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PAPERS: Process Planning Considerations for AM

Enhancing the Structural Performance of Additively Manufactured Objects Through Build Orientation Optimization

[+] Author and Article Information
Erva Ulu, Emrullah Korkmaz, Kubilay Yay, O. Burak Ozdoganlar

Department of Mechanical Engineering,
Carnegie Mellon University,
Pittsburgh, PA 15213

Levent Burak Kara

Department of Mechanical Engineering,
Carnegie Mellon University,
Pittsburgh, PA 15213
e-mail: lkara@cmu.edu

1Corresponding author.

Contributed by the Design for Manufacturing Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 2, 2015; final manuscript received May 22, 2015; published online October 12, 2015. Assoc. Editor: David Rosen.

J. Mech. Des 137(11), 111410 (Oct 12, 2015) (9 pages) Paper No: MD-15-1175; doi: 10.1115/1.4030998 History: Received March 02, 2015; Revised May 22, 2015

Additively manufactured objects often exhibit directional dependencies in their structure due to the layered nature of the printing process. While this dependency has a significant impact on the object's functional performance, the problem of finding the best build orientation to maximize structural robustness remains largely unsolved. We introduce an optimization algorithm that addresses this issue by identifying the build orientation that maximizes the factor of safety (FS) of an input object under prescribed loading and boundary configurations. First, we conduct a minimal number of physical experiments to characterize the anisotropic material properties. Next, we use a surrogate-based optimization method to determine the build orientation that maximizes the minimum factor safety. The surrogate-based optimization starts with a small number of finite element (FE) solutions corresponding to different build orientations. The initial solutions are progressively improved with the addition of new solutions until the optimum orientation is computed. We demonstrate our method with physical experiments on various test models from different categories. We evaluate the advantages and limitations of our method by comparing the failure characteristics of parts printed in different orientations.

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Figures

Grahic Jump Location
Fig. 1

Our approach takes as input a 3D model of an object with the corresponding loading/boundary configurations and anisotropic (orthotropic) material properties, then calculates an optimum build orientation that maximizes the FS. The build orientation is defined by three Euler angles [α,β,γ]. A surrogate model between the candidate orientations and the objective function is constructed. The surrogate model is progressively improved with the addition of new candidate orientations until the optimal orientation is found.

Grahic Jump Location
Fig. 2

Principal directions in the orthotropic material model: (a) a single layer in AM process where x and y are the in-plane principal directions and (b) the layer accumulation (build) direction, z

Grahic Jump Location
Fig. 3

Geometry a, b and material x, y coordinate frames. The top row shows an example for the equivalent representations of the same physical problem. The bottom row illustrates our case where there are two distinct build orientations and separate FE simulations are required to obtain the stress information for each configuration. Hence, while stress transformation formulas seem to be applicable here, they are indeed not applicable in our problem.

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Fig. 4

Material characterization: (a) Print directions considered, (b) experimental setup for tensile tests, and (c) a typical stress–strain curve showing a subset of the material properties to be extracted

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Fig. 5

Repeatability tests for: (a) orientation 2, (b) orientation 5, and (c) the stress–strain curves for different build orientations shown in Fig. 4

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Fig. 6

Performance of our objective function. (a) Problem configuration. Elements with the lowest 300 safety factor values are highlighted for the initial (b), and the optimized (c), build orientations.

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Fig. 7

Histograms of the lowest 300 FS values for the initial and optimized problem configurations of Fig. 6. Note the improvement in the minimum FS, as well as the general shift toward the right.

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Fig. 8

(a) Initial design of experiments, (b) constructing a surrogate model, (c) selecting new samples, and (d) enhancing the surrogate model. (b) and (c) are repeated until a certain stopping criteria is satisfied. Samples are represented with dots. Transparent surface is the exact objective function and shaded surface is the surrogate model constructed with the selected samples.

Grahic Jump Location
Fig. 9

Comparison of surrogate-based optimization with brute force approach. Samples representing different build orientations are shown with dots. The color of each dot represents the normalized objective function value for the corresponding orientation. The best orientations obtained with the two methods and the corresponding minimum FS values are also shown.

Grahic Jump Location
Fig. 10

Performance of surrogate-based optimization for problem configuration in Fig. 9 with respect to the sample size. The circle shows the best objective value that can be obtained using the brute force approach. With surrogate-based optimization, the same performance level of brute force approach can be obtained with only 215 samples.

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Fig. 11

Effect of different loading configurations on the optimum build orientation. Left column shows the problem configuration and right column shows the corresponding optimum build orientations.

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Fig. 12

Build orientation optimization results for three different problem configurations. Left column shows the problem settings. Middle column shows the distribution of the lowest 300 FS values over the geometry for the initial and optimum orientations. Right column shows the optimum build orientations.

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Fig. 13

Performance evaluation of our algorithm with a standard dog-bone tensile specimen

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Fig. 14

Performance evaluation of our algorithm with a custom-designed part

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