การพยากรณ์ในกระบวนการสำหรับความขรุขระผิวชิ้นงานในการกลึงอะลูมิเนียม

• المؤلف الرئيسي: อาทิตยา ชัยจรีนนท์
• مؤلفون آخرون: สมเกียรติ ตั้งจิตสิตเจริญ
• التنسيق: Theses and Dissertations
• اللغة: Thai
• منشور في: จุฬาลงกรณ์มหาวิทยาลัย 2017
• الوصول للمادة أونلاين: https://digiverse.chula.ac.th/Info/item/dc:41827

The aim of this research is to study the relation between the in-process surface roughness of aluminium and the cutting forces ratio during the turning under the various cutting conditions on the CNC turning machine. In the case of this research, the dynamometer is installed to generate a signal while the turning. The cutting force signals are amplified through the charge amplifier before digitization and calculation in the computer. The cutting force ratio, which is the ratio of feed force to main force, is applied to develop the in-process surface roughness model which cooperates with the cutting conditions of the cutting speed, the feed rate, the tool nose radius, the depth of cut and the cutting force ratio. The experimental results, represented the relation between the surface roughness and the cutting conditions which has relative to the surface roughness become lower when the feed rate and the depth of cut are low which is in contrast to the cutting speed and the tool nose radius. For the relation between the dynamic cutting forces and the surface roughness profile is examined by applying the Fast Fourier Transform (FFT). The experimentally obtained results showed that the dynamic cutting forces and the surface roughness profile which are the same frequency. Thus, the cutting force ratio is able to predict the surface roughness during the turning. The surface roughness model is developed based on the experimentally obtained results by employing the exponential function. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method at 95% confident level. It is proved by cutting tests that the arithmetic surface roughness and the surface roughness depth models can be predicted the in-process surface roughness and obtained with the high accuracy of 88.03% and 89.55% respectively.