Horseshoe Lattice Property-Structure Inverse Design Based on Deep Learning
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- Liu Guancen
- School of Materials and Chemistry, University of Shanghai for Science and Technology
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- Zheng Zhiwei
- School of Materials and Chemistry, University of Shanghai for Science and Technology
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- Zhao Rusheng
- Tokyo Metropolitan University
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- Yue Xuezheng
- School of Materials and Chemistry, University of Shanghai for Science and Technology
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抄録
<p>Lattice structures, characterized by their exceptional strength-to-weight ratios and energy absorption capabilities, have paved the way for pioneering designs in additive manufacturing (AM). To fully harness the potential of AM, robust inverse design methodologies are essential. In this study, a novel FEM-LSTM based lattice structure inverse design framework was proposed for horseshoe lattice structures characterized by Length (L), Radius (R), and Angle (A) to establish the structure-performance response. Using finite element analysis, a substantial dataset with distinct geometries and mechanical responses was meticulously furnished for training. Delving deeper into modeling, we developed an autoencoder framework anchored in long short-term memory (LSTM) networks, designed to adeptly decode the temporal intricacies of stress-strain attributes and seamlessly encode sequence characteristics. Compared to traditional GPR models and DNN models, the proposed model’s predictability increased by 9% and 7%, respectively, which is attributable to the exceptional capability of LSTM structure in handling time-series data. Our model, being versatile, can seamlessly integrate multiple stress-strain inputs, rendering precise geometric parameters that resonate with tailored design specifications. Such a streamlined approach effectively supplants the conventionally tedious iterative forward design and exhaustive simulation phases. In summation, the model emerges as a swift conduit for bespoke inverse design pertaining to lattice structures. And the paradigm of discerning time-series correlations through LSTM autoencoders holds vast potential across diverse time-dependent properties inherent to materials science.</p>
収録刊行物
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- MATERIALS TRANSACTIONS
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MATERIALS TRANSACTIONS 65 (3), 308-317, 2024-03-01
公益社団法人 日本金属学会
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詳細情報 詳細情報について
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- CRID
- 1390580793806666240
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- NII書誌ID
- AA1151294X
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- ISSN
- 13475320
- 13459678
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- NDL書誌ID
- 033355908
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
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- 抄録ライセンスフラグ
- 使用不可