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Cited by 523

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

A comparison of traditional manufacturing vs additive manufacturing, the best method for the job

Open access
Pereira, Tanisha, Kennedy, John V, Potgieter, Johan·2019·Procedia Manufacturing

Abstract

Manufacturing industries and investors are always seeking to improve techniques to lower cost, energy and expand their capability. Additive manufacturing, started in the 1960s, has since had a rapid and continuous growth, bringing to light novel techniques to expand manufacturing capability and reinvent the wheel. At this stage, research and industry interest lie in determining where AM can replace or create new manufacturing systems. Traditional manufacturing refers to subtractive and long-established manufacturing methods, quality assured and implemented in the commercial space. This paper reviews the capability of AM and its current development to compete or add to established traditional manufacturing regions. Literature reveals the capability of AM to fit into established manufacturing regions for low and high production volume products. The paper comparison focuses on the similarities, differences, advantages and disadvantages found in AM vs SM studying the economic and quality management status of the industry today.

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Crossref · 10.1016/j.promfg.2019.02.003

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Metadata

Authors
Pereira, T., Kennedy, J. V., & Potgieter, J.
Venue
Procedia Manufacturing
Publisher
Elsevier BV
Published
2019
Volume
30
Pages
11-18
ISSN
2351-9789 (ISSN-L)
OpenAlex
W2942343496
Cited by
523
References
31
FWCI
15.22
Type
journal article
Language
en
Open access
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CoChat. (2026). Citation check: A comparison of traditional manufacturing vs additive manufacturing, the best method for the job. https://app.cochat.ai/papers/10.1016/j.promfg.2019.02.003
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Pereira, T., Kennedy, J. V., & Potgieter, J. (2019). A comparison of traditional manufacturing vs additive manufacturing, the best method for the job. Procedia Manufacturing. https://doi.org/10.1016/j.promfg.2019.02.003

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.