Estimation of the PCB Production Process Using a Neural Network
| Autor: | Jr-Hung Guo, Kuo-Hsien Hsia |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Estimation
021110 strategic defence & security studies 0209 industrial biotechnology Gerber file Artificial neural network neural network lcsh:T Computer science 0211 other engineering and technologies Hardware_PERFORMANCEANDRELIABILITY 02 engineering and technology General Medicine computer.software_genre production schedule lcsh:Technology printed circuit board (PCB) 020901 industrial engineering & automation Hardware_INTEGRATEDCIRCUITS Data mining computer Hardware_LOGICDESIGN |
| Zdroj: | Proceedings of Engineering and Technology Innovation (2019) |
| ISSN: | 2518-833X 2413-7146 |
| DOI: | 10.46604/peti.2020.4265 |
| Popis: | Printed Circuit Boards (PCB) are an integral part of all electronic products, and the production process for printed circuit boards is quite complex. As the life cycle of electronic products becomes shorter and shorter, and the precision and signal bandwidth of electronic products become higher and higher, the manufacturing process of printed circuit boards is further complicated. Therefore, how to pre-evaluate the production difficulty before starting the production will effectively increase the efficiency and quality of printed circuit board production. Gerber file is the most commonly used data format for the printed circuit board industry. This file contains most of the parameters required for the manufacture of printed circuit boards. Therefore, this study uses a neural network to evaluate new PCB products before they are produced through the production parameters that are more influential in the PCB manufacturing process. This makes it possible to evaluate the difficulty and the required production process before the new PCB product is produced. This will be very beneficial for the PCB production schedule, quality control, and cost. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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