Utilizing computer vision and artificial intelligence algorithms to predict and design the mechanical compression response of direct ink write 3D printed foam replacement structures

Roach, Devin John; Rohskopf, Andrew; Hamel, Craig M.; Reinholtz, William D.; Bernstein, Robert; Qi, H. Jerry; Cook, Adam W.

doi:10.1016/j.addma.2021.101950

Title: Utilizing computer vision and artificial intelligence algorithms to predict and design the mechanical compression response of direct ink write 3D printed foam replacement structures

Journal Article · Sat Mar 20 00:00:00 EDT 2021 · Additive Manufacturing

DOI:https://doi.org/10.1016/j.addma.2021.101950· OSTI ID:1773284

Roach, Devin John ^[1]; Rohskopf, Andrew ^[2]; Hamel, Craig M. ^[3]; Reinholtz, William D. ^[3]; Bernstein, Robert ^[3]; Qi, H. Jerry ^[4]; Cook, Adam W. ^[3]

Georgia Inst. of Technology, Atlanta, GA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Georgia Inst. of Technology, Atlanta, GA (United States)

Additive Manufacturing (AM) of porous polymeric materials, such as foams, recently became a topic of intensive research due their unique combination of low density, impressive mechanical properties, and stress dissipation capabilities. Conventional methods for fabricating foams rely on complex and stochastic processes, making it challenging to achieve precise architectural control of structured porosity. In contrast, AM provides access to a wide range of printable materials, where precise spatial control over structured porosity can be modulated during the fabrication process enabling the production of foam replacement structures (FRS). Current approaches for designing FRS are based on intuitive understanding of their properties or an extensive number of finite element method (FEM) simulations. These approaches, however, are computationally expensive and time consuming. As such, in this work, we present a novel methodology for determining the mechanical compression response of direct ink write (DIW) 3D printed FRS using a simple cross-sectional image. By obtaining measurement data for a relatively small number of samples, an artificial neural network (ANN) was trained, and a computer vision algorithm was used to make inferences about foam compression characteristics from a single cross-sectional image. Finally, a genetic algorithm (GA) was used to solve the inverse design problem, generating the AM printing parameters that an engineer should use to achieve a desired compression response from a DIW printed FRS. The methods developed herein present an avenue for entirely autonomous design and analysis of additively manufactured structures using artificial intelligence.

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Research Organization:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC04-94AL85000; NA0003525

OSTI ID:: 1773284

Alternate ID(s):: OSTI ID: 1817780

Report Number(s):: SAND-2021-3420J; 694962

Journal Information:: Additive Manufacturing, Vol. 41; ISSN 2214-8604

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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