Abstract
Al–20Si alloy was subjected to friction stir processing to find its effect on the microstructure and wear behaviour. The microstructures of as-cast and friction stir processed (FSP) alloy were studied using optical microscopy and field emission scanning electron microscopy. The microstructure analysis showed significant refinement of Si particles in Al–20Si alloy by FSP. Similarly, for the experimental study of the wear behaviour, three different parameters: sliding velocity, normal load, and sliding distance were considered. In this study, five different machine learning (ML) algorithms were used for the prediction of wear rate. The hyper parameter tuning of each model was carried out for accurate comparisons. The models were then evaluated on the basis of different statistical metrics to find the superior model. Random Forest model showed the highest prediction accuracy (R2 = 0.8846) and was considered for comparing the wear rates with experimental values.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
Data Availability
The dataset used in the current study is available on reasonable request.
References
Acharya M, Mandal A (2021) Effect of semi-solid heat treatment on the microstructure and dry sliding wear behavior of Al–20Si alloy at optimized parametric conditions. Met Mater Int 27:1578–1586. https://doi.org/10.1007/s12540-019-00550-6
Stojanovic B, Bukvic M, Epler I (2018) Application of aluminum and aluminum alloys in engineering. Appl Eng Lett 3:52–62. https://doi.org/10.18485/aeletters.2018.3.2.2
Bulei C, Stojanovic B, Utu D (2022) Developments of discontinuously reinforced aluminium matrix composites: Solving the needs for the matrix. J Phys Conf Ser 2212. https://doi.org/10.1088/1742-6596/2212/1/012029
Kapranos P, Kirkwood DH, Atkinson HV et al (2003) Thixoforming of an automotive part in A390 hypereutectic Al-Si alloy. J Mater Process Technol 135:271–277. https://doi.org/10.1016/S0924-0136(02)00857-9
Lee ES (2000) Study on the economics of hypereutectic aluminum-silicon (Al-Si) alloy machining. Int J Adv Manuf Technol 16:700–708. https://doi.org/10.1007/s001700070021
Zhao JW, Wu S Sen (2010) Microstructure and mechanical properties of rheo-diecasted A390 alloy. Trans Nonferrous Met Soc China (English Ed 20:s754–s757. https://doi.org/10.1016/S1003-6326(10)60576-6
Stojanović B, Gajević S, Kostić N et al (2022) Optimization of parameters that affect wear of A356/Al2O3 nanocomposites using RSM, ANN, GA and PSO methods. Ind Lubr Tribol 74:350–359. https://doi.org/10.1108/ILT-07-2021-0262
Stojanović B, Tomović R, Gajević S, Petrović J, Miladinović S (2022) Tribological Behavior of Aluminum Composites Using Taguchi Design and Ann. Adv Eng Lett 1:28–34. https://doi.org/10.46793/adeletters.2022.1.1.5
Milojević S, Stojanović B (2018) Determination of tribological properties of aluminum cylinder by application of Taguchi method and ANN-based model. J Brazilian Soc Mech Sci Eng 40. https://doi.org/10.1007/s40430-018-1495-8
Elmadagli M, Perry T, Alpas AT (2007) A parametric study of the relationship between microstructure and wear resistance of Al – Si alloys. Wear 262:79–92. https://doi.org/10.1016/j.wear.2006.03.043
Prasad BK, Venkateswarlu K, Modi OP et al (1998) Sliding wear behavior of some Al-Si alloys: role of shape and size of si particles and test conditions. Metall Mater Trans A Phys Metall Mater Sci 29:2747–2752. https://doi.org/10.1007/s11661-998-0315-7
Acharya M, Mandal A (2019) Individual and synergistic effect of gamma alumina (γ-Al2O3) and strontium on microstructure and mechanical properties of Al–20Si alloy. Trans Nonferrous Met Soc China 29:1353–1364. https://doi.org/10.1016/S1003-6326(19)65042-9
Shi WX, Gao B, Tu GF, Li SW (2010) Effect of Nd on microstructure and wear resistance of hypereutectic Al-20%Si alloy. J Alloys Compd 508:480–485. https://doi.org/10.1016/j.jallcom.2010.08.098
Stojanović B, Gajević S, Miloradović N et al (2023) Comparative analysis of hybrid composites based on a356 and Za-27 alloys regarding their tribological behaviour. Commun Sci Lett Univ Žilina 25:B215–B227. https://doi.org/10.26552/com.C.2023.056
Acharya M, Mondol S, Mandal A (2020) Development of high strength suction cast hypereutectic Al–20Si alloy containing gamma alumina and strontium. Mater Sci Technol (United Kingdom) 36. https://doi.org/10.1080/02670836.2020.1724403
Charandabi FK, Jafarian HR, Mahdavi S (2021) Modification of microstructure, hardness, and wear characteristics of an automotive-grade Al-Si alloy after friction stir processing. J Adhes Sci Technol 35:2696–2709. https://doi.org/10.1080/01694243.2021.1898858
Hasan S, Kordijazi A, Rohatgi PK, Nosonovsky M (2021) Modeling of dry friction and wear of aluminum base alloys using machine learning algorithms. Tribol Int 161:107065. https://doi.org/10.1016/j.triboint.2021.107065
Malamousi K, Delibasis K, Allcock B, Kamnis S (2022) Digital transformation of thermal and cold spray processes with emphasis on machine learning. Surf Coatings Technol 433:128138. https://doi.org/10.1016/j.surfcoat.2022.128138
Gyurova LA, Friedrich K (2011) Artificial neural networks for predicting sliding friction and wear properties of polyphenylene sulfide composites. Tribiology Int 44:603–609. https://doi.org/10.1016/j.triboint.2010.12.011
Graser J, Kauwe SK, Sparks TD (2018) Machine learning and energy minimization approaches for crystal structure predictions : a review and new horizons. Chem Mater 30:3601–3612. https://doi.org/10.1021/acs.chemmater.7b05304
Schmidt J (2019) Recent advances and applications of machine learning in solid- state materials science. npj Comput Mater 5:1–36. https://doi.org/10.1038/s41524-019-0221-0
Wen C, Zhang Y, Wang C et al (2019) Machine learning assisted design of high entropy alloys with desired property. Acta Mater 170:109–117
Chang Y, Jui C, Lee W (2019) Prediction of the composition and hardness of high-entropy alloys by machine learning. J Mater Sci 71:3433–3442
Hasan MS, Wong T, Rohatgi PK, Nosonovsky M (2022) Analysis of the friction and wear of graphene reinforced aluminum metal matrix composites using machine learning models. Tribol Int 170:107527. https://doi.org/10.1016/j.triboint.2022.107527
Canute KR, Ojha SSN, Microstructure WÁ (2012) Investigation on the wear properties of primary Si modified Al – 20Si alloy. Trans Indian Inst Met 65:673–676. https://doi.org/10.1007/s12666-012-0174-1
Yii SLJ, Anas NM, Ramdziah MN, Anasyida AS (2016) Microstructural and mechanical properties of Al-20%Si containing cerium. Procedia Chem 19:304–310. https://doi.org/10.1016/j.proche.2016.03.015
Tiwari K, Gautam G, Kumar N et al (2018) Effect of primary silicon refinement on mechanical and wear properties of a hypereutectic Al-Si alloy. Silicon 10:2227–2239. https://doi.org/10.1007/s12633-017-9755-2
Wang F, Liu H, Ma Y, Jin Y (2004) Effect of Si content on the dry sliding wear properties of spray-deposited Al – Si alloy. Mater Des 25:163–166. https://doi.org/10.1016/j.matdes.2003.08.005
Raghukiran N, Kumar R (2013) Processing and dry sliding wear performance of spray deposited hyper-eutectic aluminum – silicon alloys. J Mater Process Tech 213:401–410. https://doi.org/10.1016/j.jmatprotec.2012.10.007
Kaiser MS, Sabbir SH, Kabir MS et al (2018) Study of mechanical and wear behaviour of hyper-eutectic Al-Si automotive alloy through Fe, Ni and Cr addition. Mater Res 21:1–9. https://doi.org/10.1590/1980-5373-MR-2017-1096
Li Q, Zhu Y, Li B et al (2018) Effect of iron addition on the microstructures and properties of hypereutectic Al-20%Si alloys. Mater Res Express 6:016506–016516
Barekar NS, Dhindaw BK, Fan Z (2010) Improvement in silicon morphology and mechanical properties of Al–17Si alloy by melt conditioning shear technology. Int J Cast Met Res 23:225–231. https://doi.org/10.1179/136404610X12665088537338
Alshmri F, Atkinson HV, Hainsworth SV et al (2014) Dry sliding wear of aluminium-high silicon hypereutectic alloys. Wear 313:106–116. https://doi.org/10.1016/j.wear.2014.02.010
Al-samarai RA, Ahmad KR, Al-Douri Y (2012) Evaluate the effects of various surface roughness on the tribological characteristics under dry and lubricated conditions for Al–Si alloy. J Surf Eng Mater Adv Technol 2:167–173
Angadi BM, Reddy AC, Nehru J et al (2016) Effect of phosphorus addition on friction-interface temperature and wear behaviour of hypereutectic Al–Si alloys. Indian Foundry J 62:56–66
Jasim KM, Dwarakadasa ES (1992) Dry sliding wear in binary Al–Si alloys at low bearing pressures. J Mater Sci Lett 11:421–423
Mahmoud TS (2013) Surface modification of A390 hypereutectic Al–Si cast alloys using friction stir processing. Surf Coat Technol 228:209–220. https://doi.org/10.1016/j.surfcoat.2013.04.031
Xu CL, Yang YF, Wang HY, Jiang QC (2007) Effects of modification and heat-treatment on the abrasive wear behavior of hypereutectic Al-Si alloys. J Mater Sci 42:6331–6338. https://doi.org/10.1007/s10853-006-1189-y
Vijeesh V, Prabhu KN (2014) Review of microstructure evolution in hypereutectic Al-Si alloys and its effect on wear properties. Trans Indian Inst Met 67:1–18
Hao Y, Gao B, Tu GF et al (2011) Influence of high current pulsed electron beam (HCPEB) treatment on wear resistance of hypereutectic Al-17.5Si and Al-20Si alloys. Mater Sci Forum 675–677:693–696. https://doi.org/10.4028/www.scientific.net/MSF.675-677.693
Raju K, Harsha AP, Ojha SN (2010) Microstructural features, wear, and corrosion behaviour of spray cast Al – Si alloys. J Eng Tribol 225:151–160. https://doi.org/10.1177/2041305X10394055
Goudar DM, Magalad VT, Kurahatti RV (2020) Study of microstructure and tribological behaviour of spray cast high silicon hypereutectic Al-Si alloy. Adv Mater Process Technol 8:1245–1254. https://doi.org/10.1080/2374068X.2020.1855402
Okfalisa, Gazalba I, Mustakim, Reza NGI (2018) Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification. Proc - 2017 2nd Int Conf Inf Technol Inf Syst Electr Eng ICITISEE 2017 2018-Janua:294–298. https://doi.org/10.1109/ICITISEE.2017.8285514
Farahnakian F, Pahikkala T, Liljeberg P, Plosila J (2013) Energy aware consolidation algorithm based on K-nearest neighbor regression for cloud data centers. Proc - 2013 IEEE/ACM 6th Int Conf Util Cloud Comput UCC 2013 256–259. https://doi.org/10.1109/UCC.2013.51
Lian Z, Li M, Lu W (2022) Fatigue life prediction of aluminum alloy via knowledge-based machine learning. Int J Fatigue 157:106716. https://doi.org/10.1016/j.ijfatigue.2021.106716
Aydin F, Durgut R, Mustu M, Demir B (2023) Prediction of wear performance of ZK60 / CeO2 composites using machine learning models. Tribol Int 177:107945. https://doi.org/10.1016/j.triboint.2022.107945
Ma ZY (2008) Friction stir processing technology : a review. Metall Mater Trans A 39:642–658. https://doi.org/10.1007/s11661-007-9459-0
Mishra RS, Ma ZY (2005) Friction stir welding and processing. Mater Sci Eng R 50:1–78. https://doi.org/10.1016/j.mser.2005.07.001
Kliauga AM, Ferrante M (2005) Liquid formation and microstructural evolution during re-heating and partial melting of an extruded A356 aluminium alloy. Acta Mater 53:345–356. https://doi.org/10.1016/j.actamat.2004.09.030
Humphreys FJ, Ardakani MG (1994) The deformation of paticle-containing aluminium single crystals. Acta Metall Mater 42:749–761
Acknowledgements
The authors are thankful to IIT Bhubaneswar for providing experimental facilities.
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study. Material preparation, data collection, original draft and analysis were performed by Mihira Acharya. conceptualization, design and review part were carried out by Dr. Animesh Mandal. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics Approval
Not applicable.
Consent to Participate
Not applicable.
Consent for Publication
The authors have given consent for publication as per the journal policy.
Competing Interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Acharya, M., Mandal, A. Microstructure Study of Friction Stir Processed Hypereutectic Al-20Si Alloy and Analysis of the Wear Behaviour using Machine Learning Algorithms. Silicon 16, 3539–3551 (2024). https://doi.org/10.1007/s12633-023-02840-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12633-023-02840-6