Skip to main content

Design for Additive Manufacturing

  • Chapter
Additive Manufacturing Technologies

Abstract

Design for manufacture and assembly (DFM) has typically meant that designers should tailor their designs to eliminate manufacturing difficulties and minimize manufacturing, assembly, and logistics costs. However, the capabilities of additive manufacturing technologies provide an opportunity to rethink DFM to take advantage of the unique capabilities of these technologies. As mentioned in Chap. 16, several companies are now using AM technologies for production manufacturing. For example, Siemens, Phonak, Widex, and the other hearing aid manufacturers use selective laser sintering and stereolithography machines to produce hearing aid shells; Align Technology uses stereolithography to fabricate molds for producing clear dental braces (“aligners”); and Boeing and its suppliers use polymer powder bed fusion (PBF) to produce ducts and similar parts for F-17 fighter jets. For hearing aids and dental aligners, AM machines enable manufacturing of tens to hundreds of thousands of parts, where each part is uniquely customized based upon person-specific geometric data. In the case of aircraft components, AM technology enables low-volume manufacturing, easy integration of design changes and, at least as importantly, piece part reductions to greatly simplify product assembly.

The unique capabilities of AM include: shape complexity, in that it is possible to build virtually any shape; hierarchical complexity, in that hierarchical multiscale structures can be designed and fabricated from the microstructure through geometric mesostructure (sizes in the millimeter range) to the part-scale macrostructure; material complexity, in that material can be processed one point, or one layer, at a time; and functional complexity, in that fully functional assemblies and mechanisms can be fabricated directly using AM processes. These unique capabilities enable new opportunities for customization, very significant improvements in product performance, multifunctionality, and lower overall manufacturing costs. These capabilities will be expanded upon in Sects. 17.3 and 17.4.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Design for manufacturing is typically abbreviated DFM, whereas design for manufacture and assembly is typically abbreviated as DFMA. To avoid confusion with the abbreviation for design for additive manufacturing (DFAM), we have utilized the shorter abbreviation DFM to encompass both design for manufacture and design for assembly.

References

  1. Susman GI (1992) Integrating design and manufacturing for competitive advantage. Oxford University Press, New York

    Google Scholar 

  2. Bralla JG (ed) (1986) Handbook of product design for manufacturing. McGraw-Hill, New York

    Google Scholar 

  3. Boothroyd G, Dewhurst P, Knight W (1994) Product design for manufacture and assembly. Marcel Dekker, New York

    Google Scholar 

  4. Shah J, Wright PK (2000) Developing theoretical foundations of DFM. ASME design for manufacturing conference, Baltimore, MD

    Google Scholar 

  5. Rosen DW, Chen Y, Sambu S, Allen JK, Mistree F (2003) The rapid tooling testbed: a distributed design-for-manufacturing system. Rapid Prototyp J 9(3):122–132

    Article  Google Scholar 

  6. 3D Systems, Inc. http://www.3dsystems.com

  7. Hague RJM (2006) Unlocking the design potential of rapid manufacturing. In: Hopkinson N, Hague RJM, Dickens PM (eds) Rapid manufacturing: an industrial revolution for the digital age, Chap. 2. Wiley, Chichester

    Google Scholar 

  8. Mavroidis C, DeLaurentis KJ, Won J, Alam M (2002) Fabrication of non-assembly mechanisms and robotic systems using rapid prototyping. ASME J Mech Des 123:516–524

    Article  Google Scholar 

  9. Kataria A, Rosen DW (2001) Building around inserts: methods for fabricating complex devices in stereolithography. Rapid Prototyp J 7(5):253–261

    Article  Google Scholar 

  10. Binnard M (1999) Design by composition for rapid prototyping, 1st edn. Kluwer Academic Publishing, Norwell

    Book  Google Scholar 

  11. Patil L, Dutta D, Bhatt AD, Lyons K, Jurrens K, Pratt MJ, Sriram RD (2000) Representation of heterogeneous objects in ISO 10303 (STEP). Proceedings of the ASME international mechanical engineering congress and exposition, Mechanical Engineering Division, Orlando, FL, November, pp 355–364

    Google Scholar 

  12. Boeing Corp. http://www.boeing.com

  13. Gibson LJ, Ashby MF (1997) Cellular solids: structure and properties. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  14. Ashby MF, Evans A, Fleck NA, Gibson LJ, Hutchinson JW, Wadley HNG (2000) Metal foams: a design guide. Butterworth-Heinemann, Woburn

    Google Scholar 

  15. Deshpande VS, Fleck NA, Ashby MF (2001) Effective properties of the octet-truss lattice material. J Mech Phys Solids 49(8):1747–1769

    Article  MATH  Google Scholar 

  16. Wang J, Evans AG, Dharmasena K, Wadley HNG (2003) On the performance of truss panels with Kagome cores. Int J Solids Struct 40:6981–6988

    Article  Google Scholar 

  17. Wang A-J, McDowell DL (2003) Optimization of a metal honeycomb sandwich beam-bar subjected to torsion and bending. Int J Solids Struct 40(9):2085–2099

    Article  MATH  Google Scholar 

  18. Nguyen J, Park S-I, Rosen DW (2013) Heuristic optimization method for cellular structure design of light weight components. Int J Precis Eng Manuf 14(6):1071–1078

    Article  Google Scholar 

  19. Kytannen J (2006) Rapid manufacture for the retail industry. In: Hopkinson N, Hague RJM, Dickens PM (eds) Rapid manufacturing: an industrial revolution for the digital age, Chap. 18. Wiley, Chichester

    Google Scholar 

  20. Rosen DW (2007) Computer-aided design for additive manufacturing of cellular structures. Comput Aided Des Appl 4(5):585–594

    Google Scholar 

  21. Additive Manufacturing and 3D Printing Research Group, Nottingham University, UK. http://www.nottingham.ac.uk/research/groups/3dprg/index.aspx

  22. Beaman JJ, Atwood C, Bergman TL, Bourell D, Hollister S, Rosen D (2004) Assessment of European Research and Development in Additive/Subtractive Manufacturing, final report from WTEC panel. http://wtec.org/additive/report/welcome.htm

  23. Ensz M, Storti D, Ganter M (1998) Implicit methods for geometry creation. Int J Comput Geom Appl 8(5, 6):509–536

    Article  MathSciNet  MATH  Google Scholar 

  24. Shapiro V, Tsukanov I (1999) Meshfree simulation of deforming domains. Comput Aided Des 31(7):459–471

    Article  MATH  Google Scholar 

  25. Zeid I (2005) Mastering CAD/CAM. McGraw-Hill, New York

    Google Scholar 

  26. Rvachev VL, Sheiko TI, Shapiro V, Tsukanov I (2001) Transfinite interpolation over implicitly defined sets. Comput Aided Geom Des 18:195–220

    Article  MathSciNet  MATH  Google Scholar 

  27. Michell AGM (1904) The limits of economy of material in frame structures. Philos Mag 8:589–597

    Article  MATH  Google Scholar 

  28. Dewhurst P, Srithongchai S (2005) An Investigation of minimum-weight dual-material symmetrically loaded wheels and torsion arms. ASME J Appl Mech 72:196–202

    Article  MATH  Google Scholar 

  29. Baldick R (2006) Applied optimization. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  30. Qi X, Wang MY, Shi T (2013) A method for shape and topology optimization of truss-like structures. Struct Multidisc Optim 47:687–697

    Article  MathSciNet  MATH  Google Scholar 

  31. Patel J, Choi S-K (2012) Classification approach for reliability-based topology optimization using probabilistic neural networks. Struct Multidisc Optim 45(4):529–543

    Article  MathSciNet  MATH  Google Scholar 

  32. Bendsoe MP (1989) Optimal shape design as a material distribution problem. Struct Optim 1(193–202):1989

    Google Scholar 

  33. Sigmund O (2001) A 99 line topology optimization code written in Matlab. Struct Multidiscip Optim 21:120–127

    Article  Google Scholar 

  34. Rozvany GIN (2009) A critical review of established methods of structural topology optimization. Struct Multidisc Optim 37:217–237

    Article  MathSciNet  MATH  Google Scholar 

  35. Suresh K (2013) Efficient generation of large-scale pareto-optimal topologies. Struct Multidisc Optim 47:49–61

    Article  MathSciNet  MATH  Google Scholar 

  36. Wei P, Wang MW (2009) Piecewise constant level set method for structural topology optimization. Int J Numer Meth Engng 78:379–402

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this chapter

Cite this chapter

Gibson, I., Rosen, D., Stucker, B. (2015). Design for Additive Manufacturing. In: Additive Manufacturing Technologies. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2113-3_17

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-2113-3_17

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-2112-6

  • Online ISBN: 978-1-4939-2113-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics