Statistical and experimental investigation using a novel multi-objective optimization algorithm on a novel titanium hybrid composite developed by lens process

Abstract

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.