Optimization of Dry Machining Parameters for High-Purity Graphite in End-Milling Process

Yang, Yung‐Kuang; Shie, Jie‐Ren; Huang, Cheng‐Hung

doi:10.1080/03602550600728141

Cited by 78 publications

(31 citation statements)

References 7 publications

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“…Hence, in such cases statistical optimization methods may work better. Few researchers tried optimization of surface roughness as a response using grey relational analysis (Yang et al, 2006) and response surface method (Routara et al, 2009). However, it is observed that multi-objective optimization is mostly attempted by earlier researchers using statistical optimization methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Process parameter optimization based on principal components analysis during machining of hardened steel

Chandgude¹,

Pawar²,

Sadaiah³

2015

10.5267/j.ijiec

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The optimum selection of process parameters has played an important role for improving the surface finish, minimizing tool wear, increasing material removal rate and reducing machining time of any machining process. In this paper, optimum parameters while machining AISI D2 hardened steel using solid carbide TiAlN coated end mill has been investigated. For optimization of process parameters along with multiple quality characteristics, principal components analysis method has been adopted in this work. The confirmation experiments have revealed that to improve performance of cutting; principal components analysis method would be a useful tool.

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Section: Literature Reviewmentioning

confidence: 99%

Process parameter optimization based on principal components analysis during machining of hardened steel

Chandgude¹,

Pawar²,

Sadaiah³

2015

10.5267/j.ijiec

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“…The grey relational grade also indicates the degree of influence that the comparability sequence can exert over the reference sequence. Therefore, if a particular comparability sequence is more important than the other comparability sequences to the reference sequence, the grey relational grade for that comparability sequence and reference sequence will be higher than other grey relational grades [27]. In this study, the corresponding weighting values are obtained from PCA.…”

Section: Gramentioning

confidence: 99%

Hybrid Integration of Taguchi Parametric Design, Grey Relational Analysis, and Principal Component Analysis Optimization for Plastic Gear Production

Mehat

Kamaruddin

Othman

2014

Chinese Journal of Engineering

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The identification of optimal processing parameters is an important practice in the plastic injection moulding industry because of the significant effect of such parameters on plastic part quality and cost. However, the optimization design of injection moulding process parameters can be difficult because more than one quality characteristic is used in the evaluation. This study systematically develops a hybrid optimization method for multiple quality characteristics by integrating the Taguchi parameter design, grey relational analysis, and principal component analysis. A plastic gear is used to demonstrate the efficiency and validity of the proposed hybrid optimization method in controlling all influential injection moulding processing parameters during plastic gear manufacturing. To minimize the shrinkage behaviour in tooth thickness, addendum circle, and dedendum circle of moulded gear, the optimal combination of different process parameters is determined. The case study demonstrates that the proposed optimization method can produce plastic-moulded gear with minimum shrinkage behaviour of 1.8%, 1.53%, and 2.42% in tooth thickness, addendum circle, and dedendum circle, respectively; these values are less than the values in the main experiment. Therefore, shrinkage-related defects that lead to severe failure in plastic gears can be effectively minimized while satisfying the demand of the global plastic gear industry.

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“…Therefore, if a particular comparability sequence is more important than the other comparability sequence for the reference sequence, then the Grey relational grade for that comparability sequence and the reference sequence is higher than the other Grey relational grade. 31,32 With this technique, both the comparability sequence and the reference sequence are of equal preference. Table 6 shows the values of MRRs and their S/N ratios.…”

Section: Grey Relational Analysis (Gra)mentioning

confidence: 99%

Optimization of the parameters for the surfactant-added EDM of a Ti–6Al–4V alloy using the GRA-Taguchi method

Kolli¹,

Kumar²

2016

Mater. Tehnol.

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In this research, the Taguchi technique was applied to determine the optimum process parameters for the electrical discharge machining (EDM) of a titanium alloy with an added surfactant. The surfactant added to the dielectric fluid played an important role in the discharge gap, increasing the conductivity and suspending debris particles in the dielectric fluid, reducing abnormal discharge conditions of the machine and improving the overall machining efficiency. The performance characteristics were the material-removal rate (MRR) and the surface roughness (SR), which were experimentally explored for various input parameters such as the discharge current, the pulse-on time, the pulse-off time and the surfactant concentration in the dielectric fluid. The optimum setting of the parameters was verified through planned and conducted experiments and analysed using the Taguchi technique. Further, a multi-response optimisation was carried out, maximising the MRR and minimising the SR, using the Grey relational analysis (GRA). It was observed that the pulse-on time, the discharge current, the pulse-off time and the surfactant concentration contribute significantly to the multi-response optimisation. Conformation test results showed an improvement in the MRR by 20.69 % and in the SR by 11.09 %. A scanning-electron-microscope (SEM) analysis was conducted to study the recast layer that evolved during the electrical discharge machining process and the topography of surfaces was also observed. Keywords: surfactant, Ti-6Al-4V alloy, Grey-Taguchi method, material-removal rate, surface roughness, recast-layer thickness V~lanku je uporabljena Taguchi metoda za dolo~anje optimalnih parametrov procesa povr{insko aktivne elektroerozije (EDM) na titanovi zlitini. Povr{insko aktivne snovi, dodane dielektri~ni teko~ini, igrajo pomembno vlogo pri razelektritveni re`i, kar pove~a prevodnost in ustavi delce v dielektri~ni teko~ini, zmanj{a neobi~ajne razelektritve pri obdelavi in na splo{no pove~a u~inkovitost obdelave. Zna~ilnosti zmogljivosti sta hitrost odstranjevanja materiala (MRR) in hrapavost povr{ine (SR), ki sta bili eksperimentalno uporabljeni za razli~ne vhodne parametre, kot je: tok razelektritve, trajanje impulza, trajanje prekinitve in koncentracija povr{insko aktivne snovi v dielektri~ni teko~ini. Optimalna postavitev parametrov je bila preizku{ena z na~rtovanimi preizkusi in analizirana s Taguchi metodo. Izvr{ena je bila optimizacija z ve~odgovori, to je maksimiranje MRR in minimiziranje SR s pomo~jo uporabe Grey relacijske analize (GRA). Ugotovljeno je, da tok razelektritve, trajanje pulza,~as brez pulza in koncentracija povr{insko aktivne snovi, mo~no prispevajo k optimizaciji. Rezultati potrditvenih preizkusov so pokazali izbolj{anje MRR za 20,69 % in SR za 11,09 %. Z vrsti~nim elektronskim mikroskopom (SEM) je bila izvr{ena analiza (SEM) pretaljenega sloja, ki se je razvil med postopkom elektroerozije, opazovana pa je bila tudi topografija povr{ine. Klju~ne besede: povr{insko aktivne snovi, Ti-6Al-4V zlitina, Grey-Taguc...

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Optimization of Dry Machining Parameters for High-Purity Graphite in End-Milling Process

Cited by 78 publications

References 7 publications

Process parameter optimization based on principal components analysis during machining of hardened steel

Process parameter optimization based on principal components analysis during machining of hardened steel

Hybrid Integration of Taguchi Parametric Design, Grey Relational Analysis, and Principal Component Analysis Optimization for Plastic Gear Production

Optimization of the parameters for the surfactant-added EDM of a Ti–6Al–4V alloy using the GRA-Taguchi method

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