Abstract
Additive manufacturing (or 3D printing) is considered to be the future of manufacturing. Flexibility, accuracy, rapidness, cost efficiency and lightness are among the advantages those additive manufacturing provides. Astronauts can now produce their own tools and parts in the space with no waiting for another launch. Planning of additive manufacturing machines is a recent hot topic. Efficient use of such resources plays an important role to reduce the costs of additively manufactured parts as well as disseminate this technology making its advantages more apparent. This research addresses the metal additive manufacturing machine scheduling problem with multiple unidentical selective laser melting machines. Machines may have different specifications (dimension, speed, and cost parameters) and parts may have different characteristics (width, length, height, volume, release date and due date). The objective is to obtain a schedule such that total tardiness is minimized and parts are allocated on platforms (or building trays) with no overlap. A genetic algorithm approach is proposed to solve the problem within reasonable times. Results of the computational tests show the promising performance of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kucukkoc, I.: MILP models to minimise makespan in additive manufacturing machine scheduling problems. Comput. Oper. Res. 105, 58–67 (2019)
Nematollahi, M., et al.: Additive manufacturing (AM), Chap. 12. In: Niinomi, M. (ed.) Metals for Biomedical Devices, 2nd edn, pp. 331–353. Woodhead Publishing (2019)
NASA. Additive Manufacturing-Pioneering Affordable Aerospace Manufacturing. George C. Marshall Space Flight Center (2016). https://www.nasa.gov/sites/default/files/atoms/files/additive_mfg.pdf
Kucukkoc, I., Li, Q., Zhang, D.Z.: Increasing the utilisation of additive manufacturing and 3D printing machines considering order delivery times. In: 19th International Working Seminar on Production Economics, Innsbruck, Austria, 22–26 February 2016 (2016)
Li, Q., Kucukkoc, I., Zhang, D.Z.: Production planning in additive manufacturing and 3D printing. Comput. Oper. Res. 83, 157–172 (2017)
Chergui, A., Hadj-Hamou, K., Vignat, F.: Production scheduling and nesting in additive manufacturing. Comput. Ind. Eng. 126, 292–301 (2018)
Kucukkoc, I., et al.: Scheduling of multiple additive manufacturing and 3D printing machines to minimise maximum lateness. In: 20th International Working Seminar on Production Economics, Innsbruck, Austria, 19–23 February 2018 (2018)
Fera, M., et al.: A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling. Int. J. Ind. Eng. Comput. 9(4), 423–438 (2018)
Dvorak, F., Micali, M., Mathieu, M.: Planning and scheduling in additive manufacturing. Inteligencia Artif. 21, 40–52 (2018)
Li, Q., et al.: A dynamic order acceptance and scheduling approach for additive manufacturing on-demand production. Int. J. Adv. Manuf. Technol. 105(9), 3711–3729 (2019)
GitHub: 2DPackingAlgorithm [Software] (2017). https://github.com/shubhampuranik/2DPackingAlgorithm
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Kucukkoc, I., Li, Z., Li, Q. (2021). 2D Nesting and Scheduling in Metal Additive Manufacturing. In: Rossit, D.A., Tohmé, F., Mejía Delgadillo, G. (eds) Production Research. ICPR-Americas 2020. Communications in Computer and Information Science, vol 1407. Springer, Cham. https://doi.org/10.1007/978-3-030-76307-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-76307-7_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-76306-0
Online ISBN: 978-3-030-76307-7
eBook Packages: Computer ScienceComputer Science (R0)