Analysis of irregular three-dimensional packing problems in additive manufacturing: a new taxonomy and dataset

Araújo, Luiz J.P.; Özcan, Ender; Atkin, Jason A.D.; Baumers, Martin

Abstract

© 2018 Informa UK Limited, trading as Taylor & Francis Group. With most Additive Manufacturing (AM) technology variants, build processes take place inside an internal enclosed build container, referred to as a ‘build volume’. It has been demonstrated that the effectiveness with which this volume is filled with product geometries forms an important determinant of overall process efficiency in AM. For effective operations management, it is important to understand not only the problem faced, but also which methods have proved effective (or ineffective) for problems with these characteristics in the past. This research aims to facilitate this increased understanding. The build volume packing task can be formulated as a three-dimensional irregular packing (3DIP) problem, which is a combinatorial optimisation problem requiring the configuration of a set of arbitrary volumetric items. This paper reviews existing general cutting and packing taxonomies and provides a new specification which is more appropriate for classifying the problems encountered in AM. This comprises a clear-cut problem definition, a set of precise categorisation criteria for objectives and problem instances, and a simple notation. Furthermore, the paper establishes an improved terminology with terms that are familiar to, but not limited to, researchers and practitioners in the field of AM. Finally, this paper describes a new dataset to be used in the evaluation of existing and proposed computational solution methods for 3DIP problems encountered in AM and discusses the importance of this research for further underpinning work.

Citation

Araújo, L. J., Özcan, E., Atkin, J. A., & Baumers, M. (2019). Analysis of irregular three-dimensional packing problems in additive manufacturing: a new taxonomy and dataset. International Journal of Production Research, 57(18), 5920-5934. https://doi.org/10.1080/00207543.2018.1534016

• Journal Article Type: Review
• Acceptance Date: Sep 30, 2018
• Online Publication Date: Oct 16, 2018
• Publication Date: Jan 1, 2019
• Deposit Date: Dec 3, 2018
• Publicly Available Date: Oct 17, 2019
• Journal: International Journal of Production Research
• Print ISSN: 0020-7543
• Electronic ISSN: 1366-588X
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 57
• Issue: 18
• Pages: 5920-5934
• DOI: https://doi.org/10.1080/00207543.2018.1534016
• Keywords: Management Science and Operations Research; Strategy and Management; Industrial and Manufacturing Engineering
• Public URL: https://nottingham-repository.worktribe.com/output/1190642
• Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 16/10/2018, available online: http://www.tandfonline.com/10.1080/00207543.2018.1534016.
• Contract Date: Dec 3, 2018