Correlation approach for quality assurance of additive manufactured parts based on optical metrology

Zhang, Xiao; Zheng, Yi; Suresh, Vignesh; Wang, Shaodong; Li, Qing; Li, Beiwen; Qin, Hantang

doi:10.1016/j.jmapro.2020.02.037

Correlation approach for quality assurance of additive manufactured parts based on optical metrology

Journal Article · Sat Feb 29 00:00:00 EST 2020 · Journal of Manufacturing Processes

DOI:https://doi.org/10.1016/j.jmapro.2020.02.037· OSTI ID:1799404

Zhang, Xiao ^[1]; Zheng, Yi ^[2]; Suresh, Vignesh ^[2]; Wang, Shaodong ^[2]; Li, Qing ^[2]; Li, Beiwen ^[2]; ^[2]

Iowa State Univ., Ames, IA (United States); OSTI
Iowa State Univ., Ames, IA (United States)

Surface topography and surface finish are two significant factors for evaluating the quality of products in additive manufacturing (AM). AM parts are fabricated layer by layer, which is quite different from traditional formative or subtractive methods. Despite rapid progress in additive manufacturing and associated optical metrology for quality control and in-situ monitoring, limited research has been conducted to investigate the reliability of 3D surface measurement data. The surface topologies scanned by multiple optical systems demonstrated significant differences due to varying sampling mechanisms, resolutions, system noises, etc. The 3D datasets should be trustworthy in order to extract parameters for quality assurance or feedback control from 3D surface measurements. In this paper, we set up new standards to evaluate the reliability of 3D surface measurement data and analyze the variation in the topographical profile. In this study, two non-contact optical methods based on Focus Variation Microscopy (FVM) and Structured Light System (SLS) were adopted to measure the surface topography of the target components. The two optical metrology systems generated two entirely different point cloud datasets. Statistical methods were applied to test the difference between the data obtained from the two systems. By using a data analytics approach for comparison, it was found that the surface roughness estimated from the point cloud data sets of FVM and SLS has no significant difference, though the point cloud data sets were completely different. This paper provides standard validation approach to evaluate the plausibility of metrology data from in-situ real-time surface analysis for process planning of AM.

View Accepted Manuscript (DOE)

Research Organization:: Sustainable Manufacturing Innovation Alliance Corp., West Henrietta, NY (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office

Grant/Contract Number:: EE0007897

OSTI ID:: 1799404

Alternate ID(s):: OSTI ID: 1780111

Journal Information:: Journal of Manufacturing Processes, Journal Name: Journal of Manufacturing Processes Vol. 53; ISSN 1526-6125

Publisher:: Society of Manufacturing Engineers; ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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