Comparative analysis and optimization of surface quality and manufacturing productivity for coaxial and orthogonal turn-milling

This study analyzed the working performance of coaxial and orthogonal turn-milling and optimized these turn-milling approaches. The surface quality and assembly suitability of machined products are becoming increasingly crucial, and the metal processing industry is eager to achieve high workpiece quality and manufacturing productivity. In this study, 6010-T6 aluminum concrete pillars were cylindrically processed using the GLS-2000 LYS turn-milling machine. A surface topology model for coaxial turn-milling was developed on the basis of material removal theory. Additionally, an Ra prediction model for orthogonal turn-milling was established. Experiments were conducted to analyze the machining effects of critical parameters on productivity and quality. Finally, the Ra and MRR prediction models for the two machining methods were introduced to a multiobjective genetic algorithm (MOGA) to compare their performance under various Ra requirements. On the basis of a theoretical derivation and real processing experiment, the relationship between the required surface roughness (Ra) and material removal rate (MRR) was analyzed to optimize the working method and parameters of turn-milling. Experimental validation revealed that the surface topographies of coaxial turn-milling and the fitted predictive model of orthogonal turn-milling were in favorable agreement with the actual machining results. According to the MOGA results, when the Ra target is greater than 0.21 μm, coaxial turn-milling provides better machining performance with a higher MRR. Conversely, when the required Ra is smaller than 0.21 μm, orthogonal turn-milling is more suitable for achieving high-precision machining under a relatively high MRR. Finally, orthogonal turn-milling could achieve an Ra value of 0.09 μm with an MRR of 5,667 mm³/min, whereas coaxial turn-milling could achieve an MRR of 17,342 mm³/min at an Ra of 3.51 μm. Compared with traditional machining methods, similar or even lower Ra and considerably higher MRR were achieved by both methods. This study provides efficient decision-making support for metal processing industry. Both productivity and surface quality are improved with less time and cost sacrifice. The product sustainability and industrial competitiveness can be well enhanced.