Study on the Correlationship and Prediction Model of Surface Roughness and Machining Parameters

• Main Authors: Yu-Chen Chen, 陳宥辰
• Other Authors: Jui-Pin Hung
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/cb3q7s

碩士 === 國立勤益科技大學 === 機械工程系 === 106 === 　　High speed and high precision machining has become the most important technology in manufacturing industry. The surface roughness of high precision components is regarded as the important characteristics of the product quality. On the other hand, regenerative chatter occurring in high speed machining could damage the machined surface and restricts the process efficiency and the longevity of cutting tools. To avoid chatter and increase machining precision, most of the engineers have selected the appropriate cutting conditions according the machining stability lobes diagram, but that cannot guarantee the surface quality in good conditions. In order to obtain better surface roughness, the selection of the cutting parameters is a prerequisite. This study was therefore aimed to investigate the influence of the machining conditions on the surface roughness. In study, the stability lobes diagram of a specific cutter was calculated based on the tool end frequency response functions, which was measured by the vibration test conducted on the milling machine. Basically, the stability lobes implied the cutting parameters (spindle speed and cutting depth) for stable machining. Next, a series of machining experiments were conducted. The surface roughness of workpieces was examined by means of the white light interferometer. 　　According to the machining tests, machined surface with or without chattering were marked on the lobes diagram for verification of the machining conditions. On the other respect, the ANOVA analysis reveals that the surface roughness show a positive correlation with machining conditions. Using multivariable regression analysis, the mathematical model describing the relationship between surface roughness (Ra) and cutting depth (a), feed rate (f) and spindle speed (s). The predicted roughness is shown to agree well with the measured roughness, an average percentage of errors of 17%. The average percentage of errors of the tool vibrations between the measurement and the predictions of exponential model is about 7.39%. Also, the tool vibration under various machining conditions are found to have a positive influence on the surface roughness (r=0.78). As a summary of this study, the stability lobes diagram was verified experimentally, which can help to identify the machining conditions without chattering in machining. Besides, a mathematical model was successfully developed to predict the surface roughness and vibration level of the tool under different cutting condition.