The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Laser surface texturing and related techniques for enhancing tribological performance of engineering materials: A review
Abstract
Abstract Surface texturing is one of the most efficient surface modification approaches for improving the tribological performance of engineering materials. Several surface texturing techniques have been developed in recent years. Among these methods, laser surface texturing (LST) has gained considerable interests due to its high flexibility, superior texturing accuracy, and good controllability. This paper reviews the recent advances and developments of LST for enhanced tribological properties of engineering materials. The process designs of LST by means of direct laser ablation, laser interference, and laser shock processing are discussed. The effects of laser parameters on the texture features are highlighted. The enhanced tribological properties of LST-processed materials and their applications on various fields are reviewed. The current challenges and future directions of LST techniques are discussed.
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Cites
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Etsion, I. (2005) · Journal of Tribology · doi:10.1115/1.1828070 · Cited by 1,409
Laser shock processing and its effects on microstructure and properties of metal alloys: a review
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Cited by
A review on laser cladding of high-entropy alloys, their recent trends and potential applications
Arif, Z. U., Khalid, M. Y., ur Rehman, E., Ullah, S., Atif, M., & Tariq, A. (2021) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2021.06.041 · Cited by 334
Metadata
- Authors
- Mao, B., Siddaiah, A., Liao, Y., & Menezes, P. L.
- Venue
- Journal of Manufacturing Processes
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 53
- Pages
- 153-173
- ISSN
- 1526-6125 (ISSN-L)
- OpenAlex
- W3007901884
- Cited by
- 489
- References
- 51
- FWCI
- 24.98
- Type
- journal article
- Language
- en
- Open access
- closed
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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).
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