Parameter Prediction for Weaving Process in Cyber-Physical Systems Using Machine Learning

• Main Authors: Jia-Ying Lin, 林佳穎
• Other Authors: Ray-I Chang
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/8v6833

碩士 === 國立臺灣大學 === 工程科學及海洋工程學研究所 === 106 === In Industry 4.0, the intelligent transformation of traditional textile factories is important to maintain competitiveness. One of the key technologies for smart transformation is the cyber-physical system. The cyber-physical system will generate the cyber twin on the virtual end for each entity element in the industrial chain to achieve the purpose of predicting and managing the behavior of the entity element. The cyber-physical system needs to be self-examination and self-comparative, meaning that it is aware of changes in its own state and more similar entity elements to make predictions. More importantly, it is "self-reconfigurable" and self-enhanced according to system goals. In order to achieve the goal of setting the loom parameters intelligently, this study has two objectives. First one is to development a cloud data analysis system, InAnalysis. The second one will be build a parameters prediction model for the weaving process. From the perspective of data science, the machine learning regression algorithm is used to build a loom parameter prediction model, and 10-fold cross validation is used to understand the performance of the model. This achieved the "self-introspection" and "self-comparison" required by cyber-physical system. Also, the concept of query-based learning is used. And the performance of the prediction model can be effectively enhanced and a better realistic strategy can be obtained. This is the most important feature of the cyber-physical system, self-reconfigurable. The experimental results show that the MSE (Mean Square Error) of the prediction model is only 0. 000165. Moreover, the performance of the parameter prediction model and quality prediction model can be reinforced through query-based learning. In the future, InAnalysis''s API will be used in an operation parameter recommendation system (OPRS) and weaving process of decision-making will become intelligent. Further, the system could implement into entire textile industry. Such as, "orders," "R&D," "supplies," "production," "inspection," "shipments," and "sales." All other services are managed efficiently and intelligently by the cyber-physical system.