In a competitive industrial setting, optimizing machining processes is important for enhancing surface quality and productivity. This study focuses on optimizing pocket milling parameters for 5083 H111 aluminum alloy using three toolpath strategies: Zig-Zag, Parallel Spiral, and One-Way. To achieve these goals, the Taguchi method, Grey Relational Analysis (GRA), ANOVA, and visual amplification were employed to evaluate the influence of cutting speed (Vc), feed per tooth (fz), and axial depth of cut (ap) on surface roughness and production rate. For the Zig-Zag and Parallel Spiral tool paths, cutting speed was the most important factor affecting surface roughness. For the One-Way strategy, axial penetration was the most important factor. The Parallel Spiral toolpath, under the Vc of 150 m/min, the fz of 0.025 mm/tooth, and the ap of 1.0 mm (A3-B3-C1) configuration, achieved the best balance between surface finish and production rate. Visual analysis also showed significative differences in how rough the wall was along perpendicular and parallel tool paths, which made it clear that finishing passes are needed in some cases. This research shows that using both statistical methods and visual amplification together makes process optimization more organized and effective, which leads to better machining performance.
