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Title: Strut diameter uncertainty prediction by deep neural network for additively manufactured lattice structures [Conference Object]
Authors: Gorguluarslan R.M.
Ates G.C.
Gungor O.U.
Yamaner Y.
Keywords: Deep neural network
Lattice structure
Material extrusion
3D printers
Computation theory
Computer aided design
Deep neural networks
Stochastic systems
Uncertainty analysis
Computer-aided design
Effective diameter
Geometric uncertainties
Lattice structures
Material extrusion
Mechanical performance
Modeling parameters
Neural network model
Number of samples
Issue Date: 2021
Publisher: American Society of Mechanical Engineers (ASME)
Abstract: Additive manufacturing introduces geometric uncertainties on the fabricated strut members of lattice structures. These uncertainties lead to deviations between the simulation result and the fabricated mechanical performance. Although these uncertainties can be characterized and quantified in the existing literature, the generation of a high number of samples for the quantified uncertainties to use in the computer-aided design of lattice structures for different strut diameters and angles requires high experimental effort and computational cost. The use of deep neural network models to accurately predict the samples of uncertainties is studied in this research to address this issue. For the training data, the geometric uncertainties on the fabricated struts introduced by the material extrusion process are characterized from microscope measurements using random field theory. These uncertainties are propagated to effective diameters of the strut members using a stochastic upscaling technique. The relationship between the deterministic strut model parameters, namely the model diameter and angle, and the effective diameter with propagated uncertainties is established through a deep neural network model. The validation data results show accurate predictions for the effective diameter when model parameters are given as inputs. Thus, the proposed model has the potential to use the fabricated results in the design optimization processes without requiring computationally expensive repetitive simulations. Copyright © 2021 by ASME
Description: Computers and Information in Engineering Division;Design Engineering Division
41st Computers and Information in Engineering Conference, CIE 2021, Held as Part of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2021 -- 17 August 2021 through 19 August 2021 -- -- 174204
ISBN: 9.78079E+12
Appears in Collections:Makine Mühendisliği Bölümü / Department of Mechanical Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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