A comprehensive review of fiber-reinforced topology optimization for advanced polymer composites produced by automated manufacturing

被引:2
|
作者
Szederkenyi, Bence [1 ]
Kovacs, Norbert Krisztian [1 ,2 ]
Czigany, Tibor [1 ,3 ]
机构
[1] Budapest Univ Technol & Econ, Fac Mech Engn, Dept Polymer Engn, Muegyet Rkp 3, H-1111 Budapest, Hungary
[2] MTA BME Lendulet Lightweight Polymer Composites Re, Muegyet Rkp 3, H-1111 Budapest, Hungary
[3] HUN REN BME Res Grp Composite Sci & Technol, Muegyet Rkp 3, H-1111 Budapest, Hungary
关键词
Topology optimization; Reinforcement optimization; Concurrent optimization; Automated manufacturing; Finite element analysis; Artificial intelligence; GEOMETRY PROJECTION METHOD; DESIGN; ORIENTATION;
D O I
10.1016/j.aiepr.2024.05.002
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This review paper focuses on Fiber-Reinforced Topology Optimization (FRTO) methods for automated manufacturing techniques, addressing topology and morphology optimization. Accordingly, the review introduces the main TO techniques and the common reinforcement path design strategies using concurrent and sequential optimization approaches. Furthermore, this paper examines the potential transformation of the conventional role of TO algorithms in structural optimization by integrating Artificial Intelligence (AI) into the optimization process [1]. We collected and categorized the most relevant papers from the past decade in the field of FRTO; comparisons were made based on appropriate metrics, including algorithm types, effectiveness, and validation environment. We emphasize practical considerations such as manufacturing constraints and algorithmic efficiency, addressing real-world usability aspects [2]. The analysis underscores the necessity for universally applicable benchmark methods and standardization to facilitate direct comparisons among various methodologies [3]. The main conclusions of the paper highlight the emerging trends in research, the potential of fiber-reinforced polymer composites designed by FRTO, the challenges facing the field, and the efficiency improvements and synergy with AI, indicating an evolving role for TO in structural optimization. (c) 2024 Kingfa Scientific and Technological Co. Ltd. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:113 / 131
页数:19
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