Artificial neural network and multi-criterion decision making approach of designing a blend of biodegradable lubricants and investigating its tribological properties:
01 Aug 2021-Vol. 235, Iss: 8, pp 1575-1589
TL;DR: In this article, various blends containing glycerol, castor oil (NCO), and cashew nut shell liquid (CNSL) were made following soft computational techniques and the blend consisting 60% glycerols and 40% NCO was propo...
Abstract: 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 propo...
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TL;DR: In this paper , the authors provide helpful guidance for efficient and scientific tribology research using tribo-informatics approaches using the concept of "triboinformatics" guided by the framework of "Tribo-Informatics".
Abstract: Abstract Tribology research mainly focuses on the friction, wear, and lubrication between interacting surfaces. With the continuous increase in the industrialization of human society, tribology research objects have become increasingly extensive. Tribology research methods have also gone through the stages of empirical science based on phenomena, theoretical science based on models, and computational science based on simulations. Tribology research has a strong engineering background. Owing to the intense coupling characteristics of tribology, tribological information includes subject information related to mathematics, physics, chemistry, materials, machinery, etc. Constantly emerging data and models are the basis for the development of tribology. The development of information technology has provided new and more efficient methods for generating, collecting, processing, and analyzing tribological data. As a result, the concept of “tribo-informatics (triboinformatics)” has been introduced. In this paper, guided by the framework of tribo-informatics, the application of tribo-informatics methods in tribology is reviewed. This article aims to provide helpful guidance for efficient and scientific tribology research using tribo-informatics approaches.
15 citations
TL;DR: In this paper , a review on the recent advances in the applications of machine learning in tribology can be found, which can be used to address intractable tribological problems including structure-property relationships and efficient lubricant design.
Abstract: In tribology, a considerable number of computational and experimental approaches to understand the interfacial characteristics of material surfaces in motion and tribological behaviors of materials have been considered to date. Despite being useful in providing important insights on the tribological properties of a system, at different length scales, a vast amount of data generated from these state-of-the-art techniques remains underutilized due to lack of analysis methods or limitations of existing analysis techniques. In principle, this data can be used to address intractable tribological problems including structure-property relationships in tribological systems and efficient lubricant design in a cost and time effective manner with the aid of machine learning. Specifically, data-driven machine learning methods have shown potential in unraveling complicated processes through the development of structure-property/functionality relationships based on the collected data. For example, neural networks are incredibly effective in modeling non-linear correlations and identifying primary hidden patterns associated with these phenomena. Here we present several exemplary studies that have demonstrated the proficiency of machine learning in understanding these critical factors. A successful implementation of neural networks, supervised, and stochastic learning approaches in identifying structure-property relationships have shed light on how machine learning may be used in certain tribological applications. Moreover, ranging from the design of lubricants, composites, and experimental processes to studying fretting wear and frictional mechanism, machine learning has been embraced either independently or integrated with optimization algorithms by scientists to study tribology. Accordingly, this review aims at providing a perspective on the recent advances in the applications of machine learning in tribology. The review on referenced simulation approaches and subsequent applications of machine learning in experimental and computational tribology shall motivate researchers to introduce the revolutionary approach of machine learning in efficiently studying tribology.
2 citations
TL;DR: In this article , the authors investigated the friction reduction capability of two types of micro-textures (grooves and dimples) created on steel surfaces using a vertical milling machine.
Abstract: The present work investigates the friction reduction capability of two types of micro-textures (grooves and dimples) created on steel surfaces using a vertical milling machine. The wear studies were conducted using a pin-on-disc tribometer, with the results indicating a better friction reduction capacity in the case of the dimple texture as compared to the grooved texture. The microscopic images of the pin surface revealed deep furrows and significant damage on the pin surfaces of the groove-textured disc. An optimization of the textured surfaces was performed using an artificial neural network (ANN) model, predicting the influence of the surface texture as a function of the load, depth of cut and distance between the micro-textures.
2 citations
10 Nov 2022-Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology
TL;DR: In this paper , machine learning techniques are used to predict the coefficient of friction of an epoxy polymer resin (SU-8) and its composite coatings deposited on a silicon wafer.
Abstract: Machine learning (ML) techniques are used to predict the coefficient of friction of an epoxy polymer resin (SU-8) and its composite coatings deposited on a silicon wafer. Filler type and the number of cycles are taken as the input parameters. The filler types included, two solid fillers namely, graphite and talc, and a liquid filler such as Perfluoropolyether (PFPE). Six variations of the SU8 coatings were developed based on the different combinations of filers used and tested. The experimental data generated for these different coatings for varying number of cycles (0 to 499) was used to train the different ML algorithms like ANN, SVM, CART, and RF to predict the coefficient of friction. The performance of these ML techniques was compared by calculating mean absolute error (MAE), root means square error (RMSE), and square of the correlation coefficient (R2). The ANN algorithm was observed to have the best (R2) metrics while the other ML techniques SVM, CART, and RF had a satisfactory performance with some inaccuracies seen for the CART algorithm for the data set under consideration.
2 citations
21 Dec 2022-Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology
TL;DR: Abrasive water jet machining (AWJM) is an advanced machining process that has a variety of applications, including machining of hard-to-machine materials, drilling, milling, coating removal, polishing, etc as mentioned in this paper .
Abstract: Abrasive Water Jet Machining (AWJM) is an advanced machining process that has a variety of applications, including machining of hard-to-machine materials, drilling, milling, coating removal, polishing, etc. They are attractive because of their versatility, absence of heat-affected zones, low thermal-distortions, low cutting-forces, and environment-friendly nature. However, use of plain-water in AWJM poses some problems due to lack of jet coherence, poor suspension characteristics, and unsatisfactory performances in submerged-cutting and cutting of hollow surfaces. Countering these problems appropriately can lead to better cutting performances in terms of material removal rate, depth of cut, kerf-width, and kerf-taper. It can be achieved with addition of some additives in abrasive water-jet. These additives are mostly polymers of high molecular mass, which increases viscosity, suspension capability, and stability of jet, while reducing drag. In addition to AWJM, the effect of polymeric-additives in other water-based jets like Abrasive-Slurry-Jet (ASJ), High-pressure ASJ, etc. are also discussed. Therefore, this study aims to identify and list of different additives that are used in AWJM and other water-jet-based machining-processes over a past few decades, and to provide a run-through on their performances. It can provide the significant information to researchers working in fields related to waterjets.
1 citations
References
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TL;DR: In this paper, the laser power dependence of the spectra of oxides and oxyhydroxides was investigated by using Raman microscopy, and it was shown that increasing laser power causes the characteristic bands of hematite to show up in the spectrum of most of the compounds studied.
Abstract: Hematite (α-Fe2O3), magnetite (Fe3O4), wustite (FeO), maghemite (γ-Fe2O3), goethite (α-FeOOH), lepidocrocite (γ-FeOOH) and δ-FeOOH were studied by Raman microscopy. Such compounds have already been studied by Raman spectroscopy, but there are some disagreements in the reported data. Here, Raman microscopy was employed to investigate the laser power dependence of the spectra of these oxides and oxyhydroxides. Low laser power was used for the reference spectra in order to minimize the risks of spectral changes due to sample degradation. The results obtained show that increasing laser power causes the characteristic bands of hematite to show up in the spectra of most of the compounds studied whereas the hematite spectrum undergoes band broadening and band shifts. © 1997 John Wiley & Sons, Ltd.
2,569 citations
"Artificial neural network and multi..." refers background in this paper
...These peaks were very close to the peaks of iron oxides and FeOOH reported by De Faria et al.38 The presence of peaks at 1626 cm 1, 1631 cm 1, 3201 cm 1, 3200 cm 1, 3427 cm 1 and 3431 cm 1 clearly indicated the presence of HOH and OH bonds39 formed due to the degradation of glycerol during tribo test....
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...These peaks were very close to the peaks of iron oxides and FeOOH reported by De Faria et al.(38) The presence of peaks at 1626 cm (1), 1631 cm (1), 3201 cm (1), 3200 cm (1), 3427 cm 1 and 3431 cm 1 clearly indicated the presence of HOH and OH bonds(39) formed due to the degradation of glycerol during tribo test....
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TL;DR: It is found that the bound water molecules retain a shear fluidity characteristic of the bulk liquid, even when compressed down to films 1.0 ± 0.3 nanometer thick, due to the ready exchange of water molecules within the hydration layers as they rub past each other under strong compression.
Abstract: We have measured the shear forces between solid surfaces sliding past each other across aqueous salt solutions, at pressures and concentrations typical of naturally occurring systems. In such systems the surface-attached hydration layers keep the compressed surfaces apart as a result of strongly repulsive hydration forces. We find, however, that the bound water molecules retain a shear fluidity characteristic of the bulk liquid, even when compressed down to films 1.0 ± 0.3 nanometer thick. We attribute this to the ready exchange (as opposed to loss) of water molecules within the hydration layers as they rub past each other under strong compression.
582 citations
"Artificial neural network and multi..." refers background in this paper
...Further to that the formation of iron oxides on the surface of the tribo pairs also led to a film which would be a combination of the oxide layer, glycerol and water molecules,(37) and led to an easy sliding of the tribo pairs in presence of water molecules.(35) The Raman spectra of the wear track of the tested balls (Figure 8) showed peaks at 361 cm (1), 425 cm (1), 706 cm (1), 1307 cm (1), 1464 cm 1 and 1600 cm 1 indicating the formation of iron oxides and FeOOH (particularly the peak at 1307 cm (1))....
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TL;DR: The ability of Glycerol/water mixtures to inhibit ice crystallization is linked to the concentration of glycerol and the hydrogen bonding patterns formed by these solutions, which mimic the strong hydrogen bonding pattern seen in ice, yet crystallization does not occur.
Abstract: Molecular dynamics simulations and infrared spectroscopy were used to determine the hydrogen bond patterns of glycerol and its mixtures with water. The ability of glycerol/water mixtures to inhibit ice crystallization is linked to the concentration of glycerol and the hydrogen bonding patterns formed by these solutions. At low glycerol concentrations, sufficient amounts of bulk-like water exist, and at low temperature, these solutions demonstrate crystallization. As the glycerol concentration is increased, the bulk-like water pool is eventually depleted. Water in the first hydration shell becomes concentrated around the polar groups of glycerol, and the alkyl groups of glycerol self-associate. Glycerol−glycerol hydrogen bonds become the dominant interaction in the first hydration shell, and the percolation nature of the water network is disturbed. At glycerol concentrations beyond this point, glycerol/water mixtures remain glassy at low temperatures and the glycerol−water hydrogen bond favors a more linea...
257 citations
"Artificial neural network and multi..." refers background in this paper
...An increase in the concentration of glycerol led to an increased amount of glycerol-water combination which formed more linear chains than glycerol itself.(36) The presence of hydrogen bonds was the primary reason for the reduction of frictional properties....
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TL;DR: In this paper, pressure and temperature dependent viscosities of two glass forming liquids, glycerol and dibutyl phthalate (DBP), have been studied in the range P=0-3 GPa, T=0 -125 °C, and η=101 −101 0 cP.
Abstract: The pressure and temperature dependent viscosities of two glass forming liquids, glycerol and dibutyl phthalate (DBP), have been studied in the range P=0–3 GPa, T=0–125 °C, and η=101–1010 cP. These studies were made using a combination of a rolling‐ball and a centrifugal‐force diamond anvil cell viscometer. The majority of the results extend up to viscosities of 107 cP, with those at 22.5 °C going to 1010 cP. The overall precision of the data are approximately 10% or better throughout. This level of precision allows us to define a viscosity surface which can then be extrapolated to the glass transition along both temperature and pressure cuts. The T‐dependence of viscosity is larger for glycerol than DBP but the P‐dependence smaller for glycerol than for DBP, whereas the T‐dependence is much more pressure sensitive for DBP. These data provide an assessment of the T‐dependence of an isothermal model (free volume), the P‐dependence of an isobaric model (Vogel–Tammann–Fulcher) and by extension that for isoch...
198 citations
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...It is known that with high pressure the viscosity of a liquid increases.26 The lower coefficient of friction exhibiting property of glycerol was also reported by Cook et al.27 where they compared the lubricating properties of glycerol and paraffin oil....
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...The lower coefficient of friction exhibiting property of glycerol was also reported by Cook et al.(27) where they compared the lubricating properties of glycerol and paraffin oil....
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TL;DR: Cashew nut shell liquid (CNSL) is a byproduct of the cashew industry and is a naturally occurring substituted phenol which can take part in a variety of reactions as discussed by the authors.
Abstract: Cashew nut shell liquid (CNSL) is a by-product of the cashew industry. It is a naturally occurring substituted phenol which can take part in a variety of reactions. It is a cheap and renewable substance and can be employed for the manufacture of a multitude of useful products. It can replace phenol in many applications with equivalent or better results. CNSL by itself is useful for insecticidal, fungicidal, anti-termite, and medicinal applications, and as an additive, in many plastic formulations. Resins derived from CNSL are employed widely in the fields of friction materials, automobiles, surface coatings, adhesives, laminates, rubber compounding, and have several miscellaneous applications. Greater utilization of CNSL as a monomer for indusrial polymer products can be an attractive proposal in view of its low cost, abundant availability, and chemically reactive nature, amongst other attributes. This review gives an accounts of the composition, extraction, reactions, and applications of CNSL based on th...
186 citations
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...CNSL mainly contains phenolic resin making it highly polymerizable substance and resulting in easy polymerization reactions.(23,24) It has been reported that modified CNSL on reacting with glycerol forms alkyd resins,(26) and those resins are viscouseased viscosity in blends E13-E21....
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