Artificial intelligence based design of multiple friction modifiers dispersed castor oil and evaluating its tribological properties

Bhaumik, Shubrajit; Pathak, Dev S.; Dey, Shobhana; Datta, Shubhabrata

doi:10.1016/j.triboint.2019.06.006

Cited by 50 publications

(33 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A typical feed forward neural network has been used in this work. The sensitivity analyses (connecting weight method) 21 was used to determine the influence of the input parameters on the output.…”

Section: Methodsmentioning

confidence: 99%

Artificial neural network and multi-criterion decision making approach of designing a blend of biodegradable lubricants and investigating its tribological properties

Bhaumik

Kamaraj

2020

Proceedings of the Institution of Mechanical Engineers, Part J:

Self Cite

View full text Add to dashboard Cite

Various blends containing glycerol, castor oil (NCO) and cashew nut shell liquid (CNSL) were made following soft computational techniques and the blend consisting 60% glycerol and 40% NCO was proposed, which exhibited 37% less coefficient of friction (CoF) than NCO and CNSL and 50% less CoF and comparable extreme pressure properties to non-biodegradable commercial mineral oil (CMO). Accelerated wear was indicated by particle quantifier index for CMO, NCO and CNSL samples while normal wear was observed in glycerol and the proposed blend. SEM and 3-D profilometer images exhibited more damaged surfaces in NCO and CNSL than other lubricants. Raman spectra indicated the presence of FeOOH, OH, HOH and fatty acids on the wear tracks of the proposed blend.

show abstract

Section: Methodsmentioning

confidence: 99%

Artificial neural network and multi-criterion decision making approach of designing a blend of biodegradable lubricants and investigating its tribological properties

Bhaumik

Kamaraj

2020

Proceedings of the Institution of Mechanical Engineers, Part J:

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, experimental work demonstrated a sensitivity regarding the respective testing conditions (four-ball versus pin-on-disk tester). A similar study was performed by Bhaumik et al [25], in which a genetic algorithm and ANN were used to optimize and design a castor oil lubricant with graphite, graphene, multi-walled carbon nanotubes, and zinc oxide nanoparticles. Pin-on-disk tests were used to gather tribological data for the castor oil with different concentrations of these modifiers.…”

Section: Lubricant Formulationsmentioning

confidence: 97%

The Use of Artificial Intelligence in Tribology—A Perspective

et al. 2020

View full text Add to dashboard Cite

Artificial intelligence and, in particular, machine learning methods have gained notable attention in the tribological community due to their ability to predict tribologically relevant parameters such as, for instance, the coefficient of friction or the oil film thickness. This perspective aims at highlighting some of the recent advances achieved by implementing artificial intelligence, specifically artificial neutral networks, towards tribological research. The presentation and discussion of successful case studies using these approaches in a tribological context clearly demonstrates their ability to accurately and efficiently predict these tribological characteristics. Regarding future research directions and trends, we emphasis on the extended use of artificial intelligence and machine learning concepts in the field of tribology including the characterization of the resulting surface topography and the design of lubricated systems.

show abstract

“…The neural network was trained with a back propagation algorithm and tangential transfer functions and the architecture considered as most suitable with relative deviations between 0.2% and 2.3% was built of three hidden layers with 2, 6, and 9 neurons, respectively. Bhaumik et al [122,123] also applied a multi-hidden layer feed forward ANN to design lubricant formulations with vegetable oil blends (coconut, castor and palm oil) and various friction modifiers (MWCNT and graphene) based upon 80 data sets obtained from four-ball-tests as well as 120 data sets from pin-on-disk tests as reported in various literature. The respective material and test conditions were also included as influencing factors.…”

Section: Surface Texturingmentioning

confidence: 99%

Current Trends and Applications of Machine Learning in Tribology—A Review

2021

View full text Add to dashboard Cite

Machine learning (ML) and artificial intelligence (AI) are rising stars in many scientific disciplines and industries, and high hopes are being pinned upon them. Likewise, ML and AI approaches have also found their way into tribology, where they can support sorting through the complexity of patterns and identifying trends within the multiple interacting features and processes. Published research extends across many fields of tribology from composite materials and drive technology to manufacturing, surface engineering, and lubricants. Accordingly, the intended usages and numerical algorithms are manifold, ranging from artificial neural networks (ANN), decision trees over random forest and rule-based learners to support vector machines. Therefore, this review is aimed to introduce and discuss the current trends and applications of ML and AI in tribology. Thus, researchers and R&D engineers shall be inspired and supported in the identification and selection of suitable and promising ML approaches and strategies.

show abstract

Artificial intelligence based design of multiple friction modifiers dispersed castor oil and evaluating its tribological properties

Cited by 50 publications

References 36 publications

Artificial neural network and multi-criterion decision making approach of designing a blend of biodegradable lubricants and investigating its tribological properties

Artificial neural network and multi-criterion decision making approach of designing a blend of biodegradable lubricants and investigating its tribological properties

The Use of Artificial Intelligence in Tribology—A Perspective

Current Trends and Applications of Machine Learning in Tribology—A Review

Contact Info

Product

Resources

About