 1-2, (2001), 1-184. https://doi.org/10.1016/S0079-6425(99)00010-9
  37. Pourfereidouni, A. and Akbari, G.H., "Development of nano-structure cu-ti alloys by mechanical alloying process", in Advanced Materials Research. Vol. 829, (2013), 168-172.
  38. Wang, Y., Bossé, G., Nair, H., Schreiber, N., Ruf, J., Cheng, B., Adamo, C., Shai, D., Lubashevsky, Y. and Schlom, D., "Subterahertz momentum drag and violation of matthiessen’s rule in an ultraclean ferromagnetic srruo 3 metallic thin film", Physical Review Letters, Vol. 125, No. 21, (2020), 217401. https://doi.org/10.1103/PhysRevLett.125.217401
  39. Murray, J., "The cu− ti (copper-titanium) system", Bulletin of Alloy Phase Diagrams, Vol. 4, No. 1, (1983), 81-95. https://doi.org/10.1007/BF02880329
  40. Datta, A. and Soffa, W., "The structure and properties of age hardened cu-ti alloys", Acta Metallurgica, Vol. 24, No. 11, (1976), 987-1001. https://doi.org/10.1016/0001-6160(76)90129-2
  41. Lopez-Hirata, V.M., Hernandez-Santiago, F., Saucedo-Muñoz, M.L., Avila-Davila, E.O. and Villegas-Cardenas, J.D., Analysis of β´(cu 4 ti) precipitation during isothermal aging of a cu–4 wt% ti alloy, in Characterization of minerals, metals, and materials 2020. 2020, Springer.403-412.
  42. Akinwekomi, A.D. and Lawal, A.I., "Neural network-based model for predicting particle size of az61 powder during high-energy mechanical milling", Neural Computing and Applications, Vol. 33, No. 24, (2021), 17611-17619. https://doi.org/10.1007/s00521-021-06345-4
  43. Varol, T. and Ozsahin, S., "Artificial neural network analysis of the effect of matrix size and milling time on the properties of flake al-cu-mg alloy particles synthesized by ball milling", Particulate Science and Technology, Vol. 37, No. 3, (2019), 381-390. https://doi.org/10.1080/02726351.2017.1381658.
  44. Varol, T., Canakci, A. and Ozsahin, S., "Prediction of effect of reinforcement content, flake size and flake time on the density and hardness of flake aa2024-sic nanocomposites using neural networks", Journal of Alloys and Compounds, Vol. 739, (2018), 1005-1014. https://doi.org/10.1016/j.jallcom.2017.12.256
  45. Devadiga, U., Poojary, R.K.R. and Fernandes, P., "Artificial neural network technique to predict the properties of multiwall carbon nanotube-fly ash reinforced aluminium composite", Journal of Materials Research and Technology, Vol. 8, No. 5, (2019), 3970-3977. https://doi.org/10.1016/j.jmrt.2019.07.005
  46. Panda, S. and Panda, G., "Performance evaluation of a new bp algorithm for a modified artificial neural network", Neural Processing Letters, (2020), 1-21. https://doi.org/10.1007/s11063-019-10172-z.
  47. Mirabootalebi, S.O., Fakhrabadi, G.H.A. and Mirahmadi, R., "Carbon via ball milling of graphite and prediction of its crystallite size through ann", Organomet. Chem, Vol. 1, No. 2, (2021), 76-85. http://dx.doi.org/10.22034/jaoc.2021.288020.1021
  48. Zhou, G., Moayedi, H., Bahiraei, M. and Lyu, Z., "Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings", Journal of Cleaner Production, Vol. 254, (2020), 120082. https://doi.org/10.1016/j.jclepro.2020.120082
  49. Moayedi, H., Tien Bui, D., Gör, M., Pradhan, B. and Jaafari, A., "The feasibility of three prediction techniques of the artificial neural network, adaptive neuro-fuzzy inference system, and hybrid particle swarm optimization for assessing the safety factor of cohesive slopes", ISPRS International Journal of Geo-Information, Vol. 8, No. 9, (2019), 391. https://doi.org/10.3390/ijgi8090391
International Journal of Engineering
Volume 36, Issue 10
TRANSACTIONS A: Basics
October 2023
Pages 1932-1941

Files

Share

How to cite

Statistics