Prediction of Surface Roughness and Coefficient of Friction Using Artificial Neural Network in Tribotesting of Bio-Lubricants

Bharadwaj, Prasanna Vineeth; Jeevan, T. P.; Suvin, P.S.; Jayaram, S. R.

doi:10.4028/www.scientific.net/amm.895.52

Cited by 3 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning has been recently used in engineering for analysing a large amount of data and for solving non-linear, complex and multi-dimensional functional relationships. More specifically in tribology, Artificial Neural Network (ANN) technique has been utilized for predicting mechanical properties [14] and wear and friction coefficient in metallic composites [15][16][17], in short fibre reinforced polymeric composites [18,19], in thin-film composites [20] and lubricated contacts [21]. As an effective alternative to the ANN, the Radial Basis Function (RBF) can be used.…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Radial Basis Function (RBF) Method for Predicting Lubricated Friction on Textured and Porous Surfaces

Boidi

Silva

Profito

et al. 2020

Surf. Topogr.: Metrol. Prop.

View full text Add to dashboard Cite

The coefficient of friction (CoF) obtained from tribological tests conducted on textured and porous surfaces was analysed using the machine learning Radial Basis Function (RBF) method. Non-textured and non-porous samples were taken as reference surfaces. Test parameters, such as entrainment velocity and slide-roll ratio (SRR), along with geometric characteristics of surface features (e.g. texture width and depth, coverage area, circularity, spatial distribution and directionality, among others), were selected as training dataset for the machine learning RBF model. The surface features were divided into designed patterns (dimples and grooves) manufactured by laser texturing, and randomised cavities (surface pores) resulted from the sintering process. The principal outcomes of this study are the effective use of the machine learning RBF method for tribological applications, as well as a critical discussion on its feasibility for the experimental dataset selected and the preliminary results obtained. Main results show that the Hardy multiquadric radial basis function provided an overall correlation coefficient of 0.934 for 35 poles. The application of the suggested machine learning technique and methodology can be extended to other experimental results available in the literature to train more robust models for predicting tribological performances of textured and structured surfaces.

show abstract

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Radial Basis Function (RBF) Method for Predicting Lubricated Friction on Textured and Porous Surfaces

Boidi

Silva

Profito

et al. 2020

Surf. Topogr.: Metrol. Prop.

View full text Add to dashboard Cite

show abstract

Recent Progress of Machine Learning Algorithms for the Oil and Lubricant Industry

2023

View full text Add to dashboard Cite

Machine learning (ML) algorithms have brought about a revolution in many industries where otherwise operation time, cost, and safety would have been compromised. Likewise, in lubrication research, ML has been utilized on many occasions. This review provides an in-depth understanding of seven ML algorithms from a tribological perspective. More specifically, it presents a comprehensive overview of recent advancements in ML applied to lubrication research, organized into four distinct categories. The first category, experimental parameter prediction, highlights the significant contributions of artificial neural networks (ANNs) in accurately forecasting operating conditions related to friction and wear. These predictions offer valuable insights that aid in forensic preparation. Discriminant analysis, Bayesian modeling, and transfer learning approaches have also been used to predict experimental parameters. Second, to predict the lubrication film thickness and identify the lubrication regime, algorithms such as logistic regression and ANN were useful. Such predictions provide up to 99.25% accuracy. Third, to predict the friction and wear for a given experimental condition, support vector machine (SVM), polynomial regression, and ANN offered an accuracy above 93%. Finally, for condition monitoring for bearings, gearboxes, gear trains, and similar critical situations where regular in-person inspection is difficult, Naïve Bayes, SVM, decision trees, and ANN were utilized to predict the safe life of lubricants. This review highlighted these four aspects with state-of-the-art examples and discussed the current situation and projected future possibilities of lubricant design facilitated by ML techniques.

show abstract

Prediction of Surface Roughness During Dry Sliding Wear

Lal

Dey

Wàňi

2021

International Journal of Surface Engineering and Interdisciplinary Materials Science

View full text Add to dashboard Cite

Due to the relatively low strength and poor wear resistance of unalloyed titanium and its good mechanical properties, corrosion resistance, and biocompatibility. Ti6Al4V has been extensively used in various type of application including aerospace, biomedical and offshore industries. The goal of this research is to enhance the surface properties of the high strength alloys are examine such as Ti6Al4V pin sliding against Al2O3disc, due to the various surfaces roughness parameters. The COF and the wear rate were found to be lower at higher applied load due to higher frictional heating leading to thermal oxidation and thereby formation of several mm thick tribo-layers on the worn surfaces. Characterization of the tribological sample was performed using a scanning electron microscope (SEM) equipped with energy dispersive X-ray analysis (EDAX) to ensure that the wear pattern and debris morphologies of the Ti6Al4V and alumina disks were distinct, suggesting a surface roughness value determined by 3D profilometer at various load and sliding speed of 0.01ms-1.

show abstract

Prediction of Surface Roughness and Coefficient of Friction Using Artificial Neural Network in Tribotesting of Bio-Lubricants

Cited by 3 publications

References 12 publications

Using Machine Learning Radial Basis Function (RBF) Method for Predicting Lubricated Friction on Textured and Porous Surfaces

Using Machine Learning Radial Basis Function (RBF) Method for Predicting Lubricated Friction on Textured and Porous Surfaces

Recent Progress of Machine Learning Algorithms for the Oil and Lubricant Industry

Prediction of Surface Roughness During Dry Sliding Wear

Contact Info

Product

Resources

About