Research Papers

Mask Image Planning for Deformation Control in Projection-Based Stereolithography Process

[+] Author and Article Information
Kai Xu

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

Yong Chen

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

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received April 15, 2014; final manuscript received February 4, 2015; published online March 5, 2015. Assoc. Editor: Joseph Beaman.

J. Manuf. Sci. Eng 137(3), 031014 (Jun 01, 2015) (12 pages) Paper No: MANU-14-1219; doi: 10.1115/1.4029802 History: Received April 15, 2014; Revised February 04, 2015; Online March 05, 2015

The mask-image-projection-based stereolithography process (MIP-SL) using a digital micromirror device (DMD) is an area-processing-based additive manufacturing (AM) process. In the MIP-SL process, a set of mask images are dynamically projected onto a resin surface to selectively cure liquid resin into layers of an object. Consequently, the MIP-SL process can be faster with a lower cost than the laser-based stereolithography apparatus (SLA) process. Currently an increasing number of companies are developing low-cost 3D printers based on the MIP-SL process. However, current commercially available MIP-SL systems are mostly based on Acrylate resins, which have larger shrinkages when compared to epoxy resins used in the laser-based SLA process. Consequently, controlling the shrinkage-related shape deformation in the MIP-SL process is challenging. In this research, we evaluate different mask image exposing strategies for building part layers and their effects on the deformation control in the MIP-SL process. Accordingly, a mask image planning method and related algorithms have been developed for a given computer-aided design (CAD) model. The planned mask images have been tested by using a commercial MIP-SL machine. The experimental results illustrate that our method can effectively reduce the deformation by as much as 32%. A discussion on the advantages and disadvantages of the mask image planning method and future research directions are also presented.

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Fig. 1

An illustration of the MIP-SL process with and without exposure mask patterns

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Fig. 2

An illustration mask image exposure strategies. (a) An input 3D CAD model; (b) a 2D slicing plane related to the 3D model; (c) a sliced layer image; (d) a mask image based on a defined mask pattern; (e) the internal portion of the mask image; and (f) the boundary portion of the mask image.

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Fig. 3

A large region in one layer is decomposed into exposures of four small regions in four layers

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Fig. 4

Using an IR camera to in situ monitor the free-surface-based MIP-SL process

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Fig. 5

An illustration of test layer and mask patterns. (a) Dimension of a test layer; (b) the mask image of the test layer; and (c) four designed mask patterns of the test layer.

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Fig. 6

Temperature plots of a curing region. (a) Using mask image of entire layer; (b) using four mask patterns in four continuous layers; and (c) using four mask patterns in one layer.

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Fig. 7

An illustration of internal volume and boundary of a given solid model

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Fig. 10

Three exposure mask patterns. (a) Isolated cube; (b) loop structure; and (c) weave structure.

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Fig. 11

Isolated cube pattern. (a) Pattern definition with gap size of 5 pixels; (b) mask image with gap size of 5 pixels; and (c) mask image with gap size of 8 pixels.

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Fig. 12

Pattern definition of loop structure and a mask image example

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Fig. 13

Pattern definition of weave structure pattern

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Fig. 14

Algorithm of exposure mask pattern generating program

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Fig. 15

Test case 1. (a) A built part and (b) schematic of the measured curl distortion.

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Fig. 16

The benchmark part. (a) Photo of a built object and (b) a two-dimensional sketch of the test part.

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Fig. 17

Relations between gap sizes and shrinkage improvement for the isolated cube exposure pattern

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Fig. 18

The effect of iteration number on gap size = 8 for the isolated cube exposure pattern

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Fig. 19

The shrinkage comparison between (a) a benchmark part and (b) a built part using the isolated cube pattern




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