A Predictive Model on the Surface Characteristics of Deep-hole Drilling Explored by Back-propagation Neural Network

• Main Authors: Yu-Wei Chen, 陳昱瑋
• Other Authors: Wen-Tung Chien
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/27911901638328483638

碩士 === 國立屏東科技大學 === 機械工程系所 === 98 === The purpose of this study is to investigate the characteristics for the deep-hole drilling of carbon steel S45C in a gun drilling process and construct a predictive model for predicting its surface roughness. Firstly, experiments are arranged by an all-factor method to conduct deep-hole drilling. After experiments have been performed, the workpiece surface roughness and the tool flank wear are selected as single quality objective, respectively the experimental results can be further used for constructing a deep-hole surface roughness predictive model that is based on a back-propagation neutral network algorithm. The overall experimental procedure is divided into three parts. In the first part, according to the preliminary tests there are two deep-hole drilling parameters that showed stronger influence on drilling quality have been selected; namely, spindle speed and feed rate, each with 5 levels. The experiments were conducted based on the arrangement of deep-hole drilling parameters by all-factor method. In the second part, the former experimental results were used for training patterns and recalling patterns of the predictive model which was constructed base on a back-propagation neutral network. The optimum network parameters were attained by the average mean analysis of Taguchi method. In the third part, there are nine sets of verifying experiments without including any experiment of the training patterns or the recalling patterns have been conducted to validate the accuracy of this predictive model. It has shown that a 0.46μm for the best surface roughness(Ra) and a 0.13mm for the best flank wear in the experimental group, Moreover, the results of the verifying experiments showed that a mean error as 6.64% was found when the predictive values were compared. It indicates the predictive model developed base on a back-propagation neural network has good predicting ability for the surface roughness(Ra) in a deep-hole drilling process with gun drill. The processes and results in this study provide substantial assistance and reference efficiently for a gun-drill in deep-hole drilling carbon steel S45C.