AFM surface investigation of Inconel 825 with multi wall carbon nano tube in electrical discharge machining process using Taguchi analysis

Prabhu, S.; Vinayagam, B. K.

doi:10.1016/s1644-9665(12)60180-0

Cited by 67 publications

(22 citation statements)

References 13 publications

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“…The machining was conducted with Taguchi L 9 orthogonal array; based on the S/N ratio analysis the optimal process parameters for surface roughness and MRR were calculated separately. Prabhu and Vinayagam (2011a) proposed that carbon nanotube (CNT) be mixed with dielectric fluid in EDM process because of high thermal conductivity. An analysis of surface characteristics like surface roughness and micro cracks of Inconel-825 was carried out and an excellent machined nano finish was obtained by setting the machining parameters at optimum level.…”

Section: Previous Workmentioning

confidence: 99%

Analysis of multi objective optimisation using TOPSIS method in EDM process with CNT infused copper electrode

Raj

Prabhu

2017

IJMMM

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This paper presents the experimental investigation of the machining characteristics of AISI D2 tool steel on the electrical discharge machining (EDM) process by replacing the copper electrode tool with carbon nanotube (CNT) fused electrode, and checking for improvement in machinability factors like material removal rate (MRR), electrode wear rate (EWR) and surface finish (SR). The problems of high electrode wear in EDM machining, such as poor dimensional and shape accuracy of the machined part, need to be addressed. The work material is spark machined with pure copper and CNT-infused copper electrodes by varying parameters on an EDM. The results of the machining characteristics with and without the CNT are compared and analysed using Taguchi analysis. This study investigates the optimisation of EDM parameters for AISI D2 tool steel using TOPSIS method. Analysis of variance (ANOVA) and F-test are used to check the validity of regression model and determine the significant parameter affecting the surface roughness.

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Section: Previous Workmentioning

confidence: 99%

Analysis of multi objective optimisation using TOPSIS method in EDM process with CNT infused copper electrode

Raj

Prabhu

2017

IJMMM

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“…Analysis with the application of D dimension to the examination of differently machined profiles presented in Figs. [8][9][10][11] shows that the most complex profiles were obtained using carbon nanotube nanofluids with pulse current 5 A, pulse duration 3 µs, pulse voltage 80 V, D = 1.424. On the other hand, the lowest D values were reached after machining with carbon nanotube nanofluids with pulse current 8 A, pulse duration 1 µs, pulse voltage 100 V, D = 0.9106.…”

Section: Fractal Analysismentioning

confidence: 99%

“…Prabhu and Vinayagam [10] proposed that Carbon nanotube (CNT) be mixed with dielectric fluid in EDM process because of high thermal conductivity. An analysis of surface characteristics, like surface roughness, micro cracks of Inconel-825, was carried out, and an excellent machined nano finish was obtained by setting the machining parameters at optimum level.…”

Section: Introductionmentioning

confidence: 99%

Multiresponse Optimization of Edm Process with Nanofluids Using Topsis Method and Genetic Algorithm

Prabhu¹,

Vinayagam²

2016

Archive of Mechanical Engineering

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Electrical Discharge Machining (EDM) process with copper tool electrode is used to investigate the machining characteristics of AISI D2 tool steel material. The multi-wall carbon nanotube is mixed with dielectric fluids and its end characteristics like surface roughness, fractal dimension and metal removal rate (MRR) are analysed. In this EDM process, regression model is developed to predict surface roughness. The collection of experimental data is by using L9 Orthogonal Array. This study investigates the optimization of EDM machining parameters for AISI D2 Tool steel using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Analysis of variance (ANOVA) and F-test are used to check the validity of the regression model and to determine the significant parameter affecting the surface roughness. Atomic Force Microscope (AFM) is used to capture the machined image at micro size and using spectroscopy software the surface roughness and fractal dimensions are analysed. Later, the parameters are optimized using MINITAB 15 software, and regression equation is compared with the actual measurements of machining process parameters. The developed mathematical model is further coupled with Genetic Algorithm (GA) to determine the optimum conditions leading to the minimum surface roughness value of the workpiece.

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“…The ratio I D /I G can be converted to an S/N ratio as follows 33) : Figure 4 shows the average S/N ratio corresponding to the level of each of the variables, as calculated using I D /I G . Figure 4 shows I D /I G for temperatures of 875 C and 900 C. The DOE samples D1, D2, D7, and D8 were processed at 875 C Fig.…”

Section: Cvd Process Conditionsmentioning

confidence: 99%

“…For a given parameter, the optimum result can be characterized in three ways: either smaller is better, on-target is better, or larger is better. For the rst case, where smaller is better, a smaller experimental result represents higher quality 33) . For the second case, where nominal is better, good results are generally obtained when the result is closer to the target, with minimal variance.…”

Section: Introductionmentioning

confidence: 99%

Application of the Taguchi Method to Optimize Graphene Coatings on Copper Nanoparticles Formed Using a Solid Carbon Source

et al. 2016

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Graphene has attracted much recent interest as an electronic material due to its large electron mobility. Large-area graphene has been synthesized using chemical vapor deposition (CVD). However, it is dif cult to apply this process to grow graphene on nanoparticles (NPs) because of their small radius of curvature, which results in a large defect density. In this work, we used the Taguchi method to optimize the deposition of graphene on nanoparticles. We used polyvinylpyrrolidone (PVP) to coat copper NPs via CVD and optimized the process conditions using a minimal number of experiments. The PVP served as the solid carbon source, forming graphene when heated to 875 C. To improve the quality of the graphene coatings on the Cu NPs, the following process parameters were varied: gas conditions (ratio of Ar to H 2 ), process time and temperature, the amount of PVP solution, and the molecular weight of PVP. We identi ed optimal process conditions using only eight experiments. Raman spectroscopy was used to analyze the quality of the graphene coatings by comparing two-dimensional (2D) spectra and I D / I G ratios of the different coatings. A decrease in I D /I G , in combination with sharper Raman bands, is indicative of the thickness and crystal quality of the graphene layer. The quality of the graphene layer was also evaluated using transmission electron microscopy (TEM) and scanning electron microscopy (SEM).The optimal conditions for the formation of graphene-coated Cu NPs were: a temperature of 875 C, a deposition time of 2 minutes, an Ar-to-H 2 ratio of 1:1, PVP with a molecular weight of M W = 3,500 (K-12) during the polyol process, and a 50-wt.% PVP solution with M W = 45,000 (K-30). Using the Taguchi method, we identi ed trends relating defect density versus process conditions and successfully obtained a graphene coating with a minimal defect density.

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AFM surface investigation of Inconel 825 with multi wall carbon nano tube in electrical discharge machining process using Taguchi analysis

Cited by 67 publications

References 13 publications

Analysis of multi objective optimisation using TOPSIS method in EDM process with CNT infused copper electrode

Analysis of multi objective optimisation using TOPSIS method in EDM process with CNT infused copper electrode

Multiresponse Optimization of Edm Process with Nanofluids Using Topsis Method and Genetic Algorithm

Application of the Taguchi Method to Optimize Graphene Coatings on Copper Nanoparticles Formed Using a Solid Carbon Source

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