ニュ-ラルネットを用いたPCBの欠陷檢出および分類に關する硏究 = A Neural network approach to defect classification on printed circuit boards

다국어 초록 (Multilingual Abstract)

We investigate the defect detection by making use of pre-made reference image data and classify the defects by using the artificial neural network. The approach is composed of three main parts. The first step consists of a proper generation of two references image data by using a low-level morphological technique. The second step proceeds by performing three times logical bit operations between two ready-made reference images and just captured image to be tested. In the third step, by extracting four features from each detected defect, followed by assigning them into the input nodes of an already trained artificial neural network we can obtain a defect class corresponding to the features. Experimental results show that proposed algorithms are found to be effective for flexible defect detection, robust classification and high-speed process by adopting a simple logic operation.

목차 (Table of Contents)

• 1. はじめに
• 2. 검사アルゴリズム
• 3. 缺陷分類のためのアルゴリズム
• 3.1 神經回路網による缺陷分類
• 3.2 역운반학습アルゴリズム(Back propagation learning algorithm)
• 3.3 神經回路網の學習標本の選定
• 4. 實驗及ぴ 結果
• 4.1 實驗裝置
• 4.2 欠陷分類
• 5. おわりに
• 參考文獻