Defect inspection technologies for additive manufacturing
Технологии контроля дефектов в аддитивном производстве
2021-01-28
SCID: 54.1/wrmc3ay3
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additive manufacturingdeep learningdefect inspectioninternal defectssurface defect detection
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Abstract (AI)
Abstract 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.
Key Findings
1
Additive manufacturing components can contain powder agglomeration, balling, porosity, internal cracks, and thermal or internal stresses.
2
Future directions for additive manufacturing defect inspection technologies are identified.
3
The paper reviews defect architectures across additive manufacturing processes and summarizes corresponding inspection technologies and applications.
4
These defects can substantially impair the quality, mechanical properties, and safety of additively manufactured parts.
5
Traditional defect detection methods and deep-learning-based surface defect inspection approaches are comprehensively surveyed.
Research Object
Additive manufacturing processes and their fabricated components
Research Subject
Internal and surface defect architectures, inspection technologies, and their effects on the quality, mechanical properties, and safety of additive-manufactured components
Publication Details
Publication Date
2021-01-28
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