Experimental Investigation and Artificial Neural Network Parametric Optimization of CNC Drilling Process on Inconel 718 Material

Abstract

The Market Demand For Super Alloy Components At Reduced Prices, Superior Surface Finish And Reduced Machining Cost Has Been An Occasionally Increasing. Particularly For NickelBased Super Alloys Like Inconel 718 That Has Come To Be The Preferred Material For Products Used In High Temperature Conditions Due To Its Excellent Characteristics. However, Machining Of This Alloy Specifically, Drilling Process Could Be Troublesome When Cutting Is In Progress Due To Its Operational Nature And Poor Machinability Of Inconel 718 Because Of Their Low Heat Conductivity, Superior Strength And High Hardness. This Work Was To Analyse The Influence Of Process Parameters Such As Spindle Speed, Feed Rate And Drill Bit Diameter On The Response Parameters Of Surface Roughness, Material Removal Rate And Tool Wear. The Experiments Were Performed On A Cnc Machine Using A Work Piece Of Inconel 718 That Was Utilized In The Aerospace, Automotive And Gas Turbine Industries. A Taguchi Combined Artificial Neural Network (Ann) Model Was Utilized In Matlab R2022 To Forecast And Optimize The Input Parameters On The Responses. Analysis Of Variance And S/N Ratio Were Utilized To Verify The Validity Of The Constructed Model For All Outcomes And Multi-Objective Of The Responses Forecasted By Ann. Feed Rate Was Identified To Contribute 74.312% Which Was The Most Significant Factor Affecting Surface Roughness. The Minimum Surface Roughness Of 0.60??M Was Realized At The Highest Speed Of 510 Rpm, Lowest Value Of Feed Rate Of 8.5 Mm/Min And Smallest Diameter Of 6mm.Feed Rate Made The Maximum Contribution Towards Material Removal Rate (50%), Followed By Drill Bit Diameter (42.30%). Minimum Tool Wear Was Achieved With The Minimum Drilling Parameters, And A Predominant Effect Was Made By Drill Bit Diameter (57.8%) Followed By Feed Rate (36.7%). The Experimental Findings Indicate That Ann Has A Valid Prediction Capability, And The Optimum Inputs And Optimum Values Of Responses Obtained Through Ann Modelling In The Case Of This Experimental Investigation On Cnc Drilling Of Inconel 718 Were Spindle Speed Of 425 Rpm, Feed Rate Of 30.6 Mm/Min And Diameter Of The Drill Bit Of 10mm And The Responses Obtained At These Parameters Were Surface Roughness Of 0.7917??M, Tool Wear Of 0.1365 Mm And Material Removal Rate Of 1942.42 Mm3/Min. Thus, It Can Be Concluded That The Results Obtained From The Ann Model Are Statistically Significant, Slightly Superior To The Classical Model, And A Good Fit.