Production Engineering

, Volume 13, Issue 6, pp 675–684 | Cite as

Reusable unit process life cycle inventory for manufacturing: stereolithography

  • Timothy Simon
  • Yiran Yang
  • Wo Jae Lee
  • Jing Zhao
  • Lin Li
  • Fu ZhaoEmail author
Production Process


Additive manufacturing technologies have been implemented in a variety of industries such as aerospace, automotive, healthcare, and military. The increasing application is owing to the unique capability of AM for fabricating parts layer by layer. Compared to traditional manufacturing processes, AM can achieve enhanced design and manufacturing complexity. In addition, it has been argued that AM could also reduce the environmental footprint of a product. However, recent studies reveal that AM processes may cause environmental consequences. Characterization of manufacturing processes for their environmental performance can be achieved by evaluating input and output material and energy flows. The unit process life cycle inventory methodology is a promising approach to develop reusable models and tools to calculate these flows. The unit process life cycle inventory models can be connected to estimate the total material/energy consumption of and emissions from product manufacturing based on a process plan. In this paper, we develop a unit process life cycle inventory model for one of the most widely used additive manufacturing processes i.e., Stereolithography, which fabricates parts by using ultraviolet light to solidify the photosensitive liquid resin in a matter of seconds layer by layer. The model is constructed with the knowledge of physical and chemical principles and is based on production information and equipment specifications. A case study is provided to demonstrate how to use the unit process life cyle inventory model on Stereolithography.


Stereolithography Mask image projection Additive manufacturing Unit process life cycle inventory 



Additive manufacturing


Unit process life cycle inventory






Life cycle inventory

List of symbols


Total energy consumption (Wh)


Projector total energy consumption (Wh)


Energy of motor (Wh)


Total energy consumption of the computer (Wh)


Total energy consumption of the computer (Wh)


Projector curing stage energy consumption (Wh)


Projector idle stage energy consumption (Wh)


Computer rated power (W)


Control board rated power (W)


Curing power (W)


Projector idle stage power (W)


Power requirement of motor (W)


Total number of bottom layers (#)


Total number of layers (#)


Layer thickness (mm)


Printing time (s)


Layer lift and sequence time (s)


Curing time for each bottom layers (s)


Curing time for each layer (s)


Total idle time (s)


Post-processing time (s)


Pre-processing time (s)

\(ER\left( t \right)_{volatilization}\)

VOC emission due to the volatilization process (g/s)


Surface area of printed part (cm3)


Volume of printed part (m3)


Mass of input raw materials, liquid (g)


Mass of finished part, solid (g)


Mass of gaseous VOC emission (g)


Liquid waste percentage (g)


Total material waste (g)


Air flow speed (cm/s)


Liquid resin coating thickness (cm)


Air density (kg/m3)


Solid part density (kg/m3)


Resin density (kg/m3)


Vapor pressure of liquid (atm)

\(\Delta z\)

Length of air–liquid interface in the direction of flow (cm)


Air diffusivity (cm/s)


Mass transfer coefficient (cm/s)


Molecular weight of resin (g/g mole)


Ambient air pressure (atm)


Mass transfer rate (g mole/cm3 s)


Universal gas constant (cm3/kPa/g mole K)


Surface area (cm2)


The Schmidt number (cm)


Absolute temperature (K)


Air viscosity (g/cm/s)



This work is supported by the National Science Foundation under Grant no. 1605472. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. This work is also supported by the U.S. National Science Foundation under Grant number 1604825.


  1. 1.
    Huang SH, Liu P, Mokasdar A, Hou L (2013) Additive manufacturing and its societal impact: a literature review. Int J Adv Manuf Technol 67(5–8):1191–1203CrossRefGoogle Scholar
  2. 2.
    Sreenivasan R, Goel A, Bourell DL (2010) Sustainability issues in laser-based additive manufacturing. Phys Procedia 5(PART 1):81–90CrossRefGoogle Scholar
  3. 3.
    Ford S, Despeisse M (2016) Additive manufacturing and sustainability: an exploratory study of the advantages and challenges. J Clean Prod 137:1573–1587CrossRefGoogle Scholar
  4. 4.
    Rejeski D, Zhao F, Huang Y (2018) Research needs and recommendations on environmental implications of additive manufacturing. Addit Manuf 19:21–28CrossRefGoogle Scholar
  5. 5.
    Kellens K, Mertens R, Paraskevas D, Dewulf W, Duflou JR (2017) Environmental impact of additive manufacturing processes: does AM contribute to a more sustainable way of part manufacturing? Procedia CIRP 61(Section 3):582–587CrossRefGoogle Scholar
  6. 6.
    Nagarajan HPN, Malshe HA, Haapala KR, Pan Y (2016) Environmental performance evaluation of a fast mask image projection stereolithography process through time and energy modeling. J Manuf Sci Eng 138(10):101004CrossRefGoogle Scholar
  7. 7.
    Lutter-Günther M, Gebbe C, Kamps T, Seidel C, Reinhart G (2018) Powder recycling in laser beam melting: strategies, consumption modeling and influence on resource efficiency. Prod Eng 12(3–4):377–389CrossRefGoogle Scholar
  8. 8.
    Linke B, Overcash M (2017) Reusable unit process life cycle inventory for manufacturing: grinding. Prod Eng 11(6):643–653CrossRefGoogle Scholar
  9. 9.
    Ren X, Shao H, Lin T, Zheng H (2016) 3D gel-printing—an additive manufacturing method for producing complex shape parts. Mater Des 101:80–87CrossRefGoogle Scholar
  10. 10.
    Xu H, Jing W, Li M, Li W (2016) A slicing model algorithm based on STL model for additive manufacturing processes. In: 2016 IEEE Advanced information management, communicates, electronic and automation control conference (IMCEC), Xi’an, pp 1607–1610.
  11. 11.
    SLA 3D Printing materials compared | 3D Hubs. Accessed: 21 Jan 2019
  12. 12.
    Yang Y, Li L (2018) Total volatile organic compound emission evaluation and control for stereolithography additive manufacturing process. J Clean Prod 170:1268–1278CrossRefGoogle Scholar
  13. 13.
    Mackay D, Matsugu RS (1973) Evaporation rates of liquid hydrocarbon spills on land and water. Can J Chem Eng 51(4):434–439CrossRefGoogle Scholar
  14. 14.
    Arnold FC, Engel AJ (2001) Evaporation of pure liquids from open surfaces. In: Linders JBHJ (ed) Modelling of environmental chemical exposure and risk. Springer, Dordrecht, 2001, pp 61–71CrossRefGoogle Scholar

Copyright information

© German Academic Society for Production Engineering (WGP) 2019

Authors and Affiliations

  • Timothy Simon
    • 1
  • Yiran Yang
    • 2
  • Wo Jae Lee
    • 1
  • Jing Zhao
    • 3
  • Lin Li
    • 3
  • Fu Zhao
    • 1
    Email author
  1. 1.Purdue UniversityWest LafayetteUSA
  2. 2.Department of Industrial, Manufacturing, & Systems EngineeringUniversity of Texas at ArlingtonArlingtonUSA
  3. 3.Department of Mechanical and Industrial EngineeringUniversity of Illinois at ChicagoChicagoUSA

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