Multi-Objective Optimization of Electrochemical Cut-off Grinding Process of Ti-6Al-4V using PCA based Grey Relational Analysis

The machining of titanium based hybrid composite by conventional method is very complicated because of its enhanced strength-to-weight ratio, resistance to corrosion, abrasion and fatigue and this hybrid composite is extensively necessary for automobile, aerospace, sports, spacecrafts, marine and bio-medical industries. In this paper, an investigation is presented based on a novel optimization algorithm named as desirable grey relational analysis (DGRA) where desirability function is coupled with grey relational analysis for multi-objective optimization (MOO). A novel titanium hybrid composite is developed by laser engineered net shaping (LENS) process. Experimental investigation is carried on wire electro-discharge machining (WEDM) process varying power, time off and peak current as the most important input process parameters. Fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) is proposed along with fuzzy analytical hierarchy process (FAHP) for criteria weights for the comparison of the experimental and the predicted results. Statistical investigation on response surface methodology (RSM) is carried on the Box-Behnken design (BBD) model using 3 factors and 3 levels design of experiments (DOE) on output responses like material removal rate (MRR), surface roughness (SR), kerf width (KW) and over cut (OC) to obtain satisfactory outcomes and then authenticated by confirmatory test. Analysis of variance (ANOVA) is used for significance of the models. Optimal condition and solution is attained by method of desirability to accomplish the best output response. This optimized result is further enhanced by 3.09%, 2.05% and 1.02% when compared with desirability to FTOPSIS, FTOPSIS to DGRA, and desirability to DGRA.

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Review on Single and Multi-objective Optimization Process Parameters of EDM Using Taguchi Method and Grey Relational Analysis

Materials Today Proceedings ◽

10.1016/j.matpr.2019.07.325 ◽

2019 ◽

Vol 18 ◽

pp. 3856-3866

Author(s):

Swarup S. Deshmukh ◽

Vijay S. Jadhav ◽

Ramakant Shrivastava

Keyword(s):

Taguchi Method ◽

Process Parameters ◽

Grey Relational Analysis ◽

Optimization Process ◽

Multi Objective Optimization ◽

Multi Objective ◽

Relational Analysis ◽

Grey Relational

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Multi-objective optimization of ciprofloxacin antibiotic removal from an aqueous phase with grey taguchi method

Journal of Water and Health ◽

10.2166/wh.2018.247 ◽

2018 ◽

Vol 16 (4) ◽

pp. 530-541 ◽

Cited By ~ 2

Author(s):

M. Salari ◽

G. R. Rakhshandehroo ◽

M. R. Nikoo

Keyword(s):

Taguchi Method ◽

Grey Relational Analysis ◽

Water Soluble ◽

H2o2 Concentration ◽

Multi Objective Optimization ◽

Anova Analysis ◽

Multi Objective ◽

Relational Analysis ◽

Grey Relational ◽

Grey Taguchi

Abstract Optimization methods are used to study and survey the optimal values for input factors and effect of optimized parameters on response variables. In this study, the effect of different factors on ciprofloxacin (CIP) removal of water soluble was studied. In this regard, a multi-objective optimization was performed utilizing the Taguchi method based on a grey relational analysis. Optimum levels of factors were determined to optimize three responses simultaneously with grey Taguchi. Meanwhile, grey relational analysis was applied to model and optimize three target responses, namely, CIP removal, chemical oxygen demand (COD) removal, and sludge to iron ratio. Multi-objective optimization results obtained based on grey relational analysis showed that the optimal value of the input factors were CIP concentration of 100 mg/L, H2O2 concentration of 100 mM, Fe(II) concentration of 10 mM, pH of 3, and a reaction time of 15 min. To confirm the results, the values obtained through a confirmation test were examined. Multi-objective optimization results from process factors were determined by analysis of variance (ANOVA) analysis and grey Taguchi method. Based on ANOVA analysis for the grey relational grade, Fe(II) concentration and H2O2 concentration were found to be the most influencing factors.

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