Machine learning models of the transition from solid to liquid lubricated friction and wear in aluminum-graphite composites

Hasan, Syam; Kordijazi, Amir; Rohatgi, Pradeep K.; Nosonovsky, Michael

doi:10.1016/j.triboint.2021.107326

Cited by 47 publications

(20 citation statements)

References 26 publications

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“…A number of authors have observed several factors affecting the tribological performance of the FRP composites. [ 26,42–45 ] Nevertheless, in the present study, sliding distance as the tribological test parameter scored higher over mechanical properties and was found to play an essential role in the SWR prediction. According to previous studies, calculations proved SWR to be inversely proportional to the sliding distance variable.…”

Section: Feature Importance For Predicting Specific Wear Ratecontrasting

confidence: 65%

“…The database regarding the SWR, its graph and other related details were directly taken from the previously published work of the present authors. [ 26 ] The trend in Figure 4 indicates a decreasing SWR with an increase in the abrading distance in all the three samples. The improved wear resistance of GNPs filled GFRE composite could be attributed to the enhanced mechanical properties.…”

Section: Resultsmentioning

confidence: 96%

“…The SWR was recorded at different abrading distance under 20 N of constant load. In one of their F I G U R E 3 Gradient boosting machine loss function minimization algorithm F I G U R E 4 Specific wear rate as a function of abrading distance [26] previous works, the present authors conducted two-body abrasive wear test on GFRE and its composites. The database regarding the SWR, its graph and other related details were directly taken from the previously published work of the present authors.…”

Section: Tribo-performance Analysismentioning

confidence: 99%

“…They also help the design engineers in selection of the appropriate tribo‐pairs of machine components for greater reliability and durability. [ 25–27 ]…”

Section: Introductionmentioning

confidence: 99%

“…They also help the design engineers in selection of the appropriate tribo-pairs of machine components for greater reliability and durability. [25][26][27] The objective of the present study is to understand the tribological behavior of the neat woven glass fabricreinforced epoxy composites and woven glass fabric reinforced epoxy composites, containing GNPs (0.5 wt.% and 1 wt.%), based on supervised ML models. Datasets were collected from previously published studies by the present authors in order to feed the ML model.…”

mentioning

confidence: 99%

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Data‐driven modeling for predicting tribo‐performance of graphene‐incorporated glass‐fabric reinforced epoxy composites using machine learning algorithms

Kumar

Singh

2022

Polymer Composites

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The present article aims to investigate a comparative effect of the mechanical properties and tribological operating variables (applied load and sliding distance) on the tribo‐performance of graphene incorporated woven glass fabric reinforced epoxy (GFRE) composites. Computational and data‐driven machine learning (ML) approach has been extensively applied to examine the advancement of the tribological systems. For the study, tribo‐mechanical data, gathered from previous investigations by the present authors, have been used as well. Accordingly, a predictive model, based on the ML algorithms for predicting specific wear rates (SWR), has been developed. Further, a co‐relation between the SWR and the mechanical properties of materials has been determined. For the investigation process, three ML approaches, have been applied, viz., artificial neural network (ANN), Random Forest (RF), and Gradient Boosting Machine (GBM). Feature score analysis has been done as well revealing that variation of the sliding distance, interlaminar shear strength (ILSS) and product of tensile strength (s) and elongation (e) significantly influence the SWR prediction. The trained ML models can predict the tribo‐performance from mechanical properties variables and tribological test conditions, which is impossible with conventional two‐parameter correlations. During the present study, the coefficient of determination (R2 value) in different models was found to be 0.9883, 0.9884, and 0.9762 for the ANN, RF, and GBM, respectively. The best‐performing model was RF.

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Section: Feature Importance For Predicting Specific Wear Ratecontrasting

confidence: 65%

Section: Resultsmentioning

confidence: 96%

Section: Tribo-performance Analysismentioning

confidence: 99%

“…They also help the design engineers in selection of the appropriate tribo‐pairs of machine components for greater reliability and durability. [ 25–27 ]…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Data‐driven modeling for predicting tribo‐performance of graphene‐incorporated glass‐fabric reinforced epoxy composites using machine learning algorithms

Kumar

Singh

2022

Polymer Composites

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Development of machine learning models for the prediction of erosion wear of hybrid composites

Mahapatra,

Satapathy

2024

Polymer Composites

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This article reports on development of an adaptive framework for predicting the erosion performance of polymer composites using certain statistical and machine learning (ML) models. For this, ramie‐epoxy composites reinforced with variations (0–30 wt%) of sponge iron slag (an iron industry waste) are considered. The composites are fabricated and then subjected to high temperature solid particle erosion wear trials following Taguchi's L27 orthogonal array. The effects of different control factors on the erosion rate in an interactive environment are appraised by analysis of variance (ANOVA) which reveals the filler content as the most significant factor contributing 66.21%, followed by impact velocity (22.86%) and impingement angle (2.28%). A regression model based on the input–output parameters obtained from experimentation is constructed for prediction of erosion rate. Further, four predictive models using different machine learning algorithms are also proposed to predict the erosion rate of the composites. The feasibility and performance of each ML model is assessed using appropriate performance metrics. Among all the models, the gradient boosting machine model is found to be the most reliable model exhibiting the highest prediction accuracy and least errors.Highlights Development of novel class of composites reinforced with sponge iron slag. Database creation based on erosion wear experimentation on the composites. Data‐driven modeling for prediction of erosion rates using machine learning. Comparison of performance of different models and identifying the best one.

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Intelligent Wear Condition Prediction of Ball Bearings Based on Convolutional Neural Networks and Lubricating Oil

Sun,

Bu,

Guo

et al. 2024

J Fail. Anal. and Preven.

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Machine learning models of the transition from solid to liquid lubricated friction and wear in aluminum-graphite composites

Cited by 47 publications

References 26 publications

Data‐driven modeling for predicting tribo‐performance of graphene‐incorporated glass‐fabric reinforced epoxy composites using machine learning algorithms

Data‐driven modeling for predicting tribo‐performance of graphene‐incorporated glass‐fabric reinforced epoxy composites using machine learning algorithms

Development of machine learning models for the prediction of erosion wear of hybrid composites

Intelligent Wear Condition Prediction of Ball Bearings Based on Convolutional Neural Networks and Lubricating Oil

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