期刊
CIRP ANNALS-MANUFACTURING TECHNOLOGY
卷 65, 期 1, 页码 417-420出版社
ELSEVIER
DOI: 10.1016/j.cirp.2016.04.072
关键词
Quality assurance; Artificial intelligence; Deep machine learning
Fast and reliable industrial inspection is a main challenge in manufacturing scenarios. However, the defect detection performance is heavily dependent on manually defined features for defect representation. In this contribution, we investigate a new paradigm from machine learning, namely deep machine learning by examining design configurations of deep Convolutional Neural Networks (CNN) and the impact of different hyper-parameter settings towards the accuracy of defect detection results. In contrast to manually designed image processing solutions, deep CNN automatically generate powerful features by hierarchical learning strategies from massive amounts of training data with a minimum of human interaction or expert process knowledge. An application of the proposed method demonstrates excellent defect detection results with low false alarm rates. (C) 2016 CIRP.
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