Mapping of Strategic Operating Conditions for End Milling Super-Transus Heat-Treated Ti1023 Alloy Using Multi-Objective Optimization

The study addresses the machinability of heat-treated Ti1023 titanium alloy and maps strategic operating conditions for reduced cutting forces, improved surface roughness, and elevated material removal rates during end milling operations. An experimental study employing a full factorial design is utilized to investigate the effects of milling process variables, cutting speed, feed rate, and axial depth of cut, on the machinability indices, namely cutting force, surface roughness, and material removal rate. Correlations between the milling process parameters and the machinability indices were established based on the experimental data, employing a least-squares method for nonlinear modeling. The formulation of a multi-objective optimization problem was undertaken with the aim of improving the machinability of the heat-treated Ti1023 alloy, considering the objectives of reduced cutting forces, improved surface roughness, and maximum material removal rate. A population-based multi-objective multiverse optimization (MOMVO) algorithm was implemented to generate Pareto optimal solutions and compared with non-dominated sorting genetic algorithm II and feasibility enhanced particle swarm optimization. The findings highlight the efficacy of the MOMVO in exploring diverse solutions across the solution space. The Pareto optimal solutions were ranked on their desirability and used to map optimal operating conditions, providing an intuitive guide for practitioners to determine appropriate parameters for efficient and effective milling of heat-treated Ti1023 alloys for aerospace applications.