Experimental And Numerical Investigation Of Dry Turning Aisi 1030 Carbon Steel

Abstract

Nowadays, Modern Metal Industries Have Difficulty Obtaining The Required Surface Quality During Machining. This Is Because Various Process Parameters Have An Impact On The Quality Of The Surface Finish. As A Result, Optimizing The Turning Machining Parameters Such As Cutting Speed, Depth Of Cut And Feed Is Crucial To Improve Surface Finish That Minimizes Mechanical Failures Caused By Wear, And Corrosion, And Increases The Productivity Of The Products. In Addition To This, To Minimize The Limitations Of Experimental Studies Of Metal Cutting, Finite Element Simulation Is Used, Which Improves Time And Cost. This Study Aims To Investigate The Dry Turning Of Aisi 1030 Carbon Steel Material Experimentally And Numerically Using Finite Element Simulation, As Well As Optimizing Cutting Parameters (Cutting Speed, Depth Of Cut, And Feed) To Get A Better Surface Finish, Reduce Tool Wear, Minimize Tool Temperature And Maximize Material Removal Rate. The Experiment Was Designed Using A Taguchi L9 Orthogonal Array, And An Analysis Of Variance Was Used To Determine The Significance Of Cutting Parameters On End Responses. Finally, Multi-Response Optimization Using Grey Relational Analysis Was Done, And An Optimal Parameter Combination For The Turning Operation Was Obtained Via This Grey Relational Analysis. According To The Results, Cutting Speed (67.21%) Was The Most Significant ParameterWhich Influences Surface Roughness Followed By Feed Rate (26.91%). At 250 M/Min Cutting Speed, 0.5 Mm Depth Of Cut, And 0.15 Mm/Rev Feed Rate, The Lowest Surface Roughness (1.21 ??M) Was Achieved. The Depth Of Cut Had A Significant Impact On The Material Removal Rate (66.13%), And The Optimum Material Removal Rate Was 453.57 Mm3/S, Which Was Attained At 90 M/Min, 2.5 Mm, And 0.35 Mm/Rev. With An Average Relative Error Of 4.85%, The Taguchi Prediction And Finite Element Simulation Were In Fair Deal With The Experimental Finding. Tool Temperature And Tool Wear Rate Were Significantly Affected By Cutting Speed With A Contribution Of 88.05% And 77.22%, Respectively. A Lower Tool Temperature (78.50c) And A Lower Tool Wear Rate (0.01494mm3/S) Were Obtained At The Lower Level Of Parameters. Predicting And Simulation Of Tool Temperature And Tool Wear Rate Was Closely Related To The Experimental Results, With Average Relative Errors Of 5.58% And 6.69%, Respectively. Cutting Speed Has The Greatest Influence On Multiple Responses Of Grey Relational Analysis, With A Significance Of 56.85%. The Optimum Parametric Combination Of Multi-Responses Is 90 M/Min, 0.25 Mm, And 0.15 Mm/Rev.