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Research Papers

Part Build Orientation Optimization and Neural Network-Based Geometry Compensation for Additive Manufacturing Process

[+] Author and Article Information
Sushmit Chowdhury, Kunal Mhapsekar

Department of Mechanical and
Materials Engineering,
Center for Global Design and Manufacturing,
University of Cincinnati,
Cincinnati 45220, OH

Sam Anand

Department of Mechanical and
Materials Engineering,
Center for Global Design and Manufacturing,
University of Cincinnati,
Cincinnati 45220, OH
e-mail: sam.anand@uc.edu

1Corresponding author.

Manuscript received June 3, 2017; final manuscript received September 20, 2017; published online December 21, 2017. Assoc. Editor: Zhijian J. Pei.

J. Manuf. Sci. Eng 140(3), 031009 (Dec 21, 2017) (15 pages) Paper No: MANU-17-1356; doi: 10.1115/1.4038293 History: Received June 03, 2017; Revised September 20, 2017

Significant advancements in the field of additive manufacturing (AM) have increased the popularity of AM in mainstream industries. The dimensional accuracy and surface finish of parts manufactured using AM depend on the AM process and the accompanying process parameters. Part build orientation is one of the most critical process parameters, since it has a direct impact on the part quality measurement metrics such as cusp error, manufacturability concerns for geometric features such as thin regions and small fusible openings, and support structure parameters. In conjunction with the build orientation, the cyclic heating and cooling of the material involved in the AM processes lead to nonuniform deformations throughout the part. These factors cumulatively affect the design conformity, surface finish, and the postprocessing requirements of the manufactured parts. In this paper, a two-step part build orientation optimization and thermal compensation methodology is presented to minimize the geometric inaccuracies resulting in the part during the AM process. In the first step, a weighted optimization model is used to determine the optimal build orientation for a part with respect to the aforementioned part quality and manufacturability metrics. In the second step, a novel artificial neural network (ANN)-based geometric compensation methodology is used on the part in its optimal orientation to make appropriate geometric modifications to counteract the thermal effects resulting from the AM process. The effectiveness of this compensation is assessed on an example part using a new point cloud to part conformity metric and shows significant improvements in the manufactured part's geometric accuracy.

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Figures

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Fig. 5

Layerwise identification of small openings using ray tracing

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Fig. 4

Layerwise thin feature analysis of a sample hip implant part

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Fig. 3

Rays traced from the contour points on the intersection of slice plane and a facet

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Fig. 2

Design parameters considered in the optimization model for build orientation

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Fig. 1

Outline of the preprocess optimization methodology to achieve optimum manufacturability and part quality in AM

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Fig. 6

Layerwise identification of sharp corners on a sample part

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Fig. 7

Identification of support facets with angle criterion

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Fig. 8

Test parts showing SCA

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Fig. 9

Support structures generated for sample test parts

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Fig. 10

Support structure removability check using ray tracing

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Fig. 11

SSA for various sample test parts

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Fig. 12

Concept of cusp error in AM

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Fig. 13

Methodology for ANN model-based CAD geometry compensation

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Fig. 16

ANN-based geometric compensation model training schematic

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Fig. 14

Comparison of original and the deformed geometry for a bracket

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Fig. 15

Schematic representation of a feedforward ANN model

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Fig. 17

Regression plot of ANN training process for a sample part

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Fig. 18

Schematic of compensated STL file generation using trained ANN model

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Fig. 19

Schematic of the point cloud to part CS calculation methodology

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Fig. 20

Test part—aircraft bearing bracket

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Fig. 21

Test part in optimum build orientation

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Fig. 23

Comparison of precompensation deformed geometry with the original CAD geometry

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Fig. 24

Compensated STL resulting from the ANN-based compensation model

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Fig. 25

Result of AM simulation with compensated part geometry

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Fig. 26

Comparison of postcompensation part geometry with original CAD geometry

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Fig. 27

Overview of the two-step methodology for the test part

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Fig. 22

Precompensation deformed geometry

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