With the development of manufacturing towards stricter precision requirements and increasingly complex geometric shapes, dimensional accuracy has become a key factor affecting precision engineering components used in many industries. Effective cooling and lubrication methods have always been a meaningful way to improve the surface quality of cutting materials. Minimum-quantity lubrication technology mixes compressed air with cutting fluid, produces a spray at ambient temperature, and guides these droplets to the cutting area under the action of high-pressure air to promote penetration into the contact area between the tool, workpiece, and chip. Minimum-quantity lubrication can be used to increase cutting speed, cool workpieces, improve workpiece quality, and significantly reduce the pollution caused by cutting fluid to the environment. However, minimum-quantity lubrication technology still cannot meet the requirements of sustainable machining in cutting processes. A test device platform for milling 6030 aluminum alloy with minimal quantity lubrication was established, and different cooling methods were used to analyze the effect on surface roughness. The spindle speed n, feed rate f, and cutting depth ap are selected as optimization variables, with surface roughness as the optimization objective. Single-factor experiments were conducted to determine the optimal range for these variables. Subsequently, a model was constructed using the response surface methodology and solved using Design-Expert software. The interaction effects of spindle speed, feed rate, and depth of cut on surface roughness were analyzed. Additionally, genetic algorithms were employed to optimize cutting process parameters for the best combination. The results demonstrated that by combining Response Surface Methodology (RSM)and genetic algorithms, when the spindle speed n was 2520 r/min, the feed rate f was 48 mm/min, and the depth of cut ap was 0.08 mm, the actual surface roughness after milling reached 0.148 µm, representing a 74.57% reduction compared to the initial surface roughness. This research method provides a theoretical foundation and technical support for optimizing minimal quantity lubrication (MQL) cutting processes.
Cheng et al. (Sat,) studied this question.