工业铸件缺陷无损检测技术的应用进展与展望

张辉; 张邹铨; 陈煜嵘; 吴天月; 钟杭; 王耀南

doi:10.16383/j.aas.c210161

工业铸件缺陷无损检测技术的应用进展与展望

doi: 10.16383/j.aas.c210161

张辉^1,,
张邹铨^2,,
陈煜嵘^1,,
吴天月^2,,
钟杭^1,,
王耀南¹

1.
湖南大学机器人学院长沙 410082
2.
长沙理工大学电气与信息工程学院长沙 410114

基金项目: 国家重点研发计划 (2018YFB1308200), 国家自然科学基金 (61971071, 92148204), 湖南省杰出青年科学基金项目 (2021JJ10025), 湖南省重点研发计划 (2021GK4011, 2022GK2011), 机器人学国家重点实验室联合开放基金 (2021-KF-22-17)资助

详细信息

作者简介:
张辉：湖南大学机器人学院教授. 2004年、2007年和2012年获得湖南大学学士、硕士和博士学位. 主要研究方向为工业机器视觉和数字图像处理. 本文通信作者. E-mail: zhanghuihby@126.com

张邹铨：长沙理工大学电气与信息工程学院硕士研究生. 主要研究方向为深度学习和视觉检测. E-mail: zouquan_zhang@163.com

陈煜嵘：湖南大学电气与信息工程学院博士研究生. 2020年获美国匹兹堡大学硕士学位. 主要研究方向为图像处理, 机器学习和领域自适应. E-mail: chenyurong1998@outlook.com

吴天月：长沙理工大学电气与信息工程学院硕士研究生. 主要研究方向为深度学习和视觉检测. E-mail: yue_wuwuwu@163.com

钟杭：湖南大学博士后. 2013年、2016年和2020年获得湖南大学学士、硕士和博士学位. 主要研究方向为机器人控制, 视觉伺服和路径规划. E-mail: zhonghang@hnu.edu.cn

王耀南：中国工程院院士, 湖南大学机器人学院教授. 1995年获湖南大学博士学位. 主要研究方向为机器人学, 智能控制和图像处理. E-mail: yaonan@hnu.edu.cn

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出版历程
- 收稿日期: 2021-02-26
- 录用日期: 2021-06-06
- 网络出版日期: 2021-07-27
- 刊出日期: 2022-04-13

Application Advance and Prospect of Nondestructive Testing Technology for Industrial Casting Defects

1.
School of Robotics, Hunan University, Changsha 410082
2.
School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114

Funds: Supported by National Key Research and Development Program of China (2018YFB1308200), National Natural Science Foundation of China (61971071, 92148204), Hunan Science Fund for Distinguished Young Scholars (2021JJ10025), Hunan Key Research and Development Program (2021GK4011, 2022GK2011) and Joint Open Foundation of State Key Laboratory of Robotics (2021-KF-22-17)

More Information

Author Bio:
ZHANG Hui　Professor at the School of Robotics, Hunan University. He received his bachelor, master and Ph.D. degrees from Hunan University in 2004, 2007 and 2012. His research interest covers industrial machine vision and digital image processing. Corresponding author of this paper

ZHANG Zou-Quan　Master student at the School of Electrical and Information Engineering, Changsha University of Science and Technology. His research interest covers deep learning and visual inspection

CHEN Yu-Rong　Ph.D. candidate at the School of Electrical and Information Engineering, Hunan University. He received his master degree from University of Pittsburgh in 2020. His research interest covers image processing, machine learning and domain adaptionon

WU Tian-Yue　Master student at the School of Electrical and Information Engineering, Changsha University of Science and Technology. Her research interest covers deep learning and visual inspection

ZHONG Hang　Postdoctor at Hunan University. He received his bachelor, master and Ph.D. degrees from Hunan University in 2013, 2016 and 2020. His research interest covers robotics control, visual servo and path planning

WANG Yao-Nan　Academician at Chinese Academy of Engineering, professor at the School of Robotics, Hunan University. He received his Ph.D. degree from Hunan University in 1995. His research interest covers robotics, intelligent control and image processing

摘要

摘要: 高端装备制造业是国民经济的支柱产业, 是推动工业转型升级的引擎, 发挥着举足轻重的作用. 而铸造产业一直是人类现代生产生活中重要的、不可替代的产业, 铸件产品既是工业制造产品, 也是大型机械的组成部分. 随着经济水平和工业自动化程度的不断提升, 人们对于铸件的需求量呈指数爆炸式增长, 铸件价值辐射到各行各业. 与此同时, 铸件在铸造、服役过程中经常会出现各种缺陷, 而传统低效的人工检测方法难以保障工业界对中高端铸件的性能需求. 因此亟需对铸件检测技术进行革新. 本文首先对铸件铸造过程以及服役过程中各类缺陷的形成机理进行分析. 然后阐述了基于声学、光学、电磁学等主流检测技术及其常规信号处理方法、磁粉检测技术与渗透检测技术等其他检测技术, 并对近年来新兴的基于神经网络的信号处理方法进行了说明. 在此基础上, 分析了近年来铸件缺陷无损检测技术以及基于神经网络的信号处理方法的研究现状. 最后, 对铸件缺陷无损检测技术及应用的发展趋势进行了展望.
- 铸造缺陷 /
- 无损检测 /
- X射线探测 /
- 神经网络
Abstract: High-end equipment manufacturing industry is the pillar industry of the national economy and the engine to promote industrial transformation and upgrading. It plays a pivotal role. Foundry industry has been an important and irreplaceable industry in modern production and life of human beings, and casting products are both industrial manufacturing products and components of large machinery. With the continuous improvement of economic level and industrial automation, the demand for castings is growing exponentially and explosively, and the value of castings radiates to all walks of life. At the same time, casting products often show various defects in the process of casting and service, and the traditional inefficient manual detection method is difficult to guarantee the performance requirements of the industry for middle and high-end castings. Therefore, it is urgent to improve the casting testing technology. Firstly, we analyze the formation mechanism of various defects in casting process and service process in this paper. Then we describe the main detection techniques based on acoustics, optics and electromagnetism and conventional signal processing methods, magnetic particle detection technology and penetration detection technology. We also describe the emerging signal proceesing methods based on neural network in recent years. On this basis, we analyze the research status of casting defect nondestructive testing technology and neural network-based signal processing method in recent years. At last, the development trend of casting defect nondestructive testing technology and its application are prospected.
- Casting defects /
- nondestructive testing /
- X-ray testing /
- neural network
注释:

1) 收稿日期 2021-02-26 录用日期 2021-06-06 Manuscript received February 26, 2021; accepted June 6, 2021 国家重点研发计划 (2018YFB1308200), 国家自然科学基金 (61971071, 92148204), 湖南省杰出青年科学基金项目 (2021JJ10025), 湖南省重点研发计划 (2021GK4011, 2022GK2011), 机器人学国家重点实验室联合开放基金 (2021-KF-22-17)资助 Supported by National Key Research and Development Program of China (2018YFB1308200), National Natural Science Foundation of China (61971071, 92148204), Hunan Science Fund for Distinguished Young Scholars (2021JJ10025), Hunan key research and development program (2021GK4011, 2022GK2011) and Joint Open Foundation of State Key Laboratory of Robotics (2021-KF-22-17) 本文责任编委徐德

2) Recommended by Associate Editor XU De 1. 湖南大学机器人学院长沙 410082 2. 长沙理工大学电气与信息工程学院长沙 410114 1. School of Robotics, Hunan University, Changsha 410082 2. School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114

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图 1 基于稀疏表示检测算法流程图

Fig. 1 The flow chart of detection algorithm based on sparse representation

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图 2 拉伸杆和航空工件原图像及CT重建的三维图像

Fig. 2 Original images of stretching rods and aerial parts and three dimensional images rebuilt by CT

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图 3 铝铸件缺陷与无损伤处的高光谱数据

Fig. 3 Hyperspectral data of aluminum casting defects and no damage

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图 4 凝固扩展裂纹的超声波信号

Fig. 4 Ultrasonic signal of solidification propagation crack

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图 5 裂纹的几何尺寸与远场涡流探测信号关系

Fig. 5 The relationship between the crack geometry size and the far-field eddy current detection signal

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图 6 合金样品的缺陷区域热成像3D模型

Fig. 6 Thermal imaging 3D model of defect area of alloy sample

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图 7 机器视觉系统示意图

Fig. 7 Schematic diagram of machine vision system

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图 8 图像识别缺陷的类别、位置和区域

Fig. 8 Image recognition defect category, location and area

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图 9 帧间深度卷积神经网络结构图

Fig. 9 Inter-frame deep convolution neural network structure diagram

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图 10 高端铸件缺陷检测技术展望概述

Fig. 10 Overview of the prospect of high-end casting defect detection technology

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表 1 铸件缺陷类型以及各伤损示意图

Table 1 Types of casting defects and diagrams of each damage

缺陷种类	成因	影响因素	特征	示例
气孔	合金凝固时气体析出	气体溶解度、浇铸温度、压射速度、砂粒度^[7]	在铸件内部、表面处有光滑孔眼,有时附有一层氧化膜
缩孔	铸件凝固过程中,合金成分的液态收缩、凝固收缩以及固态收缩	铸件复杂度、浇注温度、冒口位置、铸造压力^[12,14,18]	在铸件厚断面内部,交界面内部及厚断面处,形状多为长尾状或凸形,孔内粗糙不平,晶粒粗大
铸造裂纹	铸件表面或内部因各种原因发生断裂,或机械加工产生的微缺陷	离心转速、涂料、浇铸温度及速度	在铸件上有穿透或穿透的裂纹,开裂处金属表皮未氧化
夹杂物	铸造合金在熔炼过程中杂质颗粒保留在固体金属内	浇注时间、碳含量	铸件内部出现不规则孔洞, 内含有明显细粒
偏析	凝固过程液相或固相的物理运动	铸件厚度、浇注温度	同一铸件上化学成分、金相组织和性能不一致
疲劳裂纹	在铸件内部产生永久性累计损伤	循环应力、循环应变	疲劳扩展区裂纹表面光滑, 脆性断裂区表面粗糙

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表 2 铸件缺陷无损检测与评估技术对比

Table 2 Comparison of non-destructive testing and evaluation techniques for rail defects

物理学分类	检测技术	铸件类型	缺陷类型	优点	缺点
基于光学的无损检测技术	X射线二维成像技术	所有铸件	孔洞类缺陷、夹杂类缺陷	可探测复杂异形铸件、结果直观且便于存储、对气孔类缺陷检测良好^[21−27]	检测环境要求高、速度慢、成本高、无法表征完整的缺陷轮廓及形态^[21−27]
	X射线三维成像技术	所有铸件	孔洞类缺陷、夹杂类缺陷及裂纹	可探测复杂异形铸件、结果直观且便于存储、对三维缺陷表达能力强^{[15, 28−38]}	检测环境要求高、速度慢、成本高^[28−38]
	机器视觉检测技术	所有铸件	表面缺陷	硬件成本低、可检测复杂铸件表面缺陷、结果直观且便于存储、检测速度快^[41−47]	系统抗干扰能力弱、成像质量易受外界因素影响^[41−47]
	高光谱检测技术	所有铸件	孔洞类缺陷	可探测复杂异形铸件、能获得缺陷更详尽的特征、预测潜在缺陷^[48−49]	成像过程极长、图像所占内存量大、结果无法直观地判别缺陷^[48−49]
基于声学的无损检测技术	超声检测技术	所有铸件	内部缺陷	探测速度快、检测成本低、穿透能力强、对环境无污染^[50−51]	需要耦合剂、对铸件表面光滑度有要求、信号信噪比低^[50−51]
	相控阵超声检测技术	所有铸件	内部缺陷	探测速度快、声束角度及深度人为可控^[52−56]	需要耦合剂、对铸件表面光滑度有要求^[52−56]
	全聚焦相控阵超声技术	所有铸件	内部缺陷	探测速度快、可高分辨率成像^[57−59]	需要耦合剂、探测手段尚未成熟^[57−59]
	激光超声检测技术	复杂铸件	内部缺陷	无需耦合剂、可探测复杂铸件、穿透能力强、对细微裂纹敏感、能检测缺陷位置及大小^[60−66]	探测手段尚未成熟
基于电磁学的无损检测技术	涡流检测技术	铁磁性铸件	表面及近表面缺陷	无需耦合剂、可在高温下检测、探测速度快、检测电信号便于数据比较与存储^[67−69]	只能探测结构简单铸件、难以定量定性地评估缺陷^[67−69]
	远场涡流技术	铁磁性铸件	表面缺陷及内部裂纹	无需耦合剂、探测速度快、对管状类铸件缺陷检测效果极佳^[70−75]	只能检测管状铸件^[70−75]
	脉冲涡流检测技术	铁磁性铸件	表面及内部缺陷	无需耦合剂、探测速度快、能对缺陷定量评估^[76−81]	易受频率影响,检测时效性低、对微小裂纹异常敏感^[76−81,96]
	脉冲涡流热成像技术	金属型铸件	表面及内部缺陷	无需耦合剂、检测结果直观、精度高、检测面积大^[83−88]	对铸件本身会有一定损耗^[83−88]
其他无损检测技术	磁粉检测技术	铁磁性铸件	表面及近表面缺陷	检测结果直观、成本低、对表面细微缺陷敏感^{[91, 95]}	需要磁悬液、对铸件表面光滑度有要求、人工参与度高^{[89, 91, 95]}
其他无损检测技术	渗透检测技术	所有铸件	表面及近表面缺陷	可探测复杂铸件、检测结果直观、成本低^[94−95]	使用试剂对人与环境有害、检测流程复杂、速度慢、人工操作、检测环境有要求^{[90, 94−95]}

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表 3 基于深度学习的铸件缺陷检测研究现状

Table 3 Research status of casting defect detection based on deep learning

方法	实验对象	检测目标	结果分析	数据来源
新的空间注意力双线性卷积神经网络	所有铸件	气孔及人工钻孔缺陷	精度高达93.30%,可以有效地学习并鉴别特征	文献[105]中的表3
基于深度学习特征匹配的铸件缺陷三维定位方法	精密铸件	0.3~1 mm 大小的渣孔缺陷	在实现自动定位的基础上精度优于传统方法,平均定位误差低于传统平移视差法8.69%	文献[106]中的表3
采用特征金字塔网络提取特征,结合区域特征聚集方式的ROI Align	汽车铸铝件	微小孔洞类缺陷	与Faster R-CNN相比,使用FPN后,平均精度提高40.9%; 使用ROI Align后,精度提高了23.6%	文献[103]中的表3~6
自适应深度与感受野选择语义分割的网络	铝合金铸件	海绵收缩、低密度异物、高密度异物、孔洞类缺陷	此方法的mIoU比最新的语义分割模型Dense-ASPP高出3.85%	文献[100]中的表3和图7
基于对象级注意机制和双线性池化构建有效的CNN模型	铸铝件	一般缺陷	对于每个定量指标(准确率、精确度、召回率), 提出的模型均优于其他经典深度学习分类模型	文献[102]中的表3~5
通用特征网络和微妙特征网络结合的网络模型	汽车铸铝件	20种铸造缺陷	该模型在实际X射线图像的每个指标上均优于其他分类模型	文献[107]中的表1
基于视觉注意力机制和特征图深度学习的鲁棒跟踪检测方法	三类工件	缩孔、孔隙率	铸件缺陷的误检率和漏检率均小于4%,缺陷检测的准确率大于96%	文献[104]中的图7
基于深度残差网络的铸件外观缺陷检测方法	汽车制动支架	暗孔、浅坑、裂纹、缺口、凸起、凹陷	ResNet-34ASoftReLu方法的准确率达到93.7%, 远远高于传统检测方法	文献[108]中的表1~2
基于自适应神经模糊推理系统的阀门铸件影像智能故障诊断系统	阀门铸件	裂纹、气体夹杂物、缩孔	缺陷分类的平均准确性为80%	文献[109]中的表3
3D卷积神经网络结合非线性拓扑尺寸参数和经验模型	二十种铸造模型	缩孔、夹杂物、裂纹	所提出的CNN在平均精度上优于现有方法的10%至20%	文献[110]中的表2
改进的Faster R-CNN算法	钢带	六类表面缺陷	以20帧每秒的速度实现了98.32%的平均精度均值, 97.02%的查全率和99%的检测率	文献[111]中的表2~3
基于选择性注意机制和深度学习特征匹配的缺陷动态跟踪检测方法	一般铸件	渣孔	误检率和漏检率均低于3%, 缺陷检测准确率超过97%. 与极线约束跟踪等方法比较, 准确率提高5个百分点以上	文献[112]中的表2~4
用于识别X射线图像中铸件缺陷的基于Mask-RCNN的体系结构	汽车铸铝件	一般缺陷	超过了缺陷检测算法在GD-Xray数据集上的最高性能, mAP达95.7%	文献[113]中的表4

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参考文献(134)

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