Optimization of cnc turning parameters for minimizing temperature rise in aluminum using a genetic algorithm / Mimi Muzlina Mukri

Faculty of Mechanical and Automotive Engineering Technology

Thesis (Master of Science) -- Universiti Malaysia Pahang – 2024

Includes bibliographical references

The focus of modern machines is to achieve high-quality end products by considering a few factors such as the accuracy of the dimension, less wear of the cutting tools, and the economy of the machining process. The selection of cutting parameters for the turning process is critical to achieving optimal cutting results. Temperature rise during the machining process and surface roughness of the workpiece are crucial machining outcomes during the turning process. These outcomes contradict each other by aiming for the finest surface roughness with a minimum temperature rise. This will cause problems with the tool life and mechanical properties of the workpiece. Thus, an optimization process is important to obtain optimized machining outcomes by optimizing machining parameters. The genetic algorithm is used in this optimization because it is capable of searching for global optimal solutions since the configuration of the method can be very flexible, allowing it to be used for a variety of problems. To minimize the temperature rise during machining, the cutting speed, feed rate, depth of cut, and nose radius are optimized in this study using a single-objective genetic algorithm. In the second optimization process, machining parameters such as cutting speed, feed rate, and depth of cut are optimized using a multi-objective genetic algorithm to concurrently lower temperature rise and surface roughness. Then, based on the optimization outcomes, the analysis of the effect of material hardness on the temperature rise and surface roughness in the turning process is conducted. For the first optimization, the lowest temperature rise during turning machining was achieved by the single-objective genetic algorithm optimization approach to figure out the best cutting speed, feed rate, and depth of cut. The GA result showed a minimal temperature rise of 23.07 °C, with cutting variable values of 81.22 m/min for cutting speed, 0.08 mm/rev for feed rate, and 0.12 mm for cutting depth, respectively. In comparison to the prior optimization, which resulted in a temperature of 23.94 °C, this one has reduced the temperature by 3.62%. Three types of workpiece hardness of 20 HRC, 36 HRC, and 43 HRC are utilized for the second optimization. When the cutting parameters are 80 m/min, 0.071 mm/rev, and 0.5 mm for the cutting speed, feed rate, and depth of cut for hardness 20, respectively, the lowest temperature rise that can be achieved with this method is 243.33 °C. The cutting speed, feed rate, and depth of cut parameters were set at 85.02 m/min, 0.084 mm/rev, and 0.504 mm, respectively. This resulted in the lowest surface roughness measurement for hardness 36, which was 1.246 µm. The lowest temperature rise that can be achieved by this approach is 397.39 °C when the cutting parameters are 80.133 m/min, 0.07 mm/rev, and 0.50 mm for the cutting speed, feed rate, and depth of cut with hardness 43. The lowest surface roughness that has been identified is 0.781 µm. In comparison to earlier studies, the temperature rises were able to be reduced by 10.2% and the surface roughness by 4% by employing GA as an optimization method. From this optimization, the effect of material hardness on the temperature rise and surface roughness in the turning process was analyzed. It was discovered that surface roughness decreases with increasing hardness, but temperature increases as hardness increases.