Production planning in 3D printing factories

  1. De Antón, J. 1
  2. Senovilla, J. 1
  3. González, J.M. 1
  4. Acebes, F. 1
  1. 1 Universidad de Valladolid
    info

    Universidad de Valladolid

    Valladolid, España

    ROR https://ror.org/01fvbaw18

Revista:
International Journal of Production Management and Engineering (IJPME)

ISSN: 2340-4876,  2340-5317

Año de publicación: 2020

Volumen: 8

Número: 2

Páginas: 75-86

Tipo: Artículo

DOI: 10.4995/IJPME.2020.12944 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: International Journal of Production Management and Engineering (IJPME)

Objetivos de desarrollo sostenible

Resumen

Production planning in 3D printing factories brings new challenges among which the scheduling of parts to be produced stands out. A main issue is to increase the efficiency of the plant and 3D printers productivity. Planning, scheduling, and nesting in 3D printing are recurrent problems in the search for new techniques to promote the development of this technology. In this work, we address the problem for the suppliers that have to schedule their daily production. This problem is part of the LONJA3D model, a managed 3D printing market where the parts ordered by the customers are reorganized into new batches so that suppliers can optimize their production capacity. In this paper, we propose a method derived from the design of combinatorial auctions to solve the nesting problem in 3D printing. First, we propose the use of a heuristic to create potential manufacturing batches. Then, we compute the expected return for each batch. The selected batch should generate the highest income. Several experiments have been tested to validate the process. This method is a first approach to the planning problem in 3D printing and further research is proposed to improve the procedure.

Referencias bibliográficas

  • Canellidis, V., Giannatsis, J., & Dedoussis, V. (2013). Efficient parts nesting schemes for improving stereolithography utilization. CAD Computer Aided Design, 45(5), 875–886. https://doi.org/10.1016/j.cad.2012.12.002
  • Chergui, A., Hadj-Hamou, K., & Vignat, F. (2018). Production scheduling and nesting in additive manufacturing. Computers and Industrial Engineering, 126(May), 292–301. https://doi.org/10.1016/j.cie.2018.09.048
  • Cui, Y. (2007). An exact algorithm for generating homogenous T-shape cutting patterns. Computers & Operations Research, 34(4), 1107–1120. https://doi.org/https://doi.org/10.1016/j.cor.2005.05.025
  • Dvorak, F., Micali, M., & Mathieu, M. (2018). Planning and scheduling in additive manufacturing. Inteligencia Artificial, 21(62), 40–52. https://doi.org/10.4114/intartif.vol21iss62pp40-52
  • Gogate, A. S., & Pande, S. S. (2008). Intelligent layout planning for rapid prototyping. International Journal of Production Research, 46(20), 5607–5631. https://doi.org/10.1080/00207540701277002
  • Gupta, M. C., & Boyd, L. H. (2008). Theory of constraints: A theory for operations management. International Journal of Operations and Production Management, 28(10), 991–1012. https://doi.org/10.1108/01443570810903122
  • Jakobs, S. (1996). On genetic algorithms for the packing of polygons. European Journal of Operational Research, 88(1), 165–181. https://doi.org/10.1016/0377-2217(94)00166-9
  • Kucukkoc, I. (2019). MILP models to minimise makespan in additive manufacturing machine scheduling problems. Computers and Operations Research, 105, 58–67. https://doi.org/10.1016/j.cor.2019.01.006
  • Kucukkoc, I., Li, Q., & Zhang, D. Z. (2016). Increasing the utilisation of additive manufacturing and 3D printing machines considering order delivery times. In 19th International Working Seminar on Production Economics (pp. 195–201). Innsbruck, Austria.
  • Li, Q., Kucukkoc, I., & Zhang, D. Z. (2017). Production planning in additive manufacturing and 3D printing. Computers and Operations Research, 83, 1339–1351. https://doi.org/10.1016/j.cor.2017.01.013
  • López-Paredes, A., Pajares, J., Martín, N., del Olmo, R., & Castillo, S. (2018). Application of combinatorial auctions to create a 3Dprinting market. Advancing in Engineering Network, Castro and Gimenez Eds. Lecture Notes in Management and Industrial Engineering (In Press), 12–13.
  • Mehrpouya, M., Dehghanghadikolaei, A., Fotovvati, B., Vosooghnia, A., Emamian, S. S., & Gisario, A. (2019). The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review. Applied Sciences, 9(18), 3865. https://doi.org/10.3390/app9183865
  • Piili, H., Happonen, A., Väistö, T., Venkataramanan, V., Partanen, J., & Salminen, A. (2015). Cost Estimation of Laser Additive Manufacturing of Stainless Steel. Physics Procedia, 78(August), 388–396. https://doi.org/10.1016/j.phpro.2015.11.053
  • Shaffer, S., Yang, K., Vargas, J., Di Prima, M. A., & Voit, W. (2014). On reducing anisotropy in 3D printed polymers via ionizing radiation. Polymer, 55(23), 5969–5979. https://doi.org/10.1016/j.polymer.2014.07.054
  • Singhal, S. K., Pandey, A. P., Pandey, P. M., & Nagpal, A. K. (2005). Optimum Part Deposition Orientation in Stereolithography. Computer-Aided Design and Applications, 2(1–4), 319–328. https://doi.org/10.1080/16864360.2005.10738380
  • Sung‐Hoon, A. (2002). Anisotropic material properties of fused deposition modeling ABS. Rapid Prototyping Journal, 8(4), 248–257. https://doi.org/10.1108/13552540210441166
  • Thomas, D. S., & Gilbert, S. W. (2015). Costs and cost effectiveness of additive manufacturing: A literature review and discussion. Additive Manufacturing: Costs, Cost Effectiveness and Industry Economics, 1–96. https://doi.org/10.6028/NIST.SP.1176
  • Toro, E., Garces, A., & Ruiz, H. (2008). Two dimensional packing problem using a hybrid constructive algorithm of variable neighborhood search and simulated annealing. Revista Facultad de Ingeniería Universidad de Antioquia, 119–131.
  • Toro, E., & Granada-Echeverri, M. (2007). Problema de empaquetamiento rectangular bidimensional tipo guillotina resuelto por algoritmos genéticos. Scientia Et Technica.
  • Wang, Y., Zheng, P., Xu, X., Yang, H., & Zou, J. (2019). Production planning for cloud-based additive manufacturing—A computer vision-based approach. Robotics and Computer-Integrated Manufacturing, 58(March), 145–157. https://doi.org/10.1016/j.rcim.2019.03.003
  • Wodziak, J. R., Fadel, G. M., & Kirschman, C. (1994). A Genetic Algorithm for Optimizing Multiple Part Placement to Reduce Build Time. Proceedings of the Fifth International Conference on Rapid Prototyping., (May), 201,210.
  • Zhang, Y., Gupta, R. K., & Bernard, A. (2016). Two-dimensional placement optimization for multi-parts production in additive manufacturing. Robotics and Computer-Integrated Manufacturing, 38, 102–117. https://doi.org/10.1016/j.rcim.2015.11.003
  • Zhao, Z., Zhang, L., & Cui, J. (2018). A 3D printing task packing algorithm based on rectangle packing in cloud manufacturing. Lecture Notes in Electrical Engineering, 460, 21–31. https://doi.org/10.1007/978-981-10-6499-9_3
  • Zhou, L., Zhang, L., Laili, Y., Zhao, C., & Xiao, Y. (2018). Multi-task scheduling of distributed 3D printing services in cloud manufacturing. International Journal of Advanced Manufacturing Technology, 96(9–12), 3003–3017. https://doi.org/10.1007/s00170-017-1543-z
  • Zhou, L., Zhang, L., & Xu, Y. (2016). Research on the relationships of customized service attributes in cloud manufacturing. ASME 2016 11th International Manufacturing Science and Engineering Conference, MSEC 2016, 2, 1–8. https://doi.org/10.1115/MSEC2016-8530
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