Hong-Kyun Noh, Jae Hyuk Lim, Seungchul Lee, Taejoo Kim, Deog-Kwan Kim
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引用次数: 0
Abstract
This study proposes an image composition technique based on convolutional neural networks (CNNs) to construct a surrogate model for predicting fan plots of three-dimensional (3D) composite blades, which represent natural frequency lists at different rotational speeds. The proposed method composes critical 2D cross-section images to improve the accuracy of the model. Numerical examples with various compositions of cross-section images are presented to demonstrate the efficacy of the CNN model. Additionally, gradient-weighted class activation mapping analysis is used to reveal the relationship between the internal structure of the blade and the fan plots. The study shows that using multiple images in the image composition technique improves the accuracy of the model compared to using single or fewer images. Overall, the proposed method provides a promising approach for predicting fan plots of 3D composite blades using CNN models.
期刊介绍:
Journal of Computational Design and Engineering is an international journal that aims to provide academia and industry with a venue for rapid publication of research papers reporting innovative computational methods and applications to achieve a major breakthrough, practical improvements, and bold new research directions within a wide range of design and engineering:
• Theory and its progress in computational advancement for design and engineering
• Development of computational framework to support large scale design and engineering
• Interaction issues among human, designed artifacts, and systems
• Knowledge-intensive technologies for intelligent and sustainable systems
• Emerging technology and convergence of technology fields presented with convincing design examples
• Educational issues for academia, practitioners, and future generation
• Proposal on new research directions as well as survey and retrospectives on mature field.