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Research Papers: Design for Manufacture and the Life Cycle

Pulling at the Digital Thread: Exploring the Tolerance Stack Up Between Automatic Procedures and Expert Strategies in Scan to Print Processes

[+] Author and Article Information
Tobias Mahan

Department of Mechanical and
Nuclear Engineering,
The Pennsylvania State University,
University Park, PA 16802

Nicholas Meisel, Christopher McComb

School of Engineering Design and
Professional Programs,
The Pennsylvania State University,
University Park, PA 16802

Jessica Menold

Department of Mechanical and
Nuclear Engineering,
School of Engineering Design and
Professional Programs,
The Pennsylvania State University,
University Park, PA 16802
e-mail: jdm5407@psu.edu

Contributed by the Design for Manufacturing Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received June 22, 2018; final manuscript received October 29, 2018; published online December 20, 2018. Assoc. Editor: Tahira Reid.

J. Mech. Des 141(2), 021701 (Dec 20, 2018) (12 pages) Paper No: MD-18-1476; doi: 10.1115/1.4041927 History: Received June 22, 2018; Revised October 29, 2018

While the combination of 3D scanning and printing processes holds much promise for the field of new product development, problems with repeatability and accuracy have limited the wider spread adoption of some digital prototyping tools, such as 3D scanners. Studies have explored the errors inherent in higher fidelity scan to print (S2P) processes, yet few have explored the errors in S2P processes that leverage affordable rapid noncontact scanners. Studies have yet to explore the strategies that designers, who are experienced with additive manufacturing, employ to mitigate errors. To address these gaps, a controlled study was conducted using data from 27 scans collected with a prototypical off-the-shelf noncontact optical scanner. The geometric and dimensional integrity of the digital models was found to be significantly out of tolerance at various phases of the S2P process, as compared to the original physical model. Larger errors were found more consistently in the data acquisition phase of the S2P process, but results indicate these errors were not sufficiently filtered out during the remainder of the process. A behavioral study was conducted with 13 experienced designers in digital fabrication to determine strategies for manually cleaning Point Clouds. Actions such as increase or decrease in brush size and select or de-select points were recorded. These actions were analyzed using hidden Markov modeling, which revealed distinct patterns of behavior. Designer strategies were not beneficial and digital models produced by designers were found to be significantly smaller than original physical models.

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Figures

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Fig. 1

Adapted Ishikawa diagram [36] highlighting influential factors geometric and dimensional accuracies of S2P processes

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Fig. 2

Experiment timeline depicting two distinct phases of the study

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Fig. 3

Flow of model data through data acquisition (raw Point Cloud) to preparation for 3D printing (sliced file). Note: the Point Clouds in the figure, are reduced Point Clouds so that individual points in the data can be perceived.

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Fig. 4

(a) CAD model of part; (b) final printed part in VeroWhite photopolymer via a material jetting process

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Fig. 5

Standard S2P Process as performed in this work. All boxes in top row represent Automatic Processes. Box in bottom row represents process performed manually by designer. Dots indicate points at which data are taken.

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Fig. 6

Timeline of design session

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Fig. 7

Example sequence of operations during cleaning processes

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Fig. 8

Calculation of width and length per cube

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Fig. 9

Calculation of flatness per cube

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Fig. 10

Plots of the LOOCV log-likelihood values highlighting (a) preferred order and (b) preferred number of hidden states

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Fig. 11

Distribution of width in truth model and digital models from Point Cloud, STL, and G-Code

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Fig. 12

Distribution of length in digital models from Point Cloud, STL, and G-Code

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Fig. 13

Distribution of flatness in digital models from Point Cloud, STL, and G-Code

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Fig. 14

Contour plot of extreme outlier in flatness of STL digital model

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Fig. 15

Transmission and emission matrices for two-state hidden Markov states

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Fig. 16

Visual representation of two-state hidden Markov model based on data from design sessions

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Fig. 17

Distribution of width in truth model and Point Clouds produced by designers and automatic processes

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Fig. 18

Distribution of length in truth model and Point Clouds produced by designers and automatic processes

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Fig. 19

Distribution of flatness in truth model and Point Clouds produced by designers and automatic processes

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