Development of an Automatic Optical Inspection System for a Micro PCB Drill Bits Re-Sharpening Machine

• Main Authors: Ju-Sung Yang, 楊儒松
• Other Authors: Kuan-Yu Chen
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/32213741925296901593

碩士 === 中原大學 === 機械工程研究所 === 101 === With the rapid development of modern technologies and theories, consumer electronics products are much more popular and intelligent in recent years. Hence, there are increasing demands from customers for electronic products, including high-quality, multi-function, longer battery life, and more convenient for carrying out. Therefore, thin and lightweight electronic products become a major development trend. For mounting more electrical components on the printed circuit board (PCB), its development must be toward multi-layer designs and its wiring is growing increasingly subtle. That is, drilling holes become smaller in size and PCB drill bits play an important role in the PCB manufacturing process. Requirement of drill bits re-sharpening has become increasingly important for the PCB industry. In order to reduce costs and ensure the high quality and sufficient quantity of its products, the development of automatic grinding machine for PCB drill bits has gained wide attention. The purpose of this study to develop the automated optical inspection system for the PCB micro drill bits re-sharpening machine by using optical image capturing and digital image processing technologies. There are two major parts of this study. The first part is to determine the regrinding drill bits good or bad by compared with calculating defects in the image of drill bit and product inspection standards. The second part is to use the training classifiers of artificial neural network for examining defects, making classifications and evaluating how they perform in promoting productive efficiency. Experimental results show that the presented automated optical inspection system in this study can identify 9 types of defects, namely the percentage of correctly classified is more than 92%.