Optimization of turning operations with multiple performance characteristics

Nian, C.Y.; Yang, Wen-Hsi; Tarng, Y.S.

doi:10.1016/s0924-0136(99)00271-x

Cited by 208 publications

(92 citation statements)

References 6 publications

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“…Taguchi's approach to design of experiments is easy to be adopted and applyed for users with limited knowledge of statistics; hence it has gained a wide popularity in the engineering and scientific community. There have been plenty of recent applications of Taguchi techniques to materials processing for process optimization; some of the previous works are listed in references (Yang; Tarng, 1998) (Su et al, 1999) (Nian et al, 1999) (Lin, 2002) (Davim, 2003) (Ghani et al, 2004). In particular, it is recommended analyzing metal cutting problems to find the optimal combination of parameters (Ghani et al, 2004).…”

Section: Taguchi's Methodsmentioning

confidence: 99%

Taguchi's technique in machining of metal matrix composites

Shetty¹,

Pai²,

Rao³

et al. 2009

J. Braz. Soc. Mech. Sci. & Eng.

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Section: Taguchi's Methodsmentioning

confidence: 99%

Taguchi's technique in machining of metal matrix composites

Shetty¹,

Pai²,

Rao³

et al. 2009

J. Braz. Soc. Mech. Sci. & Eng.

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“…Gunay and Yucel (2013) used orthogonal array and analysis of variance (ANOVA) to evaluate the effect of cutting parameters on average surface roughness in turning high alloy white cast iron. Nian et al (1999) took the significant cutting parameters (cutting speed, feed rate and depth of cut) into consideration and used multi response signal-to-noise (MRSN) ratio technique to study the effect of these parameters on tool life, cutting force and surface finish. Davim (2003) studied the influence of cutting conditions like cutting velocity, feed rate and cutting time on turning metal matrix composites using orthogonal array and analysis of variance to investigate the cutting characteristics of flank wear (VB), power required (Pm) and surface roughness (Ra).…”

Section: Introductionmentioning

confidence: 99%

“…MRSN ratio method (Nian et al, 1999), WSN ratio method (Shiau, 1990;Tai et al, 1992), GRA method (Lin et al, 2009), and VIKOR method (Tong et al, 2007) are quite simpler/robust in accordance to their computational procedures. There are some other multiresponse optimization techniques (Sahin & Motorcu, 2008;Sardinas et al, 2006;Jeyapaul et al, 2006;Liao, 2006), which use complex mathematical/statistical tools and are, therefore, inconvenient to be used by the industrial personnel with little background in statistics and knowledge of software.…”

Section: Introductionmentioning

confidence: 99%

Selection of a suitable multiresponse optimization technique for turning operation

Nayak¹,

Rana²

2016

10.5267/j.dsl

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The present work deals with the comparison of four multi response optimization methods, viz. multiple response signal-to-noise (MRSN) ratio, weighted signal-to-noise (WSN) ratio, Grey relational analysis (GRA), and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian) methods taking a case study in turning mild steel specimen using HSS cutting tool. The various factors like cutting speed, feed rate, depth of cut and coolant flow rate are considered as the input process variables, while the material removal rate (MRR), surface roughness (SR) and specific energy consumption (SEC) are considered as various performance characteristics. One set of experimental data is analyzed using the standardized procedures. The optimization performances of these four methods are compared. The results show that MRSN ratio method proves to be the best optimization method. It is found that the feed rate has a highest impact on the overall performance as compared to other process parameters.

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“…Choudhury and EI-Baradie (1997) used RSM and 2 3 factorial designs for predicting surface roughness when turning high-strength steel. Nian et al (1999) used the Taguchi method with multiple performance characteristics in the optimization of turning operations. On the other hand, Lin et al (2001) used a network to construct a prediction model for surface roughness and cutting force.…”

Section: Introductionmentioning

confidence: 99%

Surface roughness model and parametric optimization in finish turning using coated carbide insert: Response surface methodology and Taguchi approach

Sahoo¹,

Sahoo²

2011

IJIEC

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This paper presents the experimental study, development of mathematical model and parametric optimization for surface roughness in turning D2 steel using TiN coated carbide insert using Taguchi parameter design and response surface methodology. The experimental plan and analysis was based on the Taguchi L 27 orthogonal array taking cutting speed (v), feed (f) and depth of cut (d) as important cutting parameters. The influence of the machining parameters on the surface finish has also been investigated and the optimum cutting condition for minimizing the surface roughness is evaluated. The optimal parametric combination for TiN coated cutting insert is found to be v3-f1-d3. The ANOVA result shows that feed the most significant process parameter on surface roughness followed by depth of cut. The cutting speed is found to be insignificant from the study. The RSM model shows good accuracy between predicted values and experimental values with 95% confidence intervals and adequate. It is concluded that the developed RSM model can be effectively utilized to predict the surface roughness in turning D2 steel.

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Optimization of turning operations with multiple performance characteristics

Cited by 208 publications

References 6 publications

Taguchi's technique in machining of metal matrix composites

Taguchi's technique in machining of metal matrix composites

Selection of a suitable multiresponse optimization technique for turning operation

Surface roughness model and parametric optimization in finish turning using coated carbide insert: Response surface methodology and Taguchi approach

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