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Article
Publication date: 21 May 2024

Joseph Vivek, Naveen Venkatesh S., Tapan K. Mahanta, Sugumaran V., M. Amarnath, Sangharatna M. Ramteke and Max Marian

This study aims to explore the integration of machine learning (ML) in tribology to optimize lubrication interval decisions, aiming to enhance equipment lifespan and operational…

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Abstract

Purpose

This study aims to explore the integration of machine learning (ML) in tribology to optimize lubrication interval decisions, aiming to enhance equipment lifespan and operational efficiency through wear image analysis.

Design/methodology/approach

Using a data set of scanning electron microscopy images from an internal combustion engine, the authors used AlexNet as the feature extraction algorithm and the J48 decision tree algorithm for feature selection and compared 15 ML classifiers from the lazy-, Bayes and tree-based families.

Findings

From the analyzed ML classifiers, instance-based k-nearest neighbor emerged as the optimal algorithm with a 95% classification accuracy against testing data. This surpassed individually trained convolutional neural networks’ (CNNs) and closely approached ensemble deep learning (DL) techniques’ accuracy.

Originality/value

The proposed approach simplifies the process, enhances efficiency and improves interpretability compared to more complex CNNs and ensemble DL techniques.

Details

Industrial Lubrication and Tribology, vol. 76 no. 5
Type: Research Article
ISSN: 0036-8792

Keywords

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Article
Publication date: 23 September 2024

Pedro Santos, Amilton Sinatora and Roberto Souza

Given the current challenges for improving the tribological behavior in automotive engines, which require lubricants that adapt to different operating conditions through…

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Abstract

Purpose

Given the current challenges for improving the tribological behavior in automotive engines, which require lubricants that adapt to different operating conditions through replacement mechanisms to reduce friction and wear, this study aims to analyze the use of hexagonal boron nitride nanoparticles blended in the lubricating oil of a diesel engine. The target was to minimize frictional power losses and wear of cylinder liner surfaces to validate what was observed in laboratory and to confirm improvements in thermal efficiency.

Design/methodology/approach

Before the definition of the concentration to be used in a real engine environment, tests of sample dispersion were conducted using an ultrasound bath. The mixtures were added to storage bottles in concentrations of 0.1% and 0.5%, as observed in Figure 1. The samples were subsequently analyzed using the dynamic light scattering (DLS) technique. There was a reduction in the hydrodynamic size for the sample with 0.5% of hexagonal boron nitride (hBN), possibly due to sedimentation of the powder during the analysis, which supported this work to continue with the use of 0.1% concentration.

Findings

The behavior of hBN as nano additive in a real diesel engine was problematic when compared with laboratory environment, leading to impact in oil temperature. In addition, it was noticed a high amount of deposit formation at the top dead center (TDC) and mid-stroke (MC) and nonsignificative wear at MC and bottom dead center (BDC) of the cylinder, with unusual formation of products from antiwear additives known as zinc dialkyl-dithiophosphate at MC position with the use of hBN. For this reason, this work provides insights into how hBN nanoparticles may not contribute toward the improvement of tribological performance.

Originality/value

The findings of this work aimed to provide a better understanding of the impact of hBN nanoparticles used as additives in real engine environment in terms of performance and tribological impacts. The results of this work indicated that hBN as additive gave poor results in terms of performance and wear prevention.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2024-0047/

Details

Industrial Lubrication and Tribology, vol. 77 no. 1
Type: Research Article
ISSN: 0036-8792

Keywords

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