Multi-Response Optimization of Process Parameter during Machining of C69300 Lead-Free Copper Alloy Using Multi-Objective Grey Wolf Algorithm

Abstract

In Machining Processes, Limiting The Use Of Resources Such As Energy, Tools, Price, And Production Time While Maximizing Process Outputs Such As Surface Quality And Productivity Has A Substantial Impact On The Environment, Process Sustainability, And Profit. This Study Addresses The Use Of Advanced Multi-Objective Grey Wolf Optimization Algorithm To Optimize Turning-Process Parameters, Specifically Cutting Speed, Feed Rate, And Depth Of Cut, In The Dry Machining Of C69300 Lead-Free Copper Alloy For A High-Efficiency Process. The C69300 Lead-Free Copper Alloy Has Strong Corrosion Resistance And Resistance To Stress Related Corrosion Cracking Which Makes This Material Applicable In Many Areas. Since C69300 Is Lead-Free, This Material Conforms To The International Safety Regulations. However, The Machining Of This Material Is Difficult Due To The Absence Of Lead In The Materials. In This Study, The L16 Taguchi Orthogonal Array Design Was Used To Conduct Sixteen Experimental Tests On A Cnc Machine In Which Cutting Temperatures, Material Removal Rate And Surface Roughness Was Determined As Main Responses Characteristics From Range Of Input Cutting Parameters (Cutting Speed, Feed Rate And Depth Of Cut). The Level Of Significance Of Machining Parameters Was Determined Using Analysis Of Variance (Anova). It Has Been Determined That The Most Significant Factor Influencing Surface Roughness Was Feed Rate, Whereas The Most Significant Factor Influencing The Cutting Temperature Was Cutting Speed. The Feed Rate Was The Most Significant Factor Affecting The Material Removal Rate. Next, The Regression Model Was Developed For Each Responses Using Minitab Software To Formulate The Relationships Between The Process Parameters And The Considered Three Response Parameters. The Multi-Objective Grey Wolf Optimization (Mogwo) Technique Was Employed To Simultaneously Optimize Surface Roughness, Material Removal Rate And Cutting Temperature. The Full Range Of Objective Functions Was Covered By Mogwo, And The Produced Pareto Front Was Quite Close To The Real Pareto Front. Using The Multi-Criteria Decision Making (Mcdm) Technique (Vikor Method), The Best Choice From The Pareto Solution Set Was Selected Based On The Higher Level Information. Higher-Level Information Was Implemented By Giving Equal Preference For Three Responses And The Obtained Solution Was Given As: Surface Roughness 3.7??M, Cutting Temperature 45.20c And Material Removal Rate 11859.85mm3/Min. The Corresponding Input Cutting Parameters Were Cutting Speed 64.6 M/Min, Feed Rate 0.15 Mm/Rev And Depth Of Cut 1.24 Mm. In Addition, Three Confirmatory Tests Were Performed To Evaluate The Performance Of The Derived Pareto Front Solutions. The Percentage Error Between Predicted And Experimental Values Was Less Than 10% For All Responses Investigated, Indicating That The Acquired Real Pareto Front Solutions Were Encouraging In Enhancing Machining Performance During Dry Turning Of C69300 Lead-FreeCopper Alloy.