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Machine Learning Algorithms to Predict Wear Behavior of Modified ZA-27 Alloy Under Varying Operating Parameters

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Abstract

The current work mainly focused on predicting the wear performance of modified ZA-27 alloy under dry sliding conditions. The trail runs were conducted with wear parameters such as varying normal loads, sliding speeds and sliding distances. A total of 75 number experiments were conducted to determine the wear loss. Supervised machine learning algorithms such as random forest (RF), Gaussian process regression, k-nearest neighbor, support vector machine and linear regression were used with the experimental results as input data set to predict the wear loss. Results reveal that the performance rate of R2 for training and testing are closely nearer for all constructed models. RF has yielded the superior results in R2, MAE and RMSE among all the constructed models. Wear surface and debris analysis were carried out using SEM micrographs.

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References

  1. Babic M, Ninkovic R (2004) Zn-Al alloys as tribo materials. Tribol Ind 26(1 & 2):3–7

    Google Scholar 

  2. Prasad BK, Patwardhan AK, Yegneswaran AH (1998) Microstructure and property characterization of a modified zinc-base alloy and comparison with bearing alloys. J Mater Eng Perform 7(1):130–135

    Article  CAS  Google Scholar 

  3. Algur V, Kabadi VR, Ganechari SM, Chavan VR (2017) Effect of Mn content on tribological wear behaviour of ZA-27 alloy. Mater Today Proc 4:10927–10934

    Article  Google Scholar 

  4. Algur V, Kabadi VR, Ganechari SM, Sharanabasappa M (2014) Experimental investigation on friction characteristics of modified ZA-27 alloy using Taguchi technique. Int J Mech Eng Robot Res 3(4):24–32

    Google Scholar 

  5. Sevik H (2014) The effect of silver on wear behaviour of zinc-aluminium based ZA-12 alloy produced by gravity casting. Mater Charact 89:81–87

    Article  CAS  Google Scholar 

  6. Algur V, Kabadi VR, Sharanabasappa M, Ganechari SM, Shetty PB (2015) Optimization of sliding specific wear and frictional force behaivour of modified ZA-27 alloy using taguchi method. Int J Recent Innov Trends Comput Commun 3(5):2644–2649

    Google Scholar 

  7. Seenappa V, Sharma KV (2011) Characterization of mechanical and micro structural properties of ZA alloys. Int J Eng Sci Technol 3(3):1783–1789

    Google Scholar 

  8. Murphy S, Savaskan T (1984) Comparative wear behavior of Zn-Al based alloys in an automotive engine application. Wear 98:151–161

    Article  CAS  Google Scholar 

  9. Li Y, Ngai TL, Xia W, Zhang W (1996) Effects of Mn content on the tribological behaviors of Zn-27% Al-2% Cu alloy. Wear 198:129–135

    Article  CAS  Google Scholar 

  10. Choudhury P, Das S, Datta BK (2002) Effect of Ni on the wear behavior of a zinc aluminium alloy. J Mater Sci 37:2103–2107

    Article  CAS  Google Scholar 

  11. Savaskan T, Aydmer A (2004) Effect of silicon content on the mechanical and tribological properties of monotectoid- based zinc-aluminium-silicon alloys. Wear 257:377–388

    Article  CAS  Google Scholar 

  12. Vencl A, Bobic I, Vucetic F, Bobic B, Kandeva M (2014) The influence of strontium addition on the tribological properties of Zn25Al3Si alloy in boundary lubricated condition. Int Conf Mater Tribol 25:1

    Google Scholar 

  13. Pandey JP, Prasad BK, Yegneswaran AH (1998) Dry sliding wear behaviour of a zinc-based alloy: a comparative study with a leaded-tin bronze. Mater Trans 39(11):1121–1125

    Article  CAS  Google Scholar 

  14. Rosenkranz A, Marian M, Profito FJ, Aragon N, Shah R (2020) The use of artificial intelligence in tribology. Lubricants 9(2):1–11

    Google Scholar 

  15. Liu Y, Li HY, Jiang HF, Su XJ (2013) Artificial neural networking modelling to predict hot deformation behaivour of zinc-aluminium alloy. Mater Sci Technol 29(2):184–189

    Article  CAS  Google Scholar 

  16. Altay O, Gurgenc T, Ulas M, Ozel C (2020) Prediction of wear loss quantities of ferro-alloy coating using different machine learning algorithms. Friction 8(1):107–114

    Article  CAS  Google Scholar 

  17. Moosavin A, Ahmadi H, Tababaeefer A, Khazaee M (2013) Comparison of two classifiers; K-nearest neighbor and artificial neural network, for fault diagnosis on a main engine journal bearing. Shock Vib 20:263–272

    Article  Google Scholar 

  18. Sharanabasappa M, Kabadi VR, Algur V (2015) The effect of pearlite, cementite, and martensite phases on volumetric wear rate of hypereutectoid steel under dry sliding conditions. Int J Metall Mater Sci Eng 5(1):2278–2524

    Google Scholar 

  19. Nagaral M, Deshapande RG, Auradi V, Boppana SB, Dayanand S, Anilkumar MR (2021) Mechanical and wear characterization of ceramic boron carbide reinforced Al2024 alloy metal composites. J Bio- Tribo Corros 7(9):1–2

    Google Scholar 

  20. Al-Nasser AD, Radaideh A (2008) Estimation of simple linear regression model using L ranked set sampling. Int J Open Probl Compt Math 1(1):18–33

    Google Scholar 

  21. Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, Amsterdam

    Google Scholar 

  22. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Google Scholar 

  23. Brereton RG, Lloyd GR (2010) Support vector machines for classification and regression. Analyst 135(2):230–267

    Article  CAS  Google Scholar 

  24. S Gunn (1998) Support vector machines for classification and regression. ISIS Technical Report. Image speech and intelligent systems group university of Southampton

  25. Zhong Y, Zhao L, Liu Z, Xu Y, Li R (2010) Using a support vector machine method to predict the development indices of very high water cut oilfields. Pet Sci 7:379–384

    Article  CAS  Google Scholar 

  26. Hashemitaheri M, Mekarthy SM, Cherukuri H (2020) Prediction of specific cutting forces and maximum tool temperatures in orthogonal machining by support vector and Gaussian process regression methods. Procedia Manuf 48:1000–1008

    Article  Google Scholar 

  27. Kong D, Chen Y, Li N (2018) Gaussian process regression for tool wear prediction. Mech Syst Signal Process 104:556–574

    Article  Google Scholar 

  28. Aye S, Heyns P (2017) An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission. Mech Syst Signal Process 84:485–498

    Article  Google Scholar 

  29. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  30. Aydin F, Durgut R (2021) Estimation of wear performance of AZ91alloy under dry sliding conditions using machine learning methods. Trans Nonferrous Met Soc China 31:125–137

    Article  CAS  Google Scholar 

  31. James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning. Springer Science and Business Media LLC, New York

    Book  Google Scholar 

  32. Bermejo S, Cabestany J (2000) Adaptive soft k-nearest-neighbour classifiers. Pattern Recogn 33:1999–2005

    Article  Google Scholar 

  33. Qiao Q, He H, Yu J, Zhang Y, Qi H (2021) Applicability of machine learning on predicting the mechanochemical wear of the borosilicate and phosphate glass. Wear 476:203721

    Article  CAS  Google Scholar 

  34. Imandoust SB, Bolandraftar M (2013) Application of K-nearest neighbour (KNN) approach for predicting economic events: theoretical background. Int J Eng Res Appl 3(5):605–610

    Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, RYMEC, Ballari, Karnataka, India

    Veerabhadrappa Algur

  2. Department of Computer Science, VSK University, Ballari, Karnataka, India

    Poornima Hulipalled

  3. Department of Studies in Mathematics, VSK University, Ballari, Karnataka, India

    V. Lokesha

  4. Aircraft Research and Design Centre, Hindustan Aeronautics Limited, Bangalore, Karnataka, India

    Madeva Nagaral

  5. Department of Mechanical Engineering, Siddaganga Institute of Technology, Tumkur, Karnataka, India

    V. Auradi

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  1. Veerabhadrappa Algur
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  2. Poornima Hulipalled
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  3. V. Lokesha
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  4. Madeva Nagaral
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  5. V. Auradi
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Correspondence to Veerabhadrappa Algur.

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Algur, V., Hulipalled, P., Lokesha, V. et al. Machine Learning Algorithms to Predict Wear Behavior of Modified ZA-27 Alloy Under Varying Operating Parameters. J Bio Tribo Corros 8, 7 (2022). https://doi.org/10.1007/s40735-021-00610-8

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  • DOI: https://doi.org/10.1007/s40735-021-00610-8

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