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• W3132723597 endingPage "126401" @default.
• W3132723597 startingPage "126401" @default.
• W3132723597 abstract "The Circular Economy concept aims to ensure environmental sustainability through the recovery of durable products that have reached the end of their useable life. Recovery strategies such as remanufacturing enable durable parts and cores to be restored to their original functionality and performance, thereby minimising the consumption of virgin materials and energy required for the production of new parts and components. To date, the repair and restoration processes concerning those parts and cores that can be remanufactured involved conventional methods such as material overlay and welding. These conventional methods are highly dependent on skilled manual labour or specialised industrial robots. With the notable growth in the global remanufacturing industry, it is imperative to deploy highly-efficient and sustainable methods to automate repair and restoration. Recent trends in remanufacturing repair and restoration indicate an increasing interest in metal additive manufacturing technology. To enhance the additive manufacturing efficiency for automated repair and restoration, it is crucial to optimise the core design. This paper provides a comprehensive and comparative outline of remanufacturing repair and restoration, using both conventional and automated methods. This paper also presents and discusses comprehensive insight into the application of AI-based techniques for design optimisation specific to additive manufacturing repair. Component design optimisation is crucial due to its impact on process efficiency and the life cycle of components. The review indicates that, despite the increasing interest in using additive manufacturing for repair and restoration, reports on the application of AI for design optimisation specific to repair and restoration using additive manufacturing remain limited. Furthermore, there are no established guidelines concerning design for repair and restoration using additive manufacturing. The paper concludes with recommendations for further research and presents a future outlook on AI-based optimisation for component design to facilitate repair and restoration using additive manufacturing. Automation is expected to facilitate the removal of roadblocks specific to process inefficiency and human limitations during conventional repair and restoration in remanufacturing. • An overview of repair and restoration processes in remanufacturing by making comparison between conventional and automated process, focusing on the use of additive manufacturing technology. • A comprehensive review on the application of artificial intelligence based approaches for design optimisation to support repair and restoration using additive manufacturing. • Challenges in the application of additive manufacturing for repair and restoration and the potentials of AI based techniques in components design optimisation to support efficient repair and restoration using additive manufacturing." @default.
• W3132723597 created "2021-03-01" @default.
• W3132723597 creator A5001464548 @default.
• W3132723597 creator A5011707731 @default.
• W3132723597 creator A5047426143 @default.
• W3132723597 creator A5075611455 @default.
• W3132723597 date "2021-05-01" @default.
• W3132723597 modified "2023-09-29" @default.
• W3132723597 title "Component design optimisation based on artificial intelligence in support of additive manufacturing repair and restoration: Current status and future outlook for remanufacturing" @default.
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