Statistical Analysis of Dry Sliding Wear Behavior of Graphite Reinforced Aluminum MMCs

Rajesh, S.; Krishna, A. Murali; Raju, P. Rama Murty; Duraiselvam, Muthukannan

doi:10.1016/j.mspro.2014.07.183

Cited by 34 publications

(9 citation statements)

References 16 publications

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“…The measured wear data was converted to S/N ratio for analysis purposes through Minitab-17 [33][34][35][36]. Smaller-the-better condition was adopted for wear loss [38][39][40]. The main effects plot for wear loss for SNR mean values as shown in figure 9.…”

Section: Discussionmentioning

confidence: 99%

Experimental and modeling studies on dry sliding wear behavior of Alumina reinforced AMC

Mohsin¹,

Qazi²,

Faizan³

2021

Eng. Res. Express

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In the present research work predicted properties of Aluminium Alloy Composites prepared through powder metallurgy technique have been examined using Artificial Neural Network (ANN) approach. The aluminium alloy (Al-20Fe −5Cr) matrix reinforced with aluminium oxide (Al 2 O 3 ) varying from 0-30 wt% with a step of 10 wt% were prepared. The green compacts prepared at three different compaction pressures viz. 470, 550 and 600 MPa were sintered at 440 °C for 30 min. Pin-on-disc test was performed to evaluate wear loss of the composites prepared. The influence of alumina varying weight percentage and different compaction pressure had been analyzed and tests were performed according to full factorial design. Analysis of variance (ANOVA) had been employed to predict the percentage contribution of various process parameters. The results indicated that the wear loss was mainly influenced by alumina percentage followed by compaction pressure by 92% and 8% respectively. For modeling and prediction of wear loss, a feed forward back propagation neural network was constructed and compared with experimentally calculated values. Both experimental and ANN predicted values of wear loss were in close correlation with each other with approximately (1-3) % error. RECEIVED

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Section: Discussionmentioning

confidence: 99%

Experimental and modeling studies on dry sliding wear behavior of Alumina reinforced AMC

Mohsin¹,

Qazi²,

Faizan³

2021

Eng. Res. Express

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“…The process parameters and their values at two levels are presented in Table 2. The selection of process parameters' values is based on the past literature [14]. 16.…”

Section: Design Of Experiments (Doe)mentioning

confidence: 99%

Experimental Investigation and Statistical Approach to Wear Characterization of Plasma Nitrided AISI 4140 Steel-A Case Study

Bhaskar

Kudal

2017

MIE

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<p>Components of forming tool dies such as draw ring, ejector pin use AISI 4140 as material for their manufacturing. The integrity of the die cutting tools is essential to achieve adequate product quality. In present study, the influence of plasma nitriding (PN) on the wear behav-iour of AISI 4140 steel was investigated. Full factorial experimental design technique was used to study the main effects and the interaction effects between operational parameters and the response variable. The control factors at their two levels (-1 and +1) were: applied load (4.905N and 14.715N), sliding speed (3.14 m/s and 5.23 m/s), and sliding distance (500m and 1000m).The parameters were coded as A, B, and C, consecutively, and were investigated at two levels (-1 and +1). Response selected was Wear Volume Loss (WVL). The effects of in-dividual variables and their interaction effects for dependent variables, namely, WVL were determined. The process of selecting significant factors, based on statistical tools, is illustrat-ed. Analysis of Variance (ANOVA) was performed to know the impact of individual factors on the WVL. Untreated and PN treated AISI 4140 specimens were investigated using field emission Scanning Electron Microscope (SEM) equipped with Energy Dispersive X-ray (EDX) analyzer. Finally diagnostics tools were used to check adequacy of the model in terms of assumptions of ANOVA. ‘Design Expert-7’ and ‘Minitab 17’ softwares were used in the study. Results of statistical analysis indicate that the most effective parameters in the WVL were load and sliding speed. The interaction between load and sliding speed was the most influencing interaction. Results of regression analysis indicate regression coefficient (R2) to be above 90% which suggests good predictability of the model. ‘Predicted-R2’ and ‘Adjusted-R2’, found to be in good agreement with R2, for both the materials under investigation. More-over, results of SEM microscopy suggest PN to be an effective technique to reduce wear.</p>

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“…The ANOVA gives failure rate equations, which highlights the influence of different factors and their importance. [63][64][65][66][67] Using failure rate equations, failure rates of crusher and its components are calculated. The ANOVA method is used at confidence level of 90%, i.e.…”

Section: Analysis Of Variancementioning

confidence: 99%

Failure rate analysis of jaw crusher using Weibull model

Sinha

Mukhopadhyay

2016

Proceedings of the Institution of Mechanical Engineers, Part E:

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The primary crusher is essential equipment employed for comminuting the mineral in processing plants. Any kind of failure of its components will accordingly hinder the performance of the plant. Therefore, to minimize sudden failures, analysis should be undertaken to improve performance and operational reliability of the crushers and its components. This paper considers the methods for analyzing failure rates of a jaw crusher and its critical components application of a two-parameter Weibull distribution in a mineral processing plant fitted using statistical tests such as goodness of fit and maximum likelihood estimation. Monte Carlo simulation, analysis of variance, and artificial neural network are also applied. Two-parameter Weibull distribution is found to be the best fit distribution using Kolmogorov-Smirnov test. Maximum likelihood estimation method is used to find out the shape and scale parameter of two-parameter Weibull distribution. Monte Carlo simulation generates 40 numbers of shape parameters, scale parameters, and time. Further, 40 numbers of Weibull distribution parameters are evaluated to examine the failure rate, significant difference, and regression coefficient using ANOVA. Artificial neural network with back-propagation algorithm is used to determine R 2 and is compared with analysis of variance.

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Statistical Analysis of Dry Sliding Wear Behavior of Graphite Reinforced Aluminum MMCs

Cited by 34 publications

References 16 publications

Experimental and modeling studies on dry sliding wear behavior of Alumina reinforced AMC

Experimental and modeling studies on dry sliding wear behavior of Alumina reinforced AMC

Experimental Investigation and Statistical Approach to Wear Characterization of Plasma Nitrided AISI 4140 Steel-A Case Study

Failure rate analysis of jaw crusher using Weibull model

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