Implementing adaptive machining parameters for quality control system of surface roughness in end milling operations

• Main Authors: Gu-Meng chen, 古孟晨
• Other Authors: Huang-Bo Tsang
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/65953185651398091695

碩士 === 中原大學 === 工業與系統工程研究所 === 99 === As the tendency to industry automation, to increase the competitiveness of the industry, the factory are not only maintain the productivity, but also to enhance the quality of product for customers. Quality is affected by different types. One important index that can represent quality characteristic is the surface roughness. Many studies were proposed to improve the surface roughness of processing work-pieces by predicting. But they would cause the stop of continuous production, and were too hard to find a balance between quality and productivity. Therefore, the purpose of this study is to have a good control of surface roughness by adjusting the parameters of CNC machine, and to meet the client’s requirements and maintain the productivity simultaneously. In order to produce qualified work-pieces, this research not only takes processing parameter into consideration, but also uses force sensors to detect influences from uncontrolled cutting force and vibration that can affect the surface roughness during cutting process, to achieve the system can accurately control the surface roughness. Data is collecting from Surftest QP1620 CNC machine and then trained by neural network to build a decision making system that investigate the relation between processing parameter and surface roughness. This intelligent surface roughness quality control system is consist of four sub-systems. The first subsystem is to make a forecasting machine parameters of the processing condition. The second one is a forecasting of the surface roughness. The third subsystem is to adjust the parameter of spindle speed. And the fourth subsystem is to adjust the feed rate. Every subsystem uses neural network as decision making system. To decrease the training time, the Taguchi method to find the best network parameter combination. Surface roughness quality indicated by clients was asked as a starting point for system implementation, and use the indicators to predict the processing condition in the first subsystem, adding the second subsystem to predict the surface roughness under this condition. Then, two major parameters (spindle speed, feed rate) of the machine CNC can be adjusted through the subtraction of the results between the first and second system. The surface roughness can be controlled in an acceptable quality range. In this study, a intelligent surface roughness quality control system. The analysis of the error function are less than 0.0011 for subsystems, 30 data was used to validate the system, the results of the experiment model adjust the processing parameters can be found up to 87% data to meet customer requirements.