Thermal Deformation Prediction of CNC Machine Tool Using Evolutionary Fuzzy Controller

• Main Authors: Chia-An Lee, 李佳安
• Other Authors: 陳政宏
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/ab4x6v

碩士 === 國立虎尾科技大學 === 電機工程系碩士班 === 105 === In recent years, the development of CNC machine tool industry towards high-speed and high-precision processing, increased precision machining requirements for workpieces, and the output value is also flourishing. Thermal error has been the main factor affecting the precision; its impact cannot ignored. The breakthrough in machine tool technology is whether it can cope with this unavoidable physical phenomenon. The internal parts of the machine are complicated; the temperature affects each other, we only discuss the impact of a small number of temperatures on the displacement has been inadequate. To establish a complete thermal prediction model as the goal, this study develops a data acquisition system for CNC vertical integrated processing machines. Temperature data and displacement data collection by cutting intermittent operation and continuous operation of the experiment, and then use the proposed evolutionary fuzzy controller (EFC) to establish the thermal deformation prediction model. Through experimental verification prediction accuracy of model in different cases, the predicted displacement value will be required for future compensation. The experimental results of evolutionary fuzzy controller (EFC) and multivariate regression analysis (MRA) in different working situations show that there is no significant difference in the performance of the modeling experiment. However, in other experiments, the evolutionary fuzzy controller (EFC) is more adaptable to different experimental conditions than the multivariate regression analysis (MRA), and the performance is higher, the average estimation error is below 3um.