A Study of Applying MR Control in Gray Online Modeling Surface Roughness Prediction.

• Main Authors: Chung-He Yu, 余忠河
• Other Authors: Po-Tsang Huang
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22105CYCU5030063%22.&searchmode=basic

碩士 === 中原大學 === 工業與系統工程研究所 === 105 === In nowadays product property of diverse and few and short life time, the need of the quality becomes more and more strict. Milling in a common way in processing, and the roughness will affect the damage level of cutting tool and the rate of manufacturing plan, to grasp the roughness will affect the quality and in order to get the accuracy of roughness lots of scholar wish to use predict, monitor the process variation and decrease the cost on the other hand. Under the different process environment and plan, there are lots of element that cannot be grasped. Therefore the sensing technology cam help to monitor the variation in the process, analysis the process effect that cause by external element. This allows to decrease the defective rate and improve the accuracy of prediction. Traditional prediction needs lot of data to model which does not fit to the present process, moreover, it needs to modify under different process. Therefore, we use Gary theory to predict and chose the strongest sensing data through the use of Grey Relational Analysis and use moving range control chart to filter the noise and put the final data in GM(1,N) and find the trend property through Accumulated Generating Operating (AGO) for prediction system. In the research we use Computer Numerical Control (CNC) milling, it is able to use less data for predicting roughness and are able to model in real time also through the setup of control boundary it will be able to prevent noise in the forecast model which effect the prediction accuracy. In order to verify the accuracy and feasibility of this research, we use two different process parameter apply few data in the prediction system and predict the roughness. The result shows the accuracy are 97.56% and 98.02% which are able to verify the accuracy and feasibility.