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.
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 networkbased signal processing method in recent years.At last,the development trend of casting defect nondestructive testing technology and its application are prospected.
作者
张辉
张邹铨
陈煜嵘
吴天月
钟杭
王耀南
ZHANG Hui;ZHANG Zou-Quan;CHEN Yu-Rong;WU Tian-Yue;ZHONG Hang;WANG Yao-Nan(School of Robotics,Hunan University,Changsha 410082;School of Electrical and Information Engineering,Changsha University of Science and Technology,Changsha 410114)
出处
《自动化学报》
EI
CAS
CSCD
北大核心
2022年第4期935-956,共22页
Acta Automatica Sinica
基金
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。
关键词
铸造缺陷
无损检测
X射线探测
神经网络
Casting defects
nondestructive testing
X-ray testing
neural network