Evolutionary Computing and Genetic Algorithms: Paradigm Applications in 3D Printing Process Optimization

  • Vassilios Canellidis
  • John GiannatsisEmail author
  • Vassilis Dedoussis
Part of the Studies in Computational Intelligence book series (SCI, volume 627)


3D printing is a relatively new group of manufacturing technologies, methods and processes that produce parts through material addition. 3D printing technologies are mainly employed for the fabrication of prototypes and physical models during product design and development; however as they continuously improve in terms of accuracy and range of raw materials they are increasingly employed in the actual manufacturing process. This puts a new emphasis on the study of some of the process planning problems and issues that are related with the cost efficient use of 3D printing systems and the quality of their products. Among the most crucial process planning problems are: (i) the selection of fabrication orientation and parameters which is by definition a multi-criteria optimization problem in which the operator seeks to achieve the optimum trade-off between cost and quality, under given fabrication constraints and requirements, and (ii) the batch selection/planning or “packing” problem, at which the selection and placement of various different parts inside the machine workspace is considered. As such, the primary goal of the chapter is to present the effective utilization of Genetic Algorithms, which are a particular class of Evolutionary Computing, as a means of optimizing the 3D printing process planning.


3D printing Stereolithography Build orientation 2D packing Genetic algorithm Multi-objective optimization 


  1. Ahn, D., Kim, H., Lee, S.: Fabrication direction optimization to minimize post-machining in layered manufacturing. Int. J. Mach. Tool. Manu. 47, 593–606 (2007)CrossRefMathSciNetGoogle Scholar
  2. Alba, E., Dorronsoro, B.: Cellular Genetic Algorithms. Springer, New York (2008)zbMATHGoogle Scholar
  3. Alexander, P., Allen, S., Dutta, D.: Part orientation and build cost determination in layered manufacturing. Comput. Aided Design 30, 343–356 (1998)CrossRefGoogle Scholar
  4. Bennell, J., Oliveira, J.F.: The geometry of nesting problems: a tutorial. Eur. J. Oper. Res. 184, 397–415 (2006)CrossRefMathSciNetGoogle Scholar
  5. Branke, J., Deb, K., Miettinen, K., Słowiński, R.: Multiobjective Optimization: Interactive and Evolutionary Approaches. Springer, Berlin (2008)CrossRefGoogle Scholar
  6. Byun, H.S., Lee, K.H.: Determination of optimal build direction in rapid prototyping with variable slicing. Int. J. Adv. Manuf. Tech. 28, 307–313 (2006a)CrossRefGoogle Scholar
  7. Byun, H.S., Lee, K.H.: Determination of the optimal build direction for different rapid prototyping processes using multi-criterion decision making. Robot. CIM-Int. Manuf. 22(1), 69–80 (2006b)CrossRefGoogle Scholar
  8. Canellidis, V., Giannatsis, J., Dedoussis, V.: Genetic algorithm based multi-objective optimization of the build orientation in stereolithography. Int. J. Adv. Manuf. Tech. 4(7-8), 714–730 (2009)CrossRefGoogle Scholar
  9. Canellidis, V., Giannatsis, J., Dedoussis, V.: Efficient parts nesting schemes for improving stereolithography utilization. Comput. Aided Design 45(5), 875–886 (2013)CrossRefGoogle Scholar
  10. Chernov, N., Stoyan, Yu., Romanova, T.: Mathematical model and efficient algorithms for object packing problem. Comput. Geom. 43(5), 535–553 (2010)CrossRefMathSciNetzbMATHGoogle Scholar
  11. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE T. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  12. Douglas, D., Peucker, T.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Can Cartographer 10(2), 112–122 (1973)CrossRefGoogle Scholar
  13. Drẻo, J., Pẻtrowski, A., Siarry, P., Taillard, E.: Metaheuristics for Hard Optimization. Springer, Berlin (2006)Google Scholar
  14. Giannatsis, J., Dedoussis, V.: A study of the build-time estimation problem for stereolithography systems. Robot. CIM-Int. Manuf. 17(4), 295–304 (2001)CrossRefGoogle Scholar
  15. Giannatsis, J., Dedoussis, V.: Decision support tool for selecting fabrication parameters in stereolithography. Int. J. Adv. Manuf. Tech. 33, 706–718 (2007)CrossRefGoogle Scholar
  16. Gibson, I., Rosen, D.W., Stucker, B.: Additive Manufacturing Technologies. Springer, Berlin (2010)CrossRefGoogle Scholar
  17. Gogate, S., Pande, S.S.: Intelligent layout planning for rapid prototyping. Int. J. Prod. Res. 46(20), 5607–5631 (2008)CrossRefzbMATHGoogle Scholar
  18. Haupt, R.L., Haupt, S.E.: Practical genetic algorithms. Wiley, New York (2004)Google Scholar
  19. Holland, J.H.: Adaptation in natural and artificial systems. MIT Press, Cambridge (1992)Google Scholar
  20. Hopper, E.: Two dimensional packing utilising evolutionary algorithms and other meta-heuristic methods. Ph.D. Thesis, School of Engineering, University of Wales (2000)Google Scholar
  21. Hsu, J.: Why 3-D Printing Matters for Made in USA. Scientific American. (2012)
  22. Hur, J., Lee, K.: The development of a CAD environment to determine the preferred build-up direction for layered manufacturing. Int. J. Adv. Manuf. Tech. 14(4), 247–254 (1998)CrossRefGoogle Scholar
  23. Hur, S.M., Choi, K.H., Lee, S.H., Chang, P.K.: Determination of fabricating orientation and packing in SLS process. J. Mater. Process Tech. 112(2-3), 236–243 (2001a)CrossRefGoogle Scholar
  24. Hur, S.M., Choi, K.H., Lee, S.H., Chang, P.K.: Determination of fabricating orientation and packing in SLS process. J. Mater. Process. Tech. 112, 236–243 (2001b)CrossRefGoogle Scholar
  25. Ikonen, I., Biles, W., Kumar, A., Ragade, R.K., Wissel, J.C.: A genetic algorithm for packing three-dimensional non-convex objects having cavities and holes, In: Proceedings of 7th International Conference on Genetic Algorithms, Michigan, pp. 591–598 (1997)Google Scholar
  26. Jakobs, S.: On genetic algorithms for the packing of polygons. Eur. J. Oper. Res. 88, 165–181 (1996)CrossRefzbMATHGoogle Scholar
  27. Kim, H.C., Lee, S.H.: Reduction of post-processing for stereolithography systems by fabrication-direction optimization. Comput. Aided Design 37(7), 711–725 (2005)CrossRefGoogle Scholar
  28. Lan, P.-T., Chou, S.-Y., Chen, L.-L., Gemmill, D.: Determining fabrication orientations for rapid prototyping with stereolithography apparatus. Comput. Aided Design 29, 53–62 (1997)CrossRefGoogle Scholar
  29. Lewis, J.E., Ragade, R.K., Kumar, A., Biles, W.E.: A distributed chromosome genetic algorithm for bin-packing. Robot. CIM-Int. Manuf. 21(4-5), 486–495 (2005)CrossRefGoogle Scholar
  30. Majhi, J., Janardan, R., Smid, M., Gupta, P.: On some geometric optimization problems in layered manufacturing. Comput. Geom. 12(3-4), 219–239 (1999)CrossRefMathSciNetzbMATHGoogle Scholar
  31. Markillie, P.A: Third industrial revolution. The Economist, Spec. Special report: Manufacturing and innovation, Apr 21st (2012)Google Scholar
  32. Masood, S.H., Rattanawong, W.: A generic part orientation system based on volumetric error in rapid prototyping. Int. J. Adv. Manuf. Tech. 19(3), 209–216 (2002)Google Scholar
  33. Pandey, P.M., Thrimurthulu, K., Reddy, N.V.: Optimal part deposition orientation in FDM by using a multicriteria genetic algorithm. Int. J. Prod. Res. 42(19), 4069–4089 (2004)CrossRefzbMATHGoogle Scholar
  34. Pandey, P.M., Reddy, N.V., Dhande, S.G.: Part deposition orientation studies in layered manufacturing. J. Mater. Process. Tech. 185, 125–131 (2007)CrossRefGoogle Scholar
  35. Pham, D.T., Dimov, S.S., Gault, R.S.: Part orientation in Stereolithography. Int. J. Adv. Manuf. Tech. 15(9), 674–682 (1999)CrossRefGoogle Scholar
  36. Powley, T.: 3D printing reshapes factory floor. Financial Times. (2013)
  37. Reeves, C.R., Rowe, J.E.: Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory. Kluwer Academic Publishers, Dordrecht (2002)Google Scholar
  38. Sivanandam, S.N., Deepa, S.N.: Introduction to Genetic Algorithms. Springer, Berlin (2008)zbMATHGoogle Scholar
  39. Thrimurthulu, K., Pandey, P.M., Reddy, N.V.: Optimum part deposition orientation in fused deposition modeling. Int. J. Mach. Tool. Manu. 44, 585–594 (2004)CrossRefGoogle Scholar
  40. Whitwell, G.: Novel heuristic and metaheuristic approaches to cutting and packing. Ph.D. Thesis, School of Computer Science and Information Technology, University of Nottingham (2004)Google Scholar
  41. Wodziak, J.R., Fadel, G.M., Kirschman, C.: A genetic algorithm for optimizing multiple part placement to reduce build time. In: Proceedings of the 5th International Conference on Rapid Prototyping, Dayton, Ohio, pp. 201–210 (1994)Google Scholar
  42. Wohlers Associates: The Use of 3D Printing for Final Part Production Continues: Impressive 10-Year Growth Trend. Press release, November 18 (2013)Google Scholar
  43. Wohlers, T.: Will Additive Manufacturing Change Manufacturing? Time Compression Technologies, May/June issue (2011)Google Scholar
  44. Zhang, X., Zhou, B., Zeng, Y., Gu, P.: Model layout optimization for solid ground curing rapid prototyping processes. Robot. CIM-Int. Manuf. 18, 41–51 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Vassilios Canellidis
    • 1
  • John Giannatsis
    • 1
    Email author
  • Vassilis Dedoussis
    • 1
  1. 1.Laboratory of Advanced Manufacturing Technologies and Testing, Department of Industrial Management and TechnologyUniversity of PiraeusPiraeusGreece

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