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Research Papers

Image-Based Slicing and Tool Path Planning for Hybrid Stereolithography Additive Manufacturing

[+] Author and Article Information
Hang Ye

Department of Industrial and
Systems Engineering,
University at Buffalo,
The State University of New York,
Buffalo, NY 14260
e-mail: hye2@buffalo.edu

Chi Zhou

Department of Industrial and
Systems Engineering,
University at Buffalo,
The State University of New York,
Buffalo, NY 14260
e-mail: chizhou@buffalo.edu

Wenyao Xu

Department of Computer Science
and Engineering,
University at Buffalo,
The State University of New York,
Buffalo, NY 14260
e-mail: wenyaoxu@buffalo.edu

1Corresponding author.

Manuscript received September 21, 2016; final manuscript received December 21, 2016; published online March 8, 2017. Assoc. Editor: Zhijian J. Pei.

J. Manuf. Sci. Eng 139(7), 071006 (Mar 08, 2017) (9 pages) Paper No: MANU-16-1512; doi: 10.1115/1.4035795 History: Received September 21, 2016; Revised December 21, 2016

The hybrid stereolithography (SLA) process integrates a laser scanning-based system and a mask projection-based system. Multiple laser paths are used to scan the border of a 2D pattern, whereas a mask image is adopted to solidify the interior area. By integrating merits of two subsystems, the hybrid SLA process can achieve high surface quality without sacrificing productivity. For the hybrid system, closed polygonal contours are required to direct the laser scanning, and a binary image is also needed for the mask projection. We proposed a novel image-based slicing method. This approach can convert a 3D model into a series of binary images directly, and each image is corresponding to the cross section of the model at a specific height. Based on the resultant binary image, we use an image processing method to gradually shrink the pattern in the image. Boundaries of the shrunk image are traced and then restored as polygons to direct the laser spot movement. The final shrunk image serves as the input for the mask projection. Experimental results of test cases demonstrate that the proposed method is substantially more efficient than the traditional approaches. Its accuracy is also studied and discussed.

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Figures

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Fig. 1

Bunny model: (a) original 3D model, (b) original contour, (c) first offset contour, (d) second offset contour, (e) third offset contour, (f) mask image, (g) original image, (h) first shrunk image, (i) second shrunk image, (j) third shrunk image, (k) final mask image, (l) first reconstructed contour, (m) second reconstructed contour, and (n) third reconstructed contour

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Fig. 2

Sampling point conversion: (a) head model and (b) hand model

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Fig. 3

Three-dimensional model used for image generation demonstration

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Fig. 4

Examples of structuring element

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Fig. 5

Comparison of solid offset and image processing methods

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Fig. 6

Polygonal contour and image contour center chain: (a) an example case where assumption is valid and (b) an example case where assumption is invalid

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Fig. 7

Corner-connected case

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