Optimization of wear and friction characteristics of Phyllanthus Emblica seed oil based lubricant using response surface methodology

Singh, Yashvir; Singh, Paramvir; Sharma, Abhishek; Choudhary, Praveen; Singla, Amneesh; Singh, Nishant

doi:10.1016/j.ejpe.2018.04.001

Cited by 37 publications

(14 citation statements)

References 23 publications

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“…The experimental method had 2400 cycles and was investigated after that; experiments were randomized. RSM described each variable based on the key influence and interaction effects on the output at each step [ 53 ]. The multivariate model is given as follows: Y = β 0 + ∑ 3 i=1 β i X i +∑ 3 i=1 β ij X i X j +∑ 3 i=1 β ijk X i X j X k …”

Section: Resultsmentioning

confidence: 99%

Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks

et al. 2021

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Lightweight composite materials have recently been recognized as appropriate materials have been adopted in many industrial applications because of their versatility. The present research recognizes the inclusion of ceramics such as Gr and B4C in manufacturing AMMCs through stir casting. Prepared composites were tested for hardness and wear behaviour. The tests’ findings revealed that the reinforced matrix was harder (60%) than the un-reinforced alloy because of the increased ceramic phase. The rising content of B4C and Gr particles led to continuous improvements in wear resistance. The microstructure and worn surface were observed through SEM (Scanning electron microscope) and revealed the formation of mechanically mixed layers of both B4C and Gr, which served as the effective insulation surface and protected the test sample surface from the steel disc. With the rise in the content of B4C and Gr, the weight loss declined, and significant wear resistance was achieved at 15 wt.% B4C and 10 wt.% Gr. A response surface analysis for the weight loss was carried out to obtain the optimal objective function. Artificial neural network methodology was adopted to identify the significance of the experimental results and the importance of the wear parameters. The error between the experimental and ANN results was found to be within 1%.

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Section: Resultsmentioning

confidence: 99%

Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks

et al. 2021

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“…A. Greco et al [6] Yashvir Singh et al [23] have optimized tribological characteristics of seed oil using response surface methodology. The tribological investigation has been conducted by using pin on disc instrument .also statistical modelling of particular wear rate & COF parameters has been done by using experimental results.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For a pin-ondisc Arrangement two specimens are required namely disc & pin of standard dimensions .The standards & disc dimension for experimentation on pin on disc instrument is mentioned in Table 1. (2) [23] Where, B is the blend percentage, L is the load in N, S is the sliding velocity in m/s…”

Section: Fig 3 Pin On Disc Setupmentioning

confidence: 99%

Tribological investigation on oil blended with Additive using Response surface methodology

Gadekar

Kamble

2020

E3S Web Conf.

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Friction and wear in dynamic parts is the primary reason for energy loss in gearbox lubrication system and this can be optimized by utilizing modified lubricant. The tribological nature of gearbox system is critically affected by factors such as type of lubricant, loading & speed etc. In latest years, multiple advanced oil and modern tribological techniques & instruments have been utilized to investigate behaviour of oil like pin on disc, Fourball tester etc. This paper presents comparative investigation of oil blended with additive for two different conditions using prediction model & RSM. The design of experimentations has been conducted by using response surface methodology. The value of inputs parameters such as concentration, load & sliding velocity ranges from 0.5 to 5 %, 60 to 100 N and 0.65 to 1.5 m/s, respectively are utilized to evaluate the outcomes of coefficient of friction and specific wear rate. At the end results from Prediction equations are compared with experimental literature based outcomes to signify the effect of parameters like blend %, load & Sliding speed. The Coefficient of friction model showed 47.57 % more closer outcomes as compared to the Specific wear rate model for specific variation of unknown parameters for pin on disc setup in oil.

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“…where X i and x i represent the coded and actual values of independent variable "i", x min and x max are the actual values of the independent variable "i" at the minimum and maximum points, respectively (Singh et al 2018).…”

Section: Choice Of Statistical Modelmentioning

confidence: 99%

Single-/triple-stage biotrickling filter treating a H₂S-rich biogas stream: Statistical analysis of the effect of empty bed retention time and liquid recirculation velocity

Salehi¹,

Chaiprapat

2019

Journal of the Air & Waste Management Association

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Optimization of wear and friction characteristics of Phyllanthus Emblica seed oil based lubricant using response surface methodology

Cited by 37 publications

References 23 publications

Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks

Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks

Tribological investigation on oil blended with Additive using Response surface methodology

Single-/triple-stage biotrickling filter treating a H₂S-rich biogas stream: Statistical analysis of the effect of empty bed retention time and liquid recirculation velocity

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Optimization of wear and friction characteristics of Phyllanthus Emblica seed oil based lubricant using response surface methodology

Cited by 37 publications

References 23 publications

Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks

Multi Ceramic Particles Inclusion in the Aluminium Matrix and Wear Characterization through Experimental and Response Surface-Artificial Neural Networks

Tribological investigation on oil blended with Additive using Response surface methodology

Single-/triple-stage biotrickling filter treating a H2S-rich biogas stream: Statistical analysis of the effect of empty bed retention time and liquid recirculation velocity

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Single-/triple-stage biotrickling filter treating a H₂S-rich biogas stream: Statistical analysis of the effect of empty bed retention time and liquid recirculation velocity