Multi-objective optimization of surface roughness and MRR in AISI 316L stainless steel processed by MQL end milling using taguchi, RSM, ANN, and RFR methods

This research improves the cutting parameters for end milling AISI 316L stainless steel, a material that is utilized in a variety of sectors, including nuclear power, food, medicine, chemicals, and the marine sector. It has remarkable corrosion resistance. Its great mechanical qualities and limited heat conduction make it challenging to manufacture. When milling with neem oil under Minimum Quantity Lubrication (MQL), the Taguchi technique was utilized to choose the cutting parameters, with an emphasis on Surface Roughness (Ra) and Material Removal Rate (MRR). Important factors such as feed rates, cutting speeds, and cut depths were examined, as well as morphological changes and chip formation. Tool dynamometers were used to quantify MRR, and a surface finish tester was used to evaluate surface roughness. The cutting parameters were optimized and validated using advanced optimization techniques such as Random Forest Regression (RFR), Back Propagation Artificial Neural Network (BPANN), Feed Forward Artificial Neural Network (FFANN), Desirability Function Analysis (DFA), Taguchi Design of Experiments (TDOE), and Response Surface Methodology (RSM). The findings show that machining efficiency is greatly impacted by Material Removal Rate (MRR). While MQL utilizes a prepared Neem oil enhanced tool life and surface quality, higher cutting speeds, feed velocities, and depths of cut increased MRR. At 150 m/min cutting speed, 250 mm/min feed velocity, and 2 mm depth of cut, the best MRR was obtained. At moderate feed velocities, shallow cuts, and medium cutting speeds (100 m/min), surface roughness was reduced. MRR and surface roughness were successfully predicted by the RSM, BPANN, FFANN, and RFR models; RFR proved to be the most accurate.