Research Papers

Statistical Predictive Modeling and Compensation of Geometric Deviations of Three-Dimensional Printed Products

[+] Author and Article Information
Qiang Huang

Daniel J. Epstein Department of Industrial and
Systems Engineering,
University of Southern California,
Los Angeles, CA 90089
e-mail: qiang.huang@usc.edu

Hadis Nouri, Kai Xu, Yong Chen

Daniel J. Epstein Department of Industrial and
Systems Engineering,
University of Southern California,
Los Angeles, CA 90089

Sobambo Sosina, Tirthankar Dasgupta

Department of Statistics,
Harvard University,
Cambridge, MA 02138

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received April 14, 2014; final manuscript received August 27, 2014; published online October 24, 2014. Assoc. Editor: David L. Bourell.

J. Manuf. Sci. Eng 136(6), 061008 (Oct 24, 2014) (10 pages) Paper No: MANU-14-1176; doi: 10.1115/1.4028510 History: Received April 14, 2014; Revised August 27, 2014

Geometric fidelity of 3D printed products is critical for additive manufacturing (AM) to be a direct manufacturing technology. Shape deviations of AM built products can be attributed to multiple variation sources such as substrate geometry defect, disturbance in process variables, and material phase change. Three strategies have been reported to improve geometric quality in AM: (1) control process variables x based on the observed disturbance of process variables Δx, (2) control process variables x based on the observed product deviation Δy, and (3) control input product geometry y based on the observed product deviation Δy. This study adopts the third strategy which changes the computer-aided design (CAD) design by optimally compensating the product deviations. To accomplish the goal, a predictive model is desirable to forecast the quality of a wide class of product shapes, particularly considering the vast library of AM built products with complex geometry. Built upon our previous optimal compensation study of cylindrical products, this work aims at a novel statistical predictive modeling and compensation approach to predict and improve the quality of both cylindrical and prismatic parts. Experimental investigation and validation of polyhedrons a indicates the promise of predicting and compensating a wide class of products built through 3D printing technology.

Copyright © 2014 by ASME
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Fig. 2

In-plane error of cylindrical parts with r0 = 0.5 in.,1 in.,2 in.,3 in. and validation for 2.5 in. cylinder [8]. (a) Observed and posterior predictive distribution and (b) validation result of 2:5 in. cylinder.

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Fig. 1

Deformation representation under the PCS [8]. (a) Polar coordinate representation and (b) deformation under polar coordinates.

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Fig. 3

An illustration of the MIP-SLA process [17]

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Fig. 4

Polygon experimental design. (a) Regular polygon with circumcircle radius r and (b) irregular polygon with circumcircle radius r.

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Fig. 5

Deviation profiles (solid lines) of cylinders with r0 = 1 in.,2 in. and square shapes with side length = 1 in.,2 in.,3 in. (a) Cylinders and square after repair and (b) square shapes before repair.

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Fig. 6

Observed deviation profiles for pentagons. (a) Two regular pentagon deviation profiles with circumcircle radii = 1 in., 3 in. (solid lines) and (b) printed regular pentagon with circumcircle radius = 3 in.

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Fig. 7

Observed deviation profile for a dodecagon. (a) Regular dodecagon deviation profile (solid line) with circumcircle radius = 3 in. and (b) printed dodecagon with circumcircle radius = 3 in.

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Fig. 8

Square wave functions with square, pentagon, and dodecagon deviation profiles

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Fig. 9

Sawtooth wave functions

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Fig. 10

Integrated model prediction and validation. (a) Polygon deviation profiles and model predictions and (b) Model validation by predicting dodecagon deviation profile.

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Fig. 11

Optimal compensation and experimental validation. (a) Optimal compensation x*(θ) and (b) Dodecagon deviation profiles before and after compensation.



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