Application of Adaptive Network based Fuzzy Inference System on Predicting Grinding Surface Quality

• Main Authors: Chih-Hsiang Chang, 張致祥
• Other Authors: Neng-Hsin Chiu
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/77017263910375879773

碩士 === 國立高雄第一科技大學 === 機械與自動化工程所 === 92 === Grinding is commonly used precision work method in the machining. But in grinding process, grinding parameter, such as the grinding wheel rotational speed, feed rate, depth of cut, grinding wheel grit size often is the influence of work piece quality. In the tradition grinding processing, frequently refers to the technical manual and the operator itself experience, is choosing these parameters by try-and-error method. As this reason, each period of work piece surface quality, often changes because of operator's difference, and don’t easily control. Therefore, in order to improve quality, it is a good method to establish an accurate effective processing model, provides group of good processing parameters. The model help us to controls the work piece quality and processing efficiency. To establish model with processing parameter input and surface quality output is the goal of this research. Using the Adaptive Network based Fuzzy Inference System (ANFIS), establishes the KG grinding wheel to grind the SKD11 molding steel model to forecast surface quality. The processing forecast model which with grinding parameter as the input parameter, work piece surface quality as the output target value, constructs by measure the data based on the actual processing experiment. By way of the network training, forecast the model is smaller than 1.2% to the surface roughness erroneous value, but is smaller than 0.3% to the surface micro-hardness erroneous value.