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Cited by 431

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

Comparing environmental impacts of additive manufacturing vs traditional machining via life-cycle assessment

Faludi, Jeremy, Bayley, Cindy, Bhogal, Suraj, Iribarne, Myles·2015·Rapid Prototyping Journal

Abstract

Purpose: – The purpose of this study is to compare the environmental impacts of two additive manufacturing machines to a traditional computer numerical control (CNC) milling machine to determine which method is the most sustainable. Design/methodology/approach: – A life-cycle assessment (LCA) was performed, comparing a Haas VF0 CNC mill to two methods of additive manufacturing: a Dimension 1200BST FDM and an Objet Connex 350 “inkjet”/“polyjet”. The LCA’s functional unit was the manufacturing of two specific parts in acrylonitrile butadiene styrene (ABS) plastic or similar polymer, as required by the machines. The scope was cradle to grave, including embodied impacts, transportation, energy used during manufacturing, energy used while idling and in standby, material used in final parts, waste material generated, cutting fluid for CNC, and disposal. Several scenarios were considered, all scored using the ReCiPe Endpoint H and IMPACT 2002+ methodologies. Findings: – Results showed that the sustainability of additive manufacturing vs CNC machining depends primarily on the per cent utilization of each machine. Higher utilization both reduces idling energy use and amortizes the embodied impacts of each machine. For both three-dimensional (3D) printers, electricity use is always the dominant impact, but for CNC at maximum utilization, material waste became dominant, and cutting fluid was roughly on par with electricity use. At both high and low utilization, the fused deposition modeling (FDM) machine had the lowest ecological impacts per part. The inkjet machine sometimes performed better and sometimes worse than CNC, depending on idle time/energy and on process parameters. Research limitations/implications: – The study only compared additive manufacturing in plastic, and did not include other additive manufacturing technologies, such as selective laser sintering or stereolithography. It also does not include post-processing that might bring the surface finish of FDM parts up to the quality of inkjet or CNC parts. Practical implications: – Designers and engineers seeking to minimize the environmental impacts of their prototypes should share high-utilization machines, and are advised to use FDM machines over CNC mills or polyjet machines if they provide sufficient quality of surface finish. Originality/value: – This is the first paper quantitatively comparing the environmental impacts of additive manufacturing with traditional machining. It also provides a more comprehensive measurement of environmental impacts than most studies of either milling or additive manufacturing alone – it includes not merely CO 2 emissions or waste but also acidification, eutrophication, human toxicity, ecotoxicity and other impact categories. Designers, engineers and job shop managers may use the results to guide sourcing or purchasing decisions related to rapid prototyping.

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Metadata

Authors
Faludi, J., Bayley, C., Bhogal, S., & Iribarne, M.
Venue
Rapid Prototyping Journal
Publisher
Emerald
Published
2015-01-19
Volume
21
Issue
1
Pages
14-33
ISSN
1355-2546 (ISSN-L)
OpenAlex
W2035263745
Cited by
431
References
18
FWCI
24.24
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Comparing environmental impacts of additive manufacturing vs traditional machining via life-cycle assessment. https://app.cochat.ai/papers/10.1108/rpj-07-2013-0067
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Faludi, J., Bayley, C., Bhogal, S., & Iribarne, M. (2015). Comparing environmental impacts of additive manufacturing vs traditional machining via life-cycle assessment. Rapid Prototyping Journal. https://doi.org/10.1108/rpj-07-2013-0067

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.