Improving the Defect Detection for Image Data in a High-speed Mass Production Process using Convolutional Neural Network and Experimental Designs
|關鍵字:||品質管理;缺陷偵測;卷積神經網絡;影像處理;實驗設計;高速生產;Quality control;Defect detection;Convolutional neural network;Image processing;Experimental designs;High-speed production|
Recent technological advances in manufacturing industry allow high-speed mass production and manufacturers often utilize the online inspection machines to monitor the production process. However, the online inspection machines for image data in a high-speed mass production process are still in the development stage. They might cause misclassifications of the non-defective or defective products, and therefore, result in unnecessary process adjustment or profit loss. Previous studies developed some high accurate discriminant methods such as feature extraction in relation to a filtering window, and ensemble classifiers for identifying the defective products. However, these methods have some drawbacks which may cause high misclassification rates when they are employed in a high-speed mass production process. Hence, the objective of this study is to build a more precise defect detection system to discriminate the non-defective products and defective products using the convolutional neural network and experimental designs. The proposed method is verified to be superior to the existing defect detection system for classifying the image data in a high-speed mass production process.
|Appears in Collections:||Thesis|