Defect inspection technologies for additive manufacturing

作     者：Yao Chen Xing Peng Lingbao Kong Guangxi Dong Afaf Remani Richard Leach Yao Chen;Xing Peng;Lingbao Kong;Guangxi Dong;Afaf Remani;Richard Leach

作者机构：Shanghai Engineering Research Center of Ultra-precision Optical ManufacturingDepartment of Optical Science and EngineeringFudan UniversityShanghaiPeople’s Republic of China Manufacturing Metrology TeamUniversity of NottinghamNottinghamUnited Kingdom 

出 版 物：《International Journal of Extreme Manufacturing》 (极端制造（英文）)

年 卷 期：2021年第3卷第2期

页      面：23-43页

核心收录：

学科分类：0817[工学-化学工程与技术] 08[工学] 0807[工学-动力工程及工程热物理] 0802[工学-机械工程] 0811[工学-控制科学与工程] 080201[工学-机械制造及其自动化] 

基　　金：financial support of the National Key R&D Program of China (Project Nos. 2017YFA0701200, 2016YFF0102003) the Shanghai Science and Technology Committee Innovation Grant (Grant Nos. 19ZR1404600, 17JC1400601) the Science Challenging Program of CAEP (Grant No. JCKY2016212A506-0106) 

主　　题：additive manufacturing defect inspection machine learning deep learning neural network 

摘      要：Additive manufacturing(AM) technology is considered one of the most promising manufacturing technologies in the aerospace and defense industries. However, AM components are known to have various internal defects, such as powder agglomeration, balling, porosity,internal cracks and thermal/internal stress, which can significantly affect the quality, mechanical properties and safety of final parts. Therefore, defect inspection methods are important for reducing manufactured defects and improving the surface quality and mechanical properties of AM components. This paper describes defect inspection technologies and their applications in AM processes. The architecture of defects in AM processes is reviewed. Traditional defect detection technology and the surface defect detection methods based on deep learning are summarized, and future aspects are suggested.