基于CiteSpace的内表面缺陷检测研究进展与趋势

为分析内表面缺陷检测的发展历程、趋势和研究动态,通过对WoS和CNKI数据库中该领域相关文献的检索,共搜集相关文献英文4 708篇,中文818篇,利用可视化分析软件CiteSpace对文献数据开展共现分析、聚类分析等知识图谱研究,分析内表面缺陷检测领域在国家、机构及研究人员层面的分布现状及合作情况,梳理研究热点和前沿趋势。研究发现内表面缺陷检测研究具有明显的多学科交叉属性,主要涉及分析化学、材料科学、光谱学、仪器仪表、机械工程和计算机等学科。近几年WoS数据库相关主题收录文献年增长率超过10%, CNKI年增长率超过20%,中美两国为本领域研究最为活跃的国家,两国发文量约占总发文量的40%,中国学者在无损检测、图像处理等领域的研究明显落后于国外学者,但在机器视觉和深度学习领域实现赶超。按照研究路线可将相关研究分为基于声光电热磁的检测和基于视觉成像的检测两类,其中前者包括采用不同技术手段获取光谱、超声和电磁图像并借助图像处理技术实现缺陷检测,而后者主要基于视觉图像进行缺陷识别和分类,目前已成为该领域主要的研究热点。内表面缺陷检测发展历程分为缺陷识别、缺陷分类、缺陷分析三个阶段,2000年...

Advances and Prospects in Inner Surface Defect Detection Based on Cite Space

In order to analyze the development, trend and dynamics of inner surface defect detection, 4 708 relevant literature in English and 818 in Chinese were collected through the search of relevant literature in this field in WoS and CNKI databases. The visual analysis software CiteSpace is used to study the knowledge map of literature co-occurrence and clustering, analyze the distribution status and cooperation of internal surface defect detection in countries, institutions and scholars, and sort out the research hotspots and cutting-edge trends. It is found that the research on inner surface defect detection has obvious interdisciplinary attributes, mainly involving analytical chemistry, material science, spectroscopy, instrumentation, mechanical engineering and computer science. In recent years, the annual growth rate of related literature in the WoS database has been more than 10%, and the annual growth rate of CNKI has been more than 20%. China and the United States have become the most active countries in this field, accounting for about 40% of the total number of publications. Chinese scholars’ research in non-destructive testing, image processing and other fields lags behind that of foreign scholars, but they catch up in machine vision and deep learning. According to the research route, it can be divided into detection based on acousto-optic electrothermal magnetism and detection based on the visual imaging. The former includes the acquisition of spectral, ultrasonic and electromagnetic images by different technical means and the realization of defect detection by image processing technology, while the latter is the main defects recognition and classification based on visual image, has become the main research focus in the field. The development of inner surface defect detection can be divided into three stages: defect identification, defect classification and defect analysis. Before 2000 defects were recognized and determined mainly by thermal, acoustic, optic, electrothermal, and magnetic signals or images. Since 2000, the support vector machine (SVM) technology greatly improves the efficiency and accuracy of defect classification. In recent ten years, with the increasing demand for defect analysis and measurement, defect location and measurement based on machine vision has gradually become a development trend, and the object of defect detection has gradually developed to the inner surface of deep holes and small holes.