Data‐Driven Modeling for Predicting Tribo‐Performance of Flax‐Steel‐Epoxy Hybrid Composites Using Machine Learning Approach

Subham Kumar Bhoi; Sourav Kumar Mahapatra; Alok Satapathy

doi:10.1002/app.57634

journal article Jul 20, 2025

Data‐Driven Modeling for Predicting Tribo‐Performance of Flax‐Steel‐Epoxy Hybrid Composites Using Machine Learning Approach

Subham Kumar Bhoi

Sourav Kumar Mahapatra

Alok Satapathy

Journal of Applied Polymer Science Vol. 142 No. 42 · Wiley

View at Publisher Save 10.1002/app.57634

Abstract

ABSTRACTMachine learning (ML) models have recently become popular for their ability to replicate human decision‐making in different fields. This study presents a data‐driven machine learning method for the analysis and prediction of sliding wear behavior of flax‐epoxy hybrid composites, both with and without steel mesh. Three hybrid composites with different stacking sequences of reinforcing fibers are prepared and subsequently well‐planned sliding wear trials are conducted according to the design of experiments. The experimental analysis examined the influence of sliding velocity, sliding distance, and normal load on the specific wear rate, revealing that wear rate increases with higher sliding velocity and normal load but decreases with higher steel mesh content. The collected data is then utilized to train four ML models, whose performances are measured using five different metrics. Among them, the extreme gradient boosting (XGB) model showed the best accuracy, with coefficient of determination (R2) values of 0.93651 for composite F11, 0.98544 for composite F7S4, and 0.97546 for composite F4S7. Feature importance analysis revealed sliding velocity as the most significant factor. Electron microscopy is further utilized to study predominant wear mechanisms.

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References

48

[1]

10.1016/j.triboint.2023.108388

[2]

10.1016/j.compositesb.2012.07.004

[3]

Deepanraj B. "Investigation and Optimization of Wear Properties of Flax Fiber Reinforced Delrin Polymer Composite" Materials Today: Proceedings (2023)

[4]

10.1007/s12221-018-7759-6

[5]

10.1115/1.4050525

[6]

10.1016/j.wear.2009.01.026

[7]

10.1007/s13369-023-08247-0

[8]

10.1016/s0301-679x(98)00007-3

[9]

10.3390/polym15204057

[10]

10.1177/0731684411420606

[11]

10.1016/j.compscitech.2005.05.028

[12]

10.1007/s40033-022-00331-3

[13]

10.1016/j.matdes.2012.05.048

[14]

10.1016/s0266-3538(00)00068-3

[15]

10.1016/s1003-6326(15)64036-5

[16]

10.1163/156855411x610241

[17]

10.1016/j.compscitech.2004.11.009

[18]

10.1177/0731684408097763

[19]

10.1038/s41598-023-40247-w

[20]

10.3390/biomedicines11082251

[21]

10.1557/mrc.2019.32

[22]

10.1038/s41586-018-0337-2

[23]

10.1016/s1003-6326(20)65482-6

[24]

10.1002/pc.25537

[25]

Hasan M. S. "Triboinformatic Modeling of Dry Friction and Wear of Aluminum Base Alloys Using Machine Learning Algorithms" Tribology International (2021)

[26]

10.1002/pc.26974

[27]

10.1038/s41524-019-0221-0

[28]

Processing and physico‐mechanical characterization of sponge iron slag filled ramie fiber reinforced epoxy‐based composites

Sourav Kumar Mahapatra, Alok Satapathy

Polymer Composites 10.1002/pc.26923

[29]

Fabrication and multi-aspect characterization of polymer composite reinforced with differently stacked flax-fiber and steel-wire meshes

Subham Kumar Bhoi, Alok Satapathy

Journal of Elastomers & Plastics 10.1177/00952443251317619

[30]

Mahapatra S. K. "Parametric Analysis of Erosion Wear of Sponge Iron Slag‐Filled Ramie–Epoxy Composites Using Taguchi and Preference Selection Index Methods" Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering (2023)

[31]

10.1016/j.proeng.2014.12.285

[32]

10.1016/j.compstruct.2016.01.098

[33]

10.1177/0021998320928509

[34]

10.1016/j.mspro.2014.07.139

[35]

10.1016/j.matpr.2022.03.258

[36]

Babu R. R. "Optimization of Drilling Parameters of Ss304 By Taguchi Method" International Research Journal of Engineering and Technology (2008)

[37]

10.1016/j.wear.2012.08.001

[38]

An experimental and analytical approach to study the sliding wear performance of epoxy composites with steel wire and glass fiber reinforcement

Subham Kumar Bhoi, Alok Satapathy

Proceedings of the Institution of Mechanical Engin... 10.1177/13506501241286612

[39]

10.3390/lubricants9090086

[40]

Mahapatra S. K. "Erosion Wear Performance of Titania Filled Ramie‐Epoxy Composites: A Data Driven Optimization Study Using Supervised Machine Learning Approach" Journal of Elastomers and Plastics (2024)

[41]

10.1016/j.matpr.2022.06.253

[42]

10.1115/1.4054247

[43]

Sliding wear characteristics of epoxy composites reinforced with steel wire and flax fiber mats: An experimental and analytical study

Subham Kumar Bhoi, Alok Satapathy

Polymer Composites 10.1002/pc.28964

[44]

10.1016/j.polymertesting.2022.107580

[45]

10.1002/pc.28315

[46]

10.3390/ma15030882

[47]

10.1177/13506501221138382

[48]

Mahapatra S. K. "Analysis and Prediction of Erosion Behavior of Epoxy Composites Using Statistical and Machine Learning Techniques" Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering (2024)

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Metrics

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Citations

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References

Details

Published: Jul 20, 2025
Vol/Issue: 142(42)
License: View

Authors

S

Subham Kumar Bhoi

Department of Mechanical Engineering National Institute of Technology Rourkela Odisha India

S

Sourav Kumar Mahapatra

Department of Mechanical Engineering National Institute of Technology Rourkela Odisha India

A

Alok Satapathy

Department of Mechanical Engineering National Institute of Technology Rourkela Odisha India

Cite This Article

Subham Kumar Bhoi, Sourav Kumar Mahapatra, Alok Satapathy (2025). Data‐Driven Modeling for Predicting Tribo‐Performance of Flax‐Steel‐Epoxy Hybrid Composites Using Machine Learning Approach. Journal of Applied Polymer Science, 142(42). https://doi.org/10.1002/app.57634

Data‐Driven Modeling for Predicting Tribo‐Performance of Flax‐Steel‐Epoxy Hybrid Composites Using Machine Learning Approach

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