Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining

Garg, Girish Kant; Sangwan, Kuldip Singh

doi:10.1016/j.jclepro.2014.07.073

Cited by 255 publications

(125 citation statements)

References 26 publications

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“…The total sum of squares deviations (SS T ) can be used to evaluate the importance of the cutting parameters change on the performance characteristic. F-value which is the ratio of mean square to the mean square error, is used to measure the significance of the model under investigation with respect to the variance of all the terms including the error term at the desired significance level usually, F > 4 means that change of the design parameters has a significant effect on performance characteristic (Kant and Sangwan, 2014). From Table 8, it can be observed that the feed rate is most influential with 54.91% contribution followed by temperature of workpiece at 19.56% contribution, cutting speed and depth of cut has less influence with 0.43%, 0.72%.…”

Section: Analysis Of Variancementioning

confidence: 99%

Multi objective optimisation of thermally enhanced machining parameters of Inconel 718 using grey relational analysis

Ganta

Sagar

Chakradhar

2017

IJMMM

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Abstract:The present work investigates an experimental study of thermally enhanced machining of nickel-based superalloy Inconel 718 using uncoated tungsten carbide inserts. An inexpensive flame heating technique using an oxyacetylene flame is used as a heat source for thermal enhancement of workpiece. The effects of cutting parameters like cutting speed, feed rate, depth of cut and temperature of workpiece on the performance characteristics like surface roughness, tool wear and material removal rate were studied. A L 27 orthogonal array with four parameters and three levels was adopted for experimental design. Multi response optimisation was done using grey relation analysis to simultaneously minimise surface roughness, tool wear and to maximise material removal rate. It was observed that at cutting speed at 85.21 m/min, feed rate at 0.048 m/min, depth of cut at 0.6 mm and workpiece temperature at 600°C were optimal cutting parameters. It is clearly shown that the above performance characteristics in thermally enhanced machining can be improved effectively through this approach.Keywords: thermally enhanced machining; surface roughness; tool wear; metal removal rate; MRR; Taguchi-based grey relation analysis; ANOVA.Reference to this paper should be made as follows: Ganta, V., Srinivasa Sagar, K. and Chakradhar, D. (2017) 'Multi objective optimisation of thermally enhanced machining parameters of Inconel 718 using grey relational analysis', Int.

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Section: Analysis Of Variancementioning

confidence: 99%

Multi objective optimisation of thermally enhanced machining parameters of Inconel 718 using grey relational analysis

Ganta

Sagar

Chakradhar

2017

IJMMM

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“…Uygun kesme parametrelerinin seçilmediği durumlarda oluşan titreşimler, tezgâha ve kesici takıma gelen yükü arttıracağı için elde edilen yüzeyin de daha pürüzlü olmasına sebeptir [17]. Dolayısıyla araştırmacılar pürüzlülüğün ölçülmesiyle optimize edebilecekleri kesme parametrelerini oluşan titreşimle beraber değerlendirdiklerinde daha verimli sonuçlara ulaşmışlardır [18][19][20]. Görüldüğü üzere, farklı malzemelere farklı kesme parametreleri kullanılarak talaş kaldırma işlemleri gerçekleştirilmiştir.…”

Section: Gi̇ri̇ş(introduction)unclassified

34CrNiMo6 Çeliğinin Frezelenmesinde Kesme Parametrelerinin Titreşim Ve Yüzey Pürüzlülüğüne Etkilerinin İncelenmesi

Erbaş¹,

Güllü²

2018

Journal of Polytechnic

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“…Yang et al [9] applied the Taguchi method and the GRA to optimize the milling parameters such as the cutting speed, the feed rate, and the depth of cut for simultaneous optimization of the energy, production rate and cutting quality. Kant and Sangwan [10] using grey relational analysis to find the optimum values of machining parameters to achieve the minimum power consumption and surface roughness. Hwang et al [11] investigated and optimized the high speed end milling of SKD61 Tool Steel using Taguchi methods with grey relational analysis.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of in Precision Turn-Boring Parameters for AA7050-T7451 with Multiple Performance Characteristics

Sj¹,

Tsai²

2017

Preprint

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The present study propose an innovative turn-boring operation method and focuses on finding optimal turn-boring process parameters for AA7050-T7451 by considering multiple performance characteristics using Taguchi orthogonal array with the grey relational analysis, the effect of cutting variables such as, feed rate, depth of cut and cutting speed are optimized with considerations of multiple performance characteristics namely surface roughness, roundness error, material removal rate and power consumption the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) is proved that the most significant factor is cutting speed, followed by feed rate, radial depth of cut. Finally, confirmation tests were performed to make a comparison between the experimental results. Experimental results have shown that machining performance in precision turn-boring process can be improved effectively through this approach.

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Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining

Cited by 255 publications

References 26 publications

Multi objective optimisation of thermally enhanced machining parameters of Inconel 718 using grey relational analysis

Multi objective optimisation of thermally enhanced machining parameters of Inconel 718 using grey relational analysis

34CrNiMo6 Çeliğinin Frezelenmesinde Kesme Parametrelerinin Titreşim Ve Yüzey Pürüzlülüğüne Etkilerinin İncelenmesi

Multi-Objective Optimization of in Precision Turn-Boring Parameters for AA7050-T7451 with Multiple Performance Characteristics

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