The research of plastics industry on Industry 4.0 - The defective product case study of E company
| Autor: | KUO, PO-HUNG, 郭柏宏 |
|---|---|
| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 Since Germany has proposed Industry 4.0 about 5-6 years, in order to solve the shortage of manpower, low-efficiency production mode, a lot of customized demands and changes in consumption patterns, countries have continuously proposed policies to assist Corporate transformation Policies such as Productivity 4.0 and Smart Manufacturing are also introduced in Taiwan. This study will explain how these policies integrate with SMEs (Small and Medium Enterprise) in Taiwan to promote Industry 4.0. These technologies penetrate into the manufacturing industry and integrate physical and virtual worlds through Cyber-Physical System(CPS). The widespread use of CPS in manufacturing environments has made manufacturing systems more intelligent. In order to facilitate the research of the implementation in Industry 4.0, this study explores the intelligent manufacturing system of Industry 4.0. This study will focus on the investigation of SME injection molding plant into Industry 4.0. There are often defective causes in the production process. The injection molding staff in the company also spends a lot of time sorting out the possible causes of defective products, however the existing production mode is through the paper and manual check. When recording with the manual registration method, the production process may end before the data is collected, and the problem point cannot be completely recovered to assist the personnel to make improvement plans. Therefore, First of all, to investigate and inspect the machine equipment that could integrate Internet of Things(IoT). To establish a demonstration line, let the computer and machine start to integrate and transfer data. In the process, the data will not be manually registered, and the data will be recorded and feedback to the equipment through Cyber-Physical System(CPS) to achieve the recordable production data per second and help the manufacturing process. The system can smoothly arrange the production schedule; transform the factory into a high-efficiency and high-transparency smart manufacturing factory. At the end, the data could analyze the data of Big data to make the data more reliable and help the company to figure out the various potential factors of defective products. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
| Externí odkaz: |
|