Szederkenyi, Bence and Kovacs, Norbert Krisztian and Czigany, Tibor (2024) A comprehensive review of fiber-reinforced topology optimization for advanced polymer composites produced by automated manufacturing. Advanced Industrial and Engineering Polymer Research. ISSN 25425048 (In Press)
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SZB_KNK_CT_A comprehensive review of fiber-reinforced topology optimization.pdf - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (4MB) | Preview |
Abstract
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.
Item Type: | Article |
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Uncontrolled Keywords: | Topology optimization, Reinforcement optimization, Concurrent optimization, Automated manufacturing, Finite element analysis, Artificial intelligence |
Subjects: | T Technology / alkalmazott, műszaki tudományok > TJ Mechanical engineering and machinery / gépészmérnöki tudományok |
Depositing User: | Dr. Tamás Tábi |
Date Deposited: | 19 Sep 2024 07:22 |
Last Modified: | 19 Sep 2024 07:22 |
URI: | https://real.mtak.hu/id/eprint/205264 |
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