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Search Results (3)

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Authors = Nick Birbilis ORCID = 0000-0002-7910-7470

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39 pages, 12218 KiB  
Review
The Additive Manufacturing of Glass: A Critical Review
by Mackinley Shaw, Dylan Vlahopoulos, Clara Usma-Mansfield, Jeffrey Sarmiento, Roland Snooks, Tony Koutsonikolas and Nick Birbilis
Appl. Sci. 2025, 15(6), 3414; https://doi.org/10.3390/app15063414 - 20 Mar 2025
Viewed by 2128
Abstract
This paper presents a critical review of additive manufacturing (AM) techniques applied to glass, elucidating the current state of the field and identifying key challenges and opportunities. The review identifies various AM methods, as applied to glass fabrication over broad length scales. Critical [...] Read more.
This paper presents a critical review of additive manufacturing (AM) techniques applied to glass, elucidating the current state of the field and identifying key challenges and opportunities. The review identifies various AM methods, as applied to glass fabrication over broad length scales. Critical aspects such as material considerations, process parameters, and postprocessing techniques are presented, offering insights into the evolving landscape of glass AM. A particular emphasis is placed on semi-solid glass processing. The paper assesses the achievements and limitations of existing methodologies, paving the way for future advancements. This review serves as a resource for both researchers and practitioners in the emerging field of the additive manufacturing of glass, including applications in the domain of architecture and art. Full article
(This article belongs to the Special Issue Smart Manufacturing and Materials Ⅱ)
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18 pages, 4895 KiB  
Article
Inverse Design of Aluminium Alloys Using Genetic Algorithm: A Class-Based Workflow
by Ninad Bhat, Amanda S. Barnard and Nick Birbilis
Metals 2024, 14(2), 239; https://doi.org/10.3390/met14020239 - 16 Feb 2024
Cited by 10 | Viewed by 2600
Abstract
The design of aluminium alloys often encounters a trade-off between strength and ductility, making it challenging to achieve desired properties. Adding to this challenge is the broad range of alloying elements, their varying concentrations, and the different processing conditions (features) available for alloy [...] Read more.
The design of aluminium alloys often encounters a trade-off between strength and ductility, making it challenging to achieve desired properties. Adding to this challenge is the broad range of alloying elements, their varying concentrations, and the different processing conditions (features) available for alloy production. Traditionally, the inverse design of alloys using machine learning involves combining a trained regression model for the prediction of properties with a multi-objective genetic algorithm to search for optimal features. This paper presents an enhancement in this approach by integrating data-driven classes to train class-specific regressors. These models are then used individually with genetic algorithms to search for alloys with high strength and elongation. The results demonstrate that this improved workflow can surpass traditional class-agnostic optimisation in predicting alloys with higher tensile strength and elongation. Full article
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24 pages, 2796 KiB  
Review
A Review of Corrosion under Insulation: A Critical Issue in the Oil and Gas Industry
by Qing Cao, Thunyaluk Pojtanabuntoeng, Marco Esmaily, Sebastian Thomas, Michael Brameld, Ayman Amer and Nick Birbilis
Metals 2022, 12(4), 561; https://doi.org/10.3390/met12040561 - 25 Mar 2022
Cited by 49 | Viewed by 17826
Abstract
Corrosion under insulation (CUI) is defined as any form of external corrosion that occurs on the underlying metal beneath insulated equipment, due to water ingress through the insulation layer. This type of corrosion is frequently observed in oil and gas production, where insulated [...] Read more.
Corrosion under insulation (CUI) is defined as any form of external corrosion that occurs on the underlying metal beneath insulated equipment, due to water ingress through the insulation layer. This type of corrosion is frequently observed in oil and gas production, where insulated piping is prevalent, and has historically remained a predominant materials integrity issue. The prediction and direct visualisation of CUI are challenging tasks because of the coverage of the insulation layer(s) and any external jacketing or cladding. Several factors, including the local/ambient environment, system design, and the piping installation process, can influence how CUI initiates and propagates. In this review, CUI background, CUI monitoring, and CUI mitigation strategies are discussed. Full article
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