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All the available Materials are manufactured from different different manufacturing processes like casting, forging and extrusion processes, which have higher typical dimension tolerances due to its producing ability. So the machining processes were introduced to achieve close tolerance assembly and improve the product quality. Today’s a lot of machining processes are available such as turning, milling, drilling and grinding to overcome these problems. This research work give particulars about the assessment of machining performance. The models was developed based on experiments on Mild steel (MS) material with spindle speed, feed rate and depth of cut as machining parameters. The needs of the manufacturer necessitate formulating the new objective function which consists of MRR and SR. Finally, the empirical equations of MRR and SR are used to formulate the Combined Objective Function (COF) and the Genetic Algorithm (GA) is used to optimize the face milling parameters. The best outcomes from GA were validated and it is recommended to manufacturing industries.

Concurrent Optimization and an Experimental Analysis of Face Milling Operation Parameters for Optimal Performance on Mild Steel Work Piece