A post-Pareto decision approach for machining AISI H13 steel using PCBN 7025 W tools based on multi-dimensional performance modeling

This study proposes a structured decision-support framework for machining operations involving multiple and conflicting performance objectives. The approach integrates multi-objective optimization using the Varimax Rotated Factor-Normal Boundary Intersection method with a post-Pareto decision stage designed to identify the most appropriate solution among Pareto-optimal alternatives. Principal Component Analysis and Factor Analysis, combined with Varimax rotation, are employed to reorganize eight correlated response variables into three independent and interpretable performance dimensions representing Reliability, Economic Behavior, and Quality. This transformation enhances the stability of the search space and improves the robustness of the optimization process. In the post-optimization stage, the Pareto-optimal solutions are evaluated using the Shannon’s Entropy/Global Percentage Error, allowing a systematic and multi-criteria assessment of the solution set. A sensitivity analysis based on the variation of the weighting parameter indicates that the decision-making process is stable and robust with respect to preference changes. In addition, a comparative analysis was conducted with well-established multi-objective optimization methods, including the Non-dominated Sorting Genetic Algorithm II, the Multi-Objective Evolutionary Algorithm based on Decomposition, the Weighted Sum Method, and the Multi-Objective Lichtenberg Algorithm, using the Hypervolume, Inverted Generational Distance and Spacing metrics. The results show that, while traditional methods exhibit strong capability in exploring the Pareto front, the proposed framework provides more uniformly distributed solutions, along with a structured mechanism for selecting the final solution, in addition to lower computational time and cost. A case study on the hard turning of AISI H13 steel using PCBN 7025W inserts confirms the applicability of the proposed framework, optimizing cutting speed, feed rate, and depth of cut with respect to tool life, economic performance, and surface integrity. The proposed method identifies balanced parameter configurations while reducing reliance on subjective decision-making, thereby supporting decision-making in multi-objective contexts within manufacturing environments.