| 摘要(英) |
Industry 4.0 Smart Manufacturing is a hot topic recently. Today′s global manufacturing industry is committed to smart manufacturing through industrial Internet of Things, big data analytics, and Cyber Physical System (CPS) technologies to improve product performance and product quality by saving production time and cost. This paper explores Virtual Metrology (VM) research to predict product quality before or after the production process has not been completed, without the need of product measurement. Specifically, this paper focuses on the Surface Roughness prediction of wire-cut EDM machines, and uses the 2nd order regression and the deep neural network method to predict the surface roughness of the product through production parameters before product processing. In addition, this thesis uses Markov Chain and Deep Neural Network (DNN) method to predict the surface roughness of the product through production parameters and time series data of the production process after the product is processed. In order to deal with the time series data with different lengths, this paper uses the Markov chain extraction feature to normalize the length and then predict the product quality through the neural network. We use the full factor experimental method to collect experimental data to verify the prediction accuracy of the proposed method. The experimental results show that the proposed prediction method has good mean absolute error and error rate. |
| 參考文獻 |
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