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...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.展开更多
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)。
文摘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.
