摘要
检测金属铸件在工程和使用过程中可能存在的缺陷,应用基于热弹机制的激光超声可视化检测仪对铸件进行扫描并将信号制成最大振幅图像,实现对铸件的可视化检测。为了高效、快速地对最大振幅图进行批量处理,结合卷积神经网络图像处理技术对最大振幅图进行识别。针对任务需要设计了一个卷积神经网络架构对最大振幅图进行识别,识别过程中通过改变卷积层和卷积核大小设置了不同的卷积神经网络架构,将预先设计的架构与其他的架构进行横向对比,实验结果表明预设架构综合性能最好。相同实验条件下,该卷积神经网络架构为使用最大振幅图检测铸件缺陷提供了一个有效可行的方案。
To detect the possible defects of metal castings in engineering and application, the laser ultrasonic visual detector based on thermoelastic mechanism is used to scan the castings and make a maximum amplitude image of the signal to realize the visual detection of the castings.In order to efficiently and quickly process the maximum amplitude graph in batches, the convolution neural network image processing technology is used to identify the maximum amplitude graph.A convolutional neural network architecture is designed to recognize the maximum amplitude figure, and different convolutional neural network architectures are set up by changing the size of convolutional layers and convolutional kernels during the recognition process, and the predefined architectures are horizontally compared with other architectures.The experimental results show that the overall performance of predefined architecture is the best.Under the same experimental conditions, the convolutional neural network architecture provides an effective and feasible scheme for casting defect detection using maximum amplitude graph.
作者
魏博文
高炜欣
刘梦溪
WEI Bo-wen;GAO Wei-xin;LIU Meng-xi(School of Electronic Engineering,Xi′an Shiyou University,Xi′an 710065,China;Key Laboratory of Gas-Oil Logging Technology,Xi′an Shiyou University,Xi′an 710065,China)
出处
《激光与红外》
CAS
CSCD
北大核心
2022年第9期1327-1334,共8页
Laser & Infrared
基金
陕西省自然科学基金项目(No.2020JQ-788)
陕西省重点研发项目(No.2020GY-179)
西安石油大学研究生创新与实践能力培养项目(No.YCS21113139)资助。
关键词
缺陷检测
激光超声
卷积神经网络
defect detection
laser ultrasonic
convolutional neural network
