Next Article in Journal
Micro-Brazing of Stainless Steel Using Ni-P Alloy Plating
Next Article in Special Issue
Effect of Process Parameters on the Generated Surface Roughness of Down-Facing Surfaces in Selective Laser Melting
Previous Article in Journal
Pore Solution Chemistry of Calcium Sulfoaluminate Cement and Its Effects on Steel Passivation
Previous Article in Special Issue
Fabrication of Multiscale-Structure Wafer-Level Microlens Array Mold
Open AccessArticle

Spatial Uncertainty Modeling for Surface Roughness of Additively Manufactured Microstructures via Image Segmentation

1
Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, 1206 W. Green ST., Urbana, IL 61801, USA
2
Department of Mechanical and Aerospace Engineering, Rutgers University–New Brunswick, 98 Brett Rd., Piscataway, NJ 08854, USA
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(6), 1093; https://doi.org/10.3390/app9061093
Received: 6 February 2019 / Revised: 8 March 2019 / Accepted: 11 March 2019 / Published: 15 March 2019
(This article belongs to the Special Issue Micro/Nano Manufacturing)
Despite recent advances in additive manufacturing (AM) that shifts the paradigm of modern manufacturing by its fast, flexible, and affordable manufacturing method, the achievement of high-dimensional accuracy in AM to ensure product consistency and reliability is still an unmet challenge. This study suggests a general method to establish a mathematical spatial uncertainty model based on the measured geometry of AM microstructures. Spatial uncertainty is specified as the deviation between the planned and the actual AM geometries of a model structure, high-aspect-ratio struts. The detailed steps of quantifying spatial uncertainties in the AM geometry are as follows: (1) image segmentation to extract the sidewall profiles of AM geometry; (2) variability-based sampling; (3) Gaussian process modeling for spatial uncertainty. The modeled spatial uncertainty is superimposed in the CAD geometry and finite element analysis is performed to quantify its effect on the mechanical behavior of AM struts with different printing angles under compressive loading conditions. The results indicate that the stiffness of AM struts with spatial uncertainty is reduced to 70% of the stiffness of CAD geometry and the maximum von Mises stress under compressive loading is significantly increased by the spatial uncertainties. The proposed modeling framework enables the high fidelity of computer-based predictive tools by seamlessly incorporating spatial uncertainties from digital images of AM parts into a traditional finite element model. It can also be applied to parts produced by other manufacturing processes as well as other AM techniques. View Full-Text
Keywords: spatial uncertainty modeling; additive manufacturing; uncertainty quantification; Image segmentation; gaussian process modeling spatial uncertainty modeling; additive manufacturing; uncertainty quantification; Image segmentation; gaussian process modeling
Show Figures

Figure 1

MDPI and ACS Style

Kim, N.; Yang, C.; Lee, H.; Aluru, N.R. Spatial Uncertainty Modeling for Surface Roughness of Additively Manufactured Microstructures via Image Segmentation. Appl. Sci. 2019, 9, 1093. https://doi.org/10.3390/app9061093

AMA Style

Kim N, Yang C, Lee H, Aluru NR. Spatial Uncertainty Modeling for Surface Roughness of Additively Manufactured Microstructures via Image Segmentation. Applied Sciences. 2019; 9(6):1093. https://doi.org/10.3390/app9061093

Chicago/Turabian Style

Kim, Namjung; Yang, Chen; Lee, Howon; Aluru, Narayana R. 2019. "Spatial Uncertainty Modeling for Surface Roughness of Additively Manufactured Microstructures via Image Segmentation" Appl. Sci. 9, no. 6: 1093. https://doi.org/10.3390/app9061093

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop