Modeling and prediction of wear rate of aluminum alloy (Al 7075) using power law and ANN

Authors

  • M. Hanief National Institute of Technology Srinagar, Jammu & Kashmir, 190006, India
  • Shafi M. Charoo National Institute of Technology Srinagar, Jammu & Kashmir, 190006, India

DOI:

https://doi.org/10.30544/544

Keywords:

ANN; Al7075 Alloy; wear rate; model

Abstract

The wear process significantly influences machine parts during their useful life. The wear process is complex, and therefore, it is very difficult to develop a comprehensive model involving all the operating parameters. In the present study, wear rate is measured during the wear process at different operating parameters such as force (load), sliding distance, and velocity. Power law and Artificial neural network (ANN) approaches are used to model the wear rate of Al7075 alloy. Power law and neural network-based models are compared using statistical methods with a coefficient of determination (R2), mean absolute percentage error (MAPE), and means square error (MSE). It is seen that the proposed models are competent to predict the wear rate of Al7075 alloy. The ANN model estimates the wear rate with high accuracy compared to that of the power law model. The models developed for wear rate were found to be consistent with the experimental data. ANOVA analysis revealed that the load has a significant effect on the wear rate than the sliding speed and sliding distance.

References

M. Sato, N. Tsuji, Y. Minaminob, Y. Koizumi: Science and Technology of Advanced Materials, 5 (2004) 145-152.

Crossreff

L. Hackel, J. R. Rankin, A. Rubenchik, W. E. King, M. Matthews: Additive Manufacturing, 24 (2018) 67-75.

Crossreff

B. B. Straumal, A. R. Kilmametov, O. A. Kogtenkova, A. A. Mazilkin, B. Baretzky, A. Korneva, P. Zięba: International Journal of Materials Research, 110 (2019) 608-613.

Crossreff

K. Wu, Y. Xinjian, H. Zhan, W. Haodong, L. Ting, Y. Zhe, L. Jun: Journal of Materials Engineering and Performance, 28 (2019) 2937-2945.

Crossreff

N. S. Prabhakar, N. Radhika, R. Raghu: Procedia Engineering, 97 (2014) 994-1003.

Crossreff

G. B. Narasimha, M. V. Krishna, R. Sindhu: Procedia Engineering, 97 (2014) 555-562.

Crossreff

A. Baradeswaran, A. Elayaperumal, R. F. Issac: Procedia Engineering, 64 (2013) 973-982.

Crossreff

L. Haviez, R. Toscano, M. El Youssef, S. Fouvry, G. Yantio, G. Moreau: Journal of Intelligent & Fuzzy Systems, 28 (2015) 1745-1753.

Crossreff

D. Li, R. Lv, G. Si, Y. You: Polymer Composites, 38 (2017) 1705-1711.

Crossreff

K. Velten, R. Reinicke, K. Friedrich: Tribology International, 33 (2000) 731-736.

Crossreff

S. P. Jones, R. Jansen, R. L. Fusaro: Tribology Transactions, 40 (1997) 312-320.

Crossreff

T. Mahmoud: Artificial neural network prediction of the wear rate of powder metallurgy Al/Al2O3 metal matrix composites, Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 226 (2012) 3-15.

Crossreff

A. Ghasempoor, T. Moore, J. Jeswiet: Online wear estimation using neural networks, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 212 (1998) 105-112.

Crossreff

N. M. Kumar, S. S. Kumaran, L. Kumaraswamidhas: Alexandria Engineering Journal, 55 (2016) 19-36.

Crossreff

N. Radhika, K. Vijaykarthik, P. Shivaram: Journal of Engineering Science and Technology, 10 (2015) 258-268.

X. Chen, L. Ma, C. Li, X. Cao: The International Journal of Advanced Manufacturing Technology, 74 ( 2014) 207-217.

I Argatov: Frontiers in Mechanical Engineering, 5 (2019) 1-9.

Crossreff

A. M. Zain, H. Haron, S. Sharif: Expert Systems with Applications, 37 (2010) 4650-4659.

Crossreff

N. R. Prabha, J. E. R. Dhas: In: Proceedings ICCICCT. Eds.: Curran, IEEE 2016, p. 750-753.

I Argatov, Young S Chai: Tribology International, 138 (2019) 211-214.

Crossreff

I Argatov , Young S Chai: Artificial neural network modeling of sliding wear, Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology (2020) 1-10.

Crossreff

Downloads

Published

2020-09-19

How to Cite

Hanief, M., and Shafi M. Charoo. 2020. “Modeling and Prediction of Wear Rate of Aluminum Alloy (Al 7075) Using Power Law and ANN”. Metallurgical and Materials Engineering 27 (2):161-69. https://doi.org/10.30544/544.

Issue

Section

Modeling and simulation in metallurgical and materials engineering