The Application of Neural-Taguchi Method in Burrs Formation Predictions and Cutting Parameters Optimization

• Main Authors: I-Chen Chiu, 邱奕琛
• Other Authors: Pai-Chung Tseng
• Format: Others
• Language: zh-TW
• Published: 2001
• Online Access: http://ndltd.ncl.edu.tw/handle/05593952822359706054

碩士 === 國立中興大學 === 機械工程學系 === 89 === Burr has been defined as undesirable projections of material beyond the edge of workpiece due to plastic deformation machining. Products are tending to smaller in scale and sterner in precision in modern industry, it’s greatly affected by the presence of burrs produced during precision components manufacturing. In order to promote the industry development and reduce the interference caused by the noise like burrs, to avoid or minimize it is necessary. The residual burrs can be reduced significantly by selecting appropriate cutting parameters. The burr size is the object that we want to evaluate; medial carbon steel (S50C) is the object in this study; milling operation is end milling. We choose some parameters according to the object first. We adapt the Taguchi Method and Artificial Neural Network to establish the burrs formation model next, and use the neural network based optimal design method as a tool in parameters optimization. Above all, the goal of reduce burrs size into a reasonable region could be accomplished by the way of adjust cutting parameters. After cutting experiments, we found that the burrs causes by optimal parameters are significantly lower than current result. As this point of view, the parameters optimization operation by optimal parameters design method is an outstanding and effectively tool, this fills our study purpose for using the motivational action to eradicate waste. To eradicate any waste by the way of build up this kind of precautious concept before it occurred, it’s the one and only way to encourage the enterprise development.