| Summary: | 碩士 === 逢甲大學 === 機械與電腦輔助工程學系 === 104 === Milling Status Detection is a project to monitor condition of CNC machines
with the help of Neural Network (NN). The use of NN has spread out in many field
of modern life because of its ability in learning and solving prediction and
classification problem. In this study, we want to use a NN to increase the efficiency
of milling machine. A wireless sensing system was designed to collect the data
from milling process as described in [8]. This system achieved the recognition rate
of milling and idle detection up to 93%. Our goal is to investigate the data and
reach a higher performance. Therefore, the NN is adopted to recognize the milling
status and is implemented by Matlab. Moreover, Genetic Algorithm and Nondominated
Sort Genetic Algorithm (NSGA – II) was used to optimize the NN
topology including three parameters: number of tapped delay parameters, number
of FFT coefficients and number of hidden neurons. A preferable recognition rate
of 99.6% was achieved with the Genetic Algorithm implemented.
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