Multi-objective optimization of surface morphology using fractal and multi-fractal analysis for dry milling of AISI 4340

Abstract

Machining of difficult-to-cut metals is typically associated with inconsistent machining quality, high machining costs, and significant environmental pollution. This work proposes a novel method that utilizes fractal and multi-fractal features of the machined surface morphology to evaluate surface quality in high-speed dry milling of 4340 steel. The results demonstrate that the fractal dimension can be used as an evaluation index for machined surface defects. It accurately characterizes the contour information of machined surface defects, including voids, tears, and material side flow. A smaller fractal dimension indicates fewer surface defects and represents a higher-quality machined surface. However, the surface defects cannot be described using the traditional evaluation method based solely on surface roughness (Ra). Moreover, multi-objective optimization of high-speed milling of 4340 steel is achieved by using the combined indices Ra and fractal features as responses. The Ra generated using the optimized parameters can reach 97 nm, which represents a reduction of 4.9 % compared to the non-optimization state. And typical defects on machined surfaces virtually disappear. Meanwhile, the machining cost has been reduced by 2.52 % and the machining efficiency has increased by 31.45 %. The results demonstrate that the proposed surface quality evaluation method is possible for practical engineering applications to achieve high-quality, high-efficiency and low-cost high-speed dry milling of high-strength steels.