An In-Process Monitoring Approach of Using Supervised Learning Neural Network to Detect Too Breakage in End Milling Operations

• Main Authors: Cheng-Chieh Ma, 馬成傑
• Other Authors: Potsang B. Huang
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/05955188508257928340

碩士 === 中原大學 === 工業與系統工程研究所 === 98 === In recent years, the needed of Computer Numerical Control (CNC) milling is creasing by the development of the electronic industry and plus the request of quality which makes the CNC milling technology progress become the issue that ever automatic industry focus on. When the CNC milling, the wear and breakage of the cutting tool obviously cause the unstable productive quality and decrease the produce efficacy, moreover, will damage and destroy the expensive tools and increase the manufacture cost. Since the CNC doesn’t have the ability of detecting the cutting tool itself, this research will mention about three decision-making systems of prompt detecting the tool breakage that makes the machine can detect the breakage of cutting tool immediately during its working. The data processing method of the three systems are all Supervised Learning Neural Network algorithm, there are Back-Propagation Network (BPN), Probabilistic Neural Network (PNN) and Learning Vector Quantization (LVQ). The systems all used the same force sensor to measure the cutting force signal, and the cutting force and machining parameters, such as spindle speed, feed rate, and depth of cut will be the input of network, and tool conditions will be the output to train the network. Then use the corresponding output to train the network, and will completely imitate in CNC afetr training to discuss the accuracy of the judgment. When the three systems finish the frame, this research will use the faster training Network and address the other prompt and relearning of tool breakage detecting system with higher accurate judgment. The system can put the ill-judged argument data back to the database when the cutting tool is shut down by ill-judged the breakage, and retrain the network structure to achieve more accurate tool breakage detecting by increasing the sample data of the adjustable detect system to raise the ability of the Network predicting.