Part orientation optimisation for the additive layer manufacture of metal components

  • H. D. Morgan
  • J. A. Cherry
  • S. Jonnalagadda
  • D. Ewing
  • J. Sienz
Open Access


In a number of additive layer manufacturing processes, particularly for metals, additional support structure is required during the build process to act as scaffolding for overhanging features and to dissipate process heat. Such structures use valuable raw materials and their removal adds to post processing time. The objective of this study was to investigate whether a simple, single objective optimisation technique could be used to find the best orientation of the part, that would minimise the volume of support needed during the build. Not only reducing waste but potentially providing an effective and consistent approach for inexperienced users to orient components during manufacture. Software was developed using MatLab with an unconstrained optimisation algorithm implemented to search the different rotations of the part and identify the configuration with the least requirement for support volume. The algorithm was gradient based, and so multiple starting points were used to identify a global minimum. The efficacy of the algorithm is illustrated with three different case studies of increasing complexity. Additionally, the component of the final study was manufactured, which allowed a comparison between the algorithm’s results and the orientations chosen by experienced operatives. In two of the three case studies, the software was able to find good solutions for the support volume minimisation. For the manufactured part, only one of the results matched the orientation chosen by the operators, the other was orientated in a similar way but the difference added significantly to the required support volume. Future developments of the software would benefit from incorporating the expertise of the manufacturing operative.


Optimisation Additive manufacture Part orientation SLM 


  1. 1.
    Alexander P, Allen S, Dutta D (1998) Part orientation and build cost determination in layered manufacturing. Comput Aided Des 30(5):343–356CrossRefGoogle Scholar
  2. 2.
    Atzeni E, Salmi A (2015) Study on unsupported overhangs of alsi10mg parts processed by direct metal laser sintering (dmls)Google Scholar
  3. 3.
    Brackett D, Ashcroft I, Hague R (2011) Topology optimization for additive manufacturing. In: Proceedings of the 22nd Annual International Solid Freeform Fabrication SymposiumGoogle Scholar
  4. 4.
    Canellidis V, Giannatsis J, Dedoussis V (2009) Genetic-algorithm-based multi-objective optimization of the build orientation in stereolithography. Int J Adv Manuf Technol 45(7-8):714–730CrossRefGoogle Scholar
  5. 5.
    Chan K, Koike M, Mason R, Okabe T (2013) Fatigue life of titanium alloys fabricated by additive layer manufacturing techniques for dental implants. Metall Mater Trans A 44(2):1010–1022CrossRefGoogle Scholar
  6. 6.
    Greitemeier D, Dalle Donne C, Syassen F, Eufinger J, Melz T (2015) Effect of surface roughness on fatigue performance of additive manufactured ti6al4v. Mater Sci Technol 0(0)Google Scholar
  7. 7.
    Kruth J, Mercelis P, Van Vaerenbergh J, Craeghs T (2007) Feedback control of selective laser melting. In: 3rd International Conference on Advanced Research in Virtual and Rapid PrototypingGoogle Scholar
  8. 8.
    Leary M, Merli L, Torti F, Mazur M, Brandt M (2014) Optimal topology for additive manufacture: a method for enabling additive manufacture of support-free optimal structures. Mater Des 63(0):678–690CrossRefGoogle Scholar
  9. 9.
    Leuders S, Thne M, Riemer A, Niendorf T, Trster T, Richard H, Maier H (2013) On the mechanical behaviour of titanium alloy ti-al6-v4 manufactured by selective laser melting: fatigue resistance and crack growth performance. Int J Fatigue 48:300–307CrossRefGoogle Scholar
  10. 10.
    Masood S, Rattanawong W, Iovenitti P (2003) A generic algorithm for a best part orientation system for complex parts in rapid prototyping. J Mater Process Technol 139(13):110–116CrossRefGoogle Scholar
  11. 11.
    Neeraj Panhalkar RP, Anandr S Increasing part accuracy in additive manufacturing processes using a k-d tree based clustered adaptive layeringGoogle Scholar
  12. 12.
    Padhye N, Deb K (2011) Multiobjective optimisation and multi?criteria decision making in sls using evolutionary approaches. Rapid Prototyp J 17(6):458–478CrossRefGoogle Scholar
  13. 13.
    Pandey P, Thrimurthulu K, Reddy N (2004) Optimal part deposition orientation in FDM by using a multicriteria genetic algorithm. Int J Prod Res 42(19):4069–4089CrossRefzbMATHGoogle Scholar
  14. 14.
    Phatak AM, Pande S (2012) Optimum part orientation in rapid prototyping using genetic algorithm. J Manuf Syst 31(4):395–402. Selected Papers of 40th North American Manufacturing Research ConferenceCrossRefGoogle Scholar
  15. 15.
    Singhal SK, Jain PK, Pandey PM (2008) Adaptive slicing for sls prototyping. Computer-Aided Design and Applications 5(1-4):412–423CrossRefGoogle Scholar
  16. 16.
    Strano G, Hao L, Everson R, Evans K (2013) A new approach to the design and optimisation of support structures in additive manufacturing. Int J Adv Manuf Technol 66(9-12):1247– 1254CrossRefGoogle Scholar
  17. 17.
    Strano G, Hao L, Everson RM, Evans KE (2013) Surface roughness analysis, modelling and prediction in selective laser melting. J Mater Process Technol 213(4):589–597CrossRefGoogle Scholar
  18. 18.
    Sunday D (2012) Intersections of rays and triangles (3d).
  19. 19.
    Vandenbroucke B, Kruth J (2007) Selective laser melting of biocompatible metals for rapid manufacturing of medical parts. Rapid Prototyp J 13(4):196–203CrossRefGoogle Scholar
  20. 20.
    Verma A, Tyagi S, Yang K (2014) Modeling and optimization of direct metal laser sintering process. Int J Adv Manuf Technol:1–14Google Scholar
  21. 21.
    Wang D, Yang Y, Yi Z, Su X (2013) Research on the fabricating quality optimization of the overhanging surface in slm process. Int J Adv Manuf Technol 65(9-12):1471–1484CrossRefGoogle Scholar
  22. 22.
    Wycisk E, Emmelmann C, Siddique S, Walther F (2013) High cycle fatigue (hcf) performance of ti-6al-4v alloy processed by selective laser melting. Advanced Materials Research 816-817:134–139CrossRefGoogle Scholar

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© The Author(s) 2016

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.ASTUTE, College of EngineeringSwansea UniversitySwanseaUK
  2. 2.Renishaw plcStoneUK

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