Using Machine Learning Algorithms to Predict Coating Thickness of Medical Screws

• Main Authors: Ruei-Jhen,Euo, 郭叡蓁
• Other Authors: Der-Chiang,Li
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/n89ss3

碩士 === 國立成功大學 === 工業與資訊管理學系碩士在職專班 === 107 === With the rapid development of semiconductor technology, the storage capacity and running speed of the computer are increasing rapidly. The researches and applications of machine learning have been more and more common. For the manufacturing fields, machine learning is often used for determining the optimal parameters to improve processes. In this paper, we introduce the process of using machine learning algorithms in real case, and four algorithms containing support vector regression (SVR), back propagation neural network (BPNN), M5’ model tree (M5’), and multiple linear regression (MLR) are applied in dealing with a real case. The real case is a traditional screw factory that begin to develop medical screws for business transformation. Considering the environmental protection and medical material safety, the company developed a coated X film that is a new process whose parameter values are very different from the old process. In the experiments, we collect data of the last one year. In order to achieve the best coating thickness, machine learning algorithms are applied to predict the coating thickness with the history data. And we find that the SVR has the best performances compared with BPNN, M5’, and MLR with evaluation metrics mean absolute percent error and root-mean-square error. So that, the SVR will be used to find the best values of production parameters in the future.