Prediction of Tribological Behaviour of WC-12Co Nanostructured Microwave Clad through ANN

Zafar, Sunny; Sharma, Apurbba Kumar

doi:10.2474/trol.11.333

Cited by 11 publications

(4 citation statements)

References 22 publications

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“…The ANN model was successfully implemented for microhardness with an R-value of 0.9998 [37]. The ANN model predicted a nearer value to experimental results for wear and coefficient of friction of WC-12Co microwave clads [38]. The neural network for wear of Mo coating has a nearer value to experimental values.…”

Section: Introductionmentioning

confidence: 94%

Analysis of tensile strength on friction stir welded Al 6061 composite reinforced with B₄C and Cr₂O₃ using RSM and ANN

Uday

Rajamurugan²

2023

Eng. Res. Express

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Aluminum (Al) alloys are reinforced with carbides and oxides to enhance their properties. Al composites are developed to meet current automotive, shipbuilding, and aviation requirements. In the current study, aluminum 6061 is reinforced with B4C and Cr2O3 separately to fabricate Al6061 + B4C and Al 6061+Cr2O3 aluminum metal matrix composites (Al MMC). The Al composites were fabricated by stir casting with a wt % in steps of 2%, 4%, and 6%. Joining of Al MMC is essential to develop valuable components. The developed composites were welded using friction stir welding (FSW). FSW is recognized and widely used for joining Al MMC due to premium weld quality with minimum defects. The present study aims to analyze the effect of process parameters and predictive accuracy of the artificial neural network (ANN) and response surface methodology (RSM). The parameters selected for the study are tool rotational speed, tool travel speed, and reinforcement wt %. The FSW was performed based on the experimental design developed by the design expert software. Through RSM analysis, it was found that both the independent factors (tool rotational and tool transverse speed) and the interaction of factors jointly contribute to the FSW joint properties. The higher ultimate strength of 139 MPa and lower tensile strength of 48 MPa are found. As the tool travel speed increase from 20 to 25 mm\min, ultimate tensile strength increase about 59%. The average accuracy of RSM was 98.26 and of ANN was 94.86.

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Section: Introductionmentioning

confidence: 94%

Analysis of tensile strength on friction stir welded Al 6061 composite reinforced with B₄C and Cr₂O₃ using RSM and ANN

Uday

Rajamurugan²

2023

Eng. Res. Express

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“…In this type of network, a linear combination of several inputs as per assigned weightage is used to calculate the one output through a non-linear activation function as illustrated in equation (1) [28,29]:…”

Section: Discussionmentioning

confidence: 99%

Statistical analysis and ANN modelling of tribological performance of aluminium hybrid composites fabricated by PM method

Hussain

Parveen²,

Chauhan³

et al. 2022

Surf. Topogr.: Metrol. Prop.

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This study represents the wear behaviour of the aluminium hybrid composites developed through powder metallurgy.The pure Al-matrix powder was blended with Si3N4/ZrO2 ceramic powders in a high-centrifugal ball mill and then compacted at three different compaction pressure. The sintering of the composites was done in an environment controlled tube furnace at 500⁰C for 1 hour. The effect of wt% of the reinforcement, compaction pressure and applied load was investigated on the wear results performed on the pin-on-disc setup. The Analysis of variance (ANOVA) and artificial neural network (ANN) was used for the wear loss results prediction. The ANOVA results revealed that load is a highly influential parameter for wear loss followed by reinforcement and compaction pressure. Furthermore, an ANN model using feed-forward backpropagation analysis was built which found that the predicted and experimental results are close.

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“…A general full factorial design has been selected for experimentation purposes and experiments were performed as per suggested parameters yielding measured wear values shown in table 3. 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].…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the relation between inputs and outputs is to be taught by network in order to get result with least difference. However, the selection criteria of both transfer function and number of neurons in the hidden layer for an optimum performance is based on iterative analysis [33][34][35][36]. In this experiment tansig and purelin functions are adopted.…”

Section: Artificial Neural Networkmentioning

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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Prediction of Tribological Behaviour of WC-12Co Nanostructured Microwave Clad through ANN

Cited by 11 publications

References 22 publications

Analysis of tensile strength on friction stir welded Al 6061 composite reinforced with B₄C and Cr₂O₃ using RSM and ANN

Analysis of tensile strength on friction stir welded Al 6061 composite reinforced with B₄C and Cr₂O₃ using RSM and ANN

Statistical analysis and ANN modelling of tribological performance of aluminium hybrid composites fabricated by PM method

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

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Prediction of Tribological Behaviour of WC-12Co Nanostructured Microwave Clad through ANN

Cited by 11 publications

References 22 publications

Analysis of tensile strength on friction stir welded Al 6061 composite reinforced with B4C and Cr2O3 using RSM and ANN

Analysis of tensile strength on friction stir welded Al 6061 composite reinforced with B4C and Cr2O3 using RSM and ANN

Statistical analysis and ANN modelling of tribological performance of aluminium hybrid composites fabricated by PM method

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

Contact Info

Product

Resources

About

Analysis of tensile strength on friction stir welded Al 6061 composite reinforced with B₄C and Cr₂O₃ using RSM and ANN

Analysis of tensile strength on friction stir welded Al 6061 composite reinforced with B₄C and Cr₂O₃ using RSM and ANN