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- 摘要
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[Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11256 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part I) || Damage Online Inspection in Large-Aperture Final Optics
摘要： Under the condition of inhomogeneous total internal reflection illumination, a novel approach based on machine learning is proposed to solve the problem of damage online inspection in large-aperture final optics. The damage online inspection mainly includes three problems: automatic classification of true and false laser-induced damage (LID), automatic classification of input and exit surface LID and size measurement of the LID. We first use the local area signal-to-noise ratio (LASNR) algorithm to segment all the candidate sites in the image, then use kernel-based extreme learning machine (K-ELM) to distinguish the true and false damage sites from the candidate sites, propose autoencoder-based extreme learning machine (A-ELM) to distinguish the input and exit surface damage sites from the true damage sites, and finally propose hierarchical kernel extreme learning machine (HK-ELM) to predict the damage size. The experimental results show that the method proposed in this paper has a better performance than traditional methods. The accuracy rate is 97.46% in the classification of true and false damage; the accuracy rate is 97.66% in the classification of input and exit surface damage; the mean relative error of the predicted size is within 10%. So the proposed method meets the technical requirements for the damage online inspection.
关键词： Size measurement,Damage online inspection,Classification,Laser-induced damage,Machine learning
更新于2025-09-23 15:21:01
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A novel algorithm for tool wear online inspection based on machine vision
摘要： To inspect the tool wear condition in the process of numerical control (NC) machining for difficult-to-cut material, an online inspection system for tool wear is developed based on machine vision. With the help of MATLAB software, a self-matching algorithm is proposed according to the characteristics of tool wear images. The corresponding user-friendly graphical user interface (GUI) of the algorithm is developed. The bottom edges are separated by the adaptive connecting domain labeling to analyze wear condition of each edge. Then, each edge is arranged regularly by the improved rotatory positioning. The cutting edge is extracted by the method of partial angle threshold to fit and calculate the bottom wear value. It is shown that the absolute values of errors on the maximum wear width are less than 0.007 mm by using the self-matching algorithm. In the case of severe wear and breakage, the absolute values of errors on the maximum wear width are less than 0.057 mm because of uneven reflected light. The system features high response speed, high inspecting accuracy, and anti-noise performance. It is proved to be able to increase the utilization of cutting tools and guarantee the quality of workpiece. This method is potential to guarantee the reliability of cutting tool in aerospace manufacturing.
关键词： Image processing,Tool wear,Machine vision,Self-matching algorithm,Online inspection
更新于2025-09-10 09:29:36