摘要
文章提出一种基于人工智能(Artificial Intelligence,AI)的机械零件加工缺陷检测工艺,以提高缺陷检测的精度和效率。通过对缺陷类型与特点的分析,构建高质量缺陷图像数据集,并采用数据增强技术扩充样本。基于卷积神经网络(Convolutional Neural Networks,CNN)设计多尺度残差网络模型,实现缺陷的自动识别。实验结果表明,该方法在尺寸偏差、表面烧伤、内部组织异常等典型缺陷检测中表现出色,能够满足工业级缺陷检测的高标准要求。
In this paper,an Artificial Intelligence(AI)based defect detection technology for machine parts is proposed to improve the accuracy and efficiency of defect detection.Through the analysis of defect types and characteristics,a high-quality defect image dataset is constructed,and data enhancement technology is used to expand the sample.A multi-scale residual network model based on Convolutional Neural Networks(CNNS)is designed to realize the automatic identification of defects.The experimental results show that the method has excellent performance in the detection of typical defects such as dimensional deviation,surface burn and internal tissue abnormality,and can meet the high standard of industrial defect detection.
作者
钱磊
李锦楼
QIAN Lei;LI Jinlou(Shandong Xutian Sign Engineering Co.,Ltd.,Jinan 250000)
出处
《现代制造技术与装备》
2024年第8期110-112,共3页
Modern Manufacturing Technology and Equipment
