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

Cited 6 time in Web of Science Cited 6 time in Scopus

Kim, Namjung; Yang, Chen; Lee, Howon; Aluru, Narayana R.

Issue Date
Applied Sciences-basel, Vol.9 No.6, p. 1093
Featured Application The proposed modeling framework helps to generate a mathematical spatial roughness model including the staircase side profile as well as spatial uncertainty of additively manufactured parts, which might significantly affect the mechanical behavior of printed parts. This general approach can be applied to any complex AM parts which have spatial roughness that can be obtained via optical images. Abstract 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.
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Related Researcher

  • College of Engineering
  • Department of Mechanical Engineering
Research Area Additive Manufacturing, Architected Materials, Programmable Matter


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