Multi-Objective Optimisation of CNC Turning Parameters for Surface Quality and Tool Wear Minimisation in Machining Locally Produced Austenitic Stainless Steel

The machinability of austenitic stainless steel presents persistent challenges for precision machining operations in Nigerian manufacturing facilities, where work hardening tendencies, high cutting forces, and rapid tool wear impose significant cost penalties and dimensional accuracy limitations on components produced for the oil and gas, food processing, and pharmaceutical equipment sectors. This study conducts a multi-objective optimisation of CNC turning parameters for machining locally produced Type 316L austenitic stainless steel bars sourced from a steel processing facility in Abeokuta, Ogun State. A Box-Behnken design experiment was implemented, varying cutting speed from 80 to 160 metres per minute, feed rate from 0.1 to 0.3 millimetres per revolution, and depth of cut from 0.5 to 1.5 millimetres. Responses measured were surface roughness Ra, cutting force, and tool flank wear after a standardised cutting distance of 500 metres using coated carbide insert tools. Response surface models were developed for each response, and multi-objective optimisation was performed using the non-dominated sorting genetic algorithm II to identify the Pareto-optimal front of parameter combinations. The optimal compromise solution minimising surface roughness and tool wear simultaneously was identified at a cutting speed of 140 metres per minute, feed rate of 0.12 mm/rev, and depth of cut of 0.75 millimetres, yielding Ra of 0.72 micrometres and tool wear of 0.18 millimetres. Confirmation experiments validated predicted outcomes within 6 percent. Keywords: CNC turning optimisation, austenitic stainless steel, surface roughness, tool wear, multi-objective optimisation.