Machining with a cutting edge with extensive damage or a fractured cutting edge significantly influences the machining performance. Therefore, investigations on tool wear behavior, their forms, and wear mechanisms will be very helpful in the current environment of sustainable manufacturing. On the other hand, the machining economy is negatively impacted by replacing the tool well before its useful life. This proactive maintenance planning reduces the risk of sudden tool failure and potential workpiece damage. Accordingly, the current work creates empirical and ANN models to predict flank wear growth for turning AISI 304 stainless steel using a MTCVD-TiCN/Al2O3 coated carbide tool. The experiments were designed to cover a broad range of operating conditions to ensure the model's accuracy and applicability in practical machining scenarios. An ANN was modeled using a feedforward backpropagation machine learning technique. In this study, a higher prediction accuracy of 0.9975 was achieved with ANN model as compared to the empirical model. The most common wear mechanism observed is metal adhesion, followed by fracture due to the pulling away of adhered material. The developed models have been found to be valuable for optimizing cutting parameters and enhancing tool life in machining.

Aluminum, one of the most abundant metals found on our planet, plays a crucial role in manufacturing as it is lightweight and resistant to corrosion and has excellent machinability. Of its numerous alloys, Al6061 is one of the most popular alloys used for CNC machining due to its superior mechanical and processing properties. This paper aims to investigate the impact of machining under dry and wet machining conditions. Correspondingly, the impact of dry machining on the material removal rate (MRR) and surface roughness (Ra) of Al6061 was evaluated. Machining was performed on a CNC Lathe. Two rods of Al6061 were used, and a dynamometer was attached to them to measure the radial, thrust, and tangential forces. In wet machining, the coolant used was a mixture of cutting oil and water. Different RPMs, feed rates, and depths of cut were entered into the machine as parameters. And the optimum parameters where found. This research utilizes particle swarm optimization approaches in order to evaluate optimal parameters, in contrast to traditional measurement methods such as contact profilometry or cutting force measurement. The results indicate that surface roughness rises with the depth of cut and feed rate. Ra rises by about 200% when dry machining is conducted at 0.05 mm/rev with increased depths of cut from 0.5 mm to 2.5 mm. In wet machining, the rise is much smaller, approximately 67% at 0.05 mm/rev and 30% at 0.25 mm/rev. Wet machining always produces more finished surfaces, decreasing Ra by 22–25% over dry machining. Wet machining is therefore better suited for achieving high-quality surface finish in Al6061 machining.