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Research Papers

Environmental Performance Evaluation of a Fast Mask Image Projection Stereolithography Process Through Time and Energy Modeling

[+] Author and Article Information
Hari P. N. Nagarajan

School of Mechanical, Industrial,
and Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331
e-mail: nagarajh@oregonstate.edu

Harsha A. Malshe

School of Mechanical, Industrial,
and Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331
e-mail: malsheh@oregonstate.edu

Karl R. Haapala

School of Mechanical, Industrial,
and Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331
e-mail: karl.haapala@oregonstate.edu

Yayue Pan

Department of Mechanical
and Industrial Engineering,
University of Illinois at Chicago,
2039 Engineering Research Facility,
Chicago, IL 60607
e-mail: yayuepan@uic.edu

1Corresponding author.

Manuscript received January 15, 2016; final manuscript received May 28, 2016; published online August 3, 2016. Editor: Y. Lawrence Yao.

J. Manuf. Sci. Eng 138(10), 101004 (Aug 03, 2016) (10 pages) Paper No: MANU-16-1041; doi: 10.1115/1.4033756 History: Received January 15, 2016; Revised May 28, 2016

The emergence of additive manufacturing (AM) has potential for dramatic changes in labor productivity and economic welfare. With the growth of AM, understanding of the sustainability performance of relevant technologies is required. Toward that goal, an environmental impact assessment (EIA) approach is undertaken to evaluate an AM process. A novel fast mask image projection stereolithography (MIP-SL) process is investigated for the production of six functional test parts. The materials, energy, and wastes are documented for parts fabricated using this process. The EIA is completed for human health, ecosystem diversity, and resource costs using the ReCiPe 2008 impact assessment method. It is noted that process energy, in the form of electricity, is the key contributor for all three damage types. The results are used to depict the underlying relationship between energy consumed and the environmental impact of the process. Thus, to facilitate prediction of process energy utilization, a mathematical model relating shape complexity and dimensional size of the part with respect to part build time and washing time is developed. The effectiveness of this model is validated using data from real-time process energy monitoring. This work quantifies the elemental influence of design features on AM process energy consumption and environmental impacts. While focused on the environmental performance of the fast MIP-SL process, the developed approach can be extended to evaluate other AM processes and can encompass a triple bottom line analysis approach for sustainable design by predicting environmental, economic, and social performance of products.

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References

Figures

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Fig. 1

Fast MIP-SL system

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Fig. 2

Parts manufactured using MIP-SL process: (a) gear, (b) head, (c) statue, (d) shell, (e) teeth, and (f) brush

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Fig. 3

Environmental impacts of each fabricated part with postprocessing (method: ReCiPe endpoint (H) V1.03/world ReCiPe H/A, functional unit = 1000 parts)

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Fig. 4

Environmental impacts of each fabricated part without postprocessing (method: ReCiPe endpoint (H) V1.03/world ReCiPe H/A, functional unit = 1000 parts)

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Fig. 5

Relative environmental impacts of the shell part (method: ReCiPe endpoint (H) V1.03/world ReCiPe H/A, functional unit = 1000 parts)

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Fig. 6

Relative environmental impacts of the head part without postprocessing (method: ReCiPe endpoint (H) V1.03/world ReCiPe H/A, functional unit = 1000 parts)

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Fig. 7

Variation of fast MIP-SL machine energy consumed with respect to build time for the fabricated parts

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Fig. 8

Variation of postprocessing energy with respect to postprocessing time for the fabricated parts

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Fig. 9

Total calculated and measure energy consumed with respect to total time for each part produced using Fast MIP-SL

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