The DOI matches its Crossref record; 109 of the 109 references it cites were found in a registry or an index.
Surface post-treatments for metal additive manufacturing: Progress, challenges, and opportunities
Open accessAbstract
Abstract Metal additive manufacturing is a rapidly expanding area owing to its capacity to fabricate parts of intricate geometries with customized features for a wide range of applications. However, these parts generally exhibit inadequate and poor surface quality in the as-built configuration. The surface imperfections and defects ranging from staircase effect due to the layer by layer nature of the deposition techniques, partially fused feedstock material, balling effects, spatters, or inadequate fusion lead to a notably irregular surface morphology. This high surface roughness can significantly deteriorate the performance of the additive manufactured parts imposing a substantial limit on their prospective applications; for instance, fatigue performance, wear and scratch resistance, dimensional accuracy, and aesthetical aspects can be highly affected by these surface defects. A great effort has been lately dedicated to developing post-treatments for improving the surface quality of additively manufactured metallic parts. In this paper, various treatments applied to as-built samples fabricated using different additive manufacturing technologies are introduced and discussed. The advances in this area are highlighted, and the results obtained from different categories of post-treatments are compared and reviewed. Challenges and opportunities to gain more control on the surface roughness of additively manufactured metallic parts through the application of these post-treatments are addressed.
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Cites
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Metadata
- Authors
- Maleki, E., Bagherifard, S., Bandini, M., & Guagliano, M.
- Venue
- Additive Manufacturing
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 37
- Pages
- 101619
- ISSN
- 2214-7810 (ISSN-L)
- 2214-8604
- OpenAlex
- W3091330969
- Cited by
- 528
- References
- 109
- FWCI
- 30.36
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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