Using Artificial Neural Network to Predict Processing Time of CNC Machining Center

• Main Authors: Zheng-Hao Huang, 黃政豪
• Other Authors: Chun-Hui Chung
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/42u6fr

碩士 === 國立臺灣科技大學 === 機械工程系 === 106 === In the manufacture industry, CNC become a part of indispensable roles. Because of the process scheduling and delivery deadline, predict the machining time become very important. All of the CAM software likes the NX, used the length of cutting roots divided by the feedrate to predict the machining time, but in reality, machine have different acceleration, deceleration followed by different motion type and different machine have its own machine performance, so the time predict will not very accuracy. Neural network is applied in every field accompanied by the big data and Artificial Intelligence. It is used to predict the surface roughness, surface topography, cutting force and machining temperature. In this thesis, we are used backpropagation neural network to predict the machining time. Between that we consider the effect factor which is caused by the motion type, linear and circular. Finally, we can use this model to predict the machining time. According to the research, using LM, one hidden layer and sixty hidden layer neurons to train the little 5-axis machine model can reach the error rate between 0.3 to 8.3% ; however, the NX error rate is between 6.7 to 18.5%. Then we used the same method to get the QUASER UX300 CNC machining time and trained it, the model error rate can reach 0.37 to 6%, this thesis confirmed that used this experiment method can train the time model rapidly and suitable for every machine.