Nesting and scheduling problems for additive manufacturing: A taxonomy and review
- Authors
- Oh, Yosep; Witherell, Paul; Lu, Yan; Sprock, Timothy
- Issue Date
- Aug-2020
- Publisher
- Elsevier BV
- Keywords
- 3D printing; Additive manufacturing; Nesting; Production planning; Scheduling
- Citation
- Additive Manufacturing, v.36, pp 1 - 12
- Pages
- 12
- Indexed
- SCIE
SCOPUS
- Journal Title
- Additive Manufacturing
- Volume
- 36
- Start Page
- 1
- End Page
- 12
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/113829
- DOI
- 10.1016/j.addma.2020.101492
- ISSN
- 2214-8604
2214-7810
- Abstract
- With the trends of Industry 4.0 spanning physical and virtual worlds, Additive Manufacturing (AM) has been the mainstream for realizing complex geometries designed in computers. Meanwhile, a considerable number of AM studies have focused on effectively building these elaborate designs. However, as the AM technologies have matured, production-driven studies have recently been spotlighted to achieve mass customization. This means that the research scope has been extended to incorporate production management concerns focused on efficiently producing high volumes of heterogeneous parts. Particularly, since AM allows batch processing of multiple parts within the same build volume, nesting methods have been studied to properly place objects in the limited space to improve yield. Since the middle of 2010, the nesting topic has been considered with a scheduling matter for assigning objects to AM machines to minimize production time and cost. The main contribution of this investigation is to show the current status of nesting and scheduling studies applied to AM. This reveals critical issues for future research directions. Since traditional manufacturing usually addresses nesting and scheduling problems separately, each problem is specified with its specialized taxonomies. This causes the existing taxonomies to be limited in comprehensively covering both nesting and scheduling topics. To provide a holistic view covering both topics, this paper proposes an alternative taxonomy based on three dimensions: Part, Build, and AM Machine. Considering combinations of the three dimensions, six classes are defined to identify and cluster problem characteristics and types. Moreover, eight supplementary criteria are added to further refine the organization of the research papers within those classes. In this survey, 53 technical papers are classified and critical issues are discussed. © 2020 Elsevier B.V.
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