Optimization of turning operations with multiple performance characteristics using the Taguchi method and Grey relational analysis

Tzeng, Chorng‐Jyh; Lin, Yu‐Hsin; Yang, Yung‐Kuang; Jeng, Ming-Chang

doi:10.1016/j.jmatprotec.2008.06.046

Cited by 336 publications

(155 citation statements)

References 18 publications

Supporting

Mentioning

149

Contrasting

Unclassified

Order By: Relevance

“…The residual stresses and white layer have a great relationship with the type of the EDM dielectric [31,32]. Machining performance in the EDM process can be improved effectively through optimal machining parameters [33][34][35][36][37][38][39]. Using kerosene as dielectric will discharge large amounts of solid, liquid and gaseous wastes, which result in serious environmental problems.…”

Section: Introductionmentioning

confidence: 99%

Experimental study on green electrical discharge machining in tap water of Ti–6Al–4V and parameters optimization

Tang

2013

Int J Adv Manuf Technol

104

View full text Add to dashboard Cite

Ti-6Al-4V is widely used in the aerospace, automobile, and biomedical fields, but is a difficult-to-machine material. Electrical discharge machining (EDM) is regarded as one of the most effective approaches to machining Ti-6Al-4V alloy, since it is a non-contact electro-thermal machining method, and it is independent from the mechanical properties of the processed material. This paper aims to combine grey relational analysis and Taguchi methods to solve the problem of EDM parameters optimization. From the viewpoint of health and environment, tap water as working fluid has good working environment, since it does not release harmful gas. The process parameters include discharge current, gap voltage, lifting height, negative polarity and pulse duty factor. The electrode wear ratio (EWR), material removal rate (MRR) and surface roughness (SR) as objective parameters are chosen to evaluate the whole machining effects. Experiments were carried out based on Taguchi L 9 orthogonal array and grey relational analysis, and then verified the results through a confirmation experiment. Compared the machining parameters A 1 B 1 C 3 D 2 with A 1 B 2 C 2 D 2 , MRR increased from 1.28 mm 3 /min to 2.38 mm 3 /min, EWR decreased from 0.14 to 0.10 mm 3 /min and SR decreased from Ra 2.37 μm to Ra 1.93 μm. The process parameters sequenced in order of relative importance are: the ratio of pulse width to pulse interval, discharge current, lifting height and gap voltage. The results showed that using tap water machining Ti-6Al-4V material can obtain high MRR, decrease the machining cost and have no harmful to the operators and the environment.

show abstract

Section: Introductionmentioning

confidence: 99%

Experimental study on green electrical discharge machining in tap water of Ti–6Al–4V and parameters optimization

Tang

2013

Int J Adv Manuf Technol

104

View full text Add to dashboard Cite

show abstract

“…Tzeng et al [4] have used Grey relational analysis to perform optimization of turning operations with multiple performance characteristics such as roughness average, roughness maximum, and roundness. The depth of cut was identified to be the most influencing parameter affecting the Grey relational grade followed by cutting speed and feed rate.…”

Section: Introductionmentioning

confidence: 99%

Optimization of machining parameters for turning operation with multiple quality characteristics using Grey relational analysis

et al. 2016

View full text Add to dashboard Cite

Original scientific paper Optimization of machining processes is essential for achieving of higher productivity and high quality products in order to remain competitive. This study investigates multi-objective optimization of turning process for an optimal parametric combination to provide the minimum surface roughness (Ra) with the maximum material-removal rate (MRR) using the Grey-Based Taguchi method. Turning parameters considered are cutting speed, feed rate and depth of cut. Nine experimental runs based on Taguchi's L9 (3 4 ) orthogonal array were performed followed by the Grey relational analysis to solve the multiresponse optimization problem. Based on the Grey relational grade value, optimum levels of parameters have been identified. The significance of parameters on overall quality characteristics of the cutting process has been evaluated by the analysis of variance (ANOVA). The optimal parameter values obtained during the study have been validated by confirmation experiment. Keywords: ANOVA; Grey relational analysis; multi-objective optimization; Taguchi method; turning Optimizacija parametara obrade tokarenja s više kriterija kvalitete uporabom Grey relacijske analizeIzvorni znanstveni članak Optimizacija procesa obrade je neophodna za postizanje veće produktivnosti i visoke kvalitete proizvoda kako bi ostali tržišno konkurentni. Ovaj rad istražuje više-kriterijsku optimizaciju procesa tokarenja s optimalnom kombinacijom parametara obrade koji osiguravaju minimalnu hrapavost površine (Ra) s maksimalnim učinkom uklanjanja materijala (MRR) uporabom Grey-based Taguchi metode. Razmatrani parametri obrade tokarenjem su brzina rezanja, posmak i dubina rezanja. Primjenom Taguchijevog L9 (3 4 ) ortogonalnog plana provedeno je devet eksperimenata te je korištena Grey relacijska analiza kako bi se riješio višekriterijski problem optimizacije. Temeljem vrijednosti Grey relacijskog stupnja utvrđene su optimalne razine parametara. Signifikantnost parametara na sveukupne kriterije kvalitete procesa tokarenja ocijenjena je analizom varijance (ANOVA). Optimalne vrijednosti parametara dobivene tijekom istraživanja potvrđene su verifikacijskim eksperimentom.

show abstract

“…Tong et al (2007) proposed two case studies of plasma enhanced chemical vapor deposition and copper chemical mechanical polishing using VIKOR method, which was a compromise ranking approach used for multi-criteria decision making (MCDM), for optimizing multiple responses. Lin et al (2009) proposed the Grey Relational Analysis technique to optimize the CNC turning operation parameters for SKD11 (JIS). The depth of cut was found to have most impact on the average roughness and the cutting speed was the most influential factor for the roughness and the roundness.…”

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

View full text Add to dashboard Cite

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.

show abstract

Optimization of turning operations with multiple performance characteristics using the Taguchi method and Grey relational analysis

Cited by 336 publications

References 18 publications

Experimental study on green electrical discharge machining in tap water of Ti–6Al–4V and parameters optimization

Experimental study on green electrical discharge machining in tap water of Ti–6Al–4V and parameters optimization

Optimization of machining parameters for turning operation with multiple quality characteristics using Grey relational analysis

Selection of a suitable multiresponse optimization technique for turning operation

Contact Info

Product

Resources

About