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

Abstract

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.

Development of risk factors taxonomy in ramp operations for corporate sustainability

“…In this section, a literature review of existing taxonomies in various fields and especially the aviation sector is presented. Araújo et al (2019) reviewed existing general cutting and packing taxonomies and provides a new specification, which is more appropriate for classifying the problems encountered in additive manufacturing. Muhammad et al (2021) stated that recent progress, new taxonomies, challenges and opportunities for future research opportunities on the application of deep learning in smart cities have been unveiled.…”

PackIt: A Virtual Environment for Geometric Planning

“…Packing tasks in 3D printing are also related to ours, except cases when they have additional constraints like objects should have some distance between them (Araújo et al, 2019). Works in 3D printing have used genetic algorithms with heuristics (BLBF, Largest-First) similar to our baselines (Gogate & Pande, 2008;Canellidis et al, 2010).…”

“…Works in 3D printing have used genetic algorithms with heuristics (BLBF, Largest-First) similar to our baselines (Gogate & Pande, 2008;Canellidis et al, 2010). However, most of them evaluated on very few packing instances (Araújo et al, 2019). (Araújo et al, 2019) intro-duced A2018, an extensive set of packing problems for 3d printing.…”

“…However, most of them evaluated on very few packing instances (Araújo et al, 2019). (Araújo et al, 2019) intro-duced A2018, an extensive set of packing problems for 3d printing. Our works differs from them in three ways: First, in A2018, the focus is 3D printing, so it majorly has machinery parts like gears, screws; while in PackIt the focus is geometric planning in everyday tasks, so it has objects like cups, chairs.…”

“…To sum up, geometric planning in PackIt embodies the general ability to arrange shapes to satisfy constraints, which is required for complex high-level tasks such as grocery shopping, room decluttering and cleaning, and warehouse management. Another utility of PackIt is that it enables training learning-based methods for packing problems arising in ISO luggage packing (Tiwari et al, 2010) and 3D printing (Araújo et al, 2019). In these domains, just like PackIt, CAD models are directly fed to the system.…”