Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2023/01/17
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Journal
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Indian UGC (journal)
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Refrences61
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Citations13
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Qihao Zhu Singapore University of Technology and Design Engineering Product Development Pillar, , 8 Somapah Road, Singapore 487372
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Xinyu Zhang Tsinghua University State Key Laboratory of Automotive Safety and Energy, , Beijing 100084 , China
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Jianxi Luo Singapore University of Technology and Design Data-Driven Innovation Lab, , 8 Somapah Road, Singapore 487372
Abstract
Cite
Zhu, Qihao, et al. “Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers”. Journal of Mechanical Design, vol. 145, no. 4, 2023, https://doi.org/10.1115/1.4056598.
Zhu, Q., Zhang, X., & Luo, J. (2023). Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design, 145(4). https://doi.org/10.1115/1.4056598
Zhu, Qihao, Xinyu Zhang, and Jianxi Luo. “Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers”. Journal of Mechanical Design 145, no. 4 (2023). https://doi.org/10.1115/1.4056598.
Zhu Q, Zhang X, Luo J. Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design. 2023;145(4).
Journal Categories
Technology
Engineering (General)
Civil engineering (General)
Technology
Mechanical engineering and machinery
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Semantic Networks for Engineering Design: State of the Art and Future Directions | Journal of Mechanical Design |
| 8 | 2022 |
Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams | Journal of Mechanical Design |
| 2 | 2022 |
Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design | Journal of Mechanical Design |
| 14 | 2022 |
Deep Generative Models in Engineering Design: A Review | Journal of Mechanical Design |
| 54 | 2022 |
Data-Driven Design-By-Analogy: State-of-the-Art and Future Directions | Journal of Mechanical Design |
| 27 | 2022 |
Refrences Analysis
The category
Technology: Engineering (General). Civil engineering (General) 11
is the most frequently represented among the references in this article. It primarily includes studies from Computers in Industry and Design Studies. The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| ChatGPT and finetuned BERT: A comparative study for developing intelligent design support systems | Intelligent Systems with Applications | 2024 | ||
The innovation paradox: concept space expansion with diminishing originality and the promise of creative artificial intelligence | Design Science |
| 2024 | |
| A knowledge graph-based bio-inspired design approach for knowledge retrieval and reasoning | Journal of Engineering Design |
| 2024 | |
Mapping Novice Designer Behavior to Design Fixation in the Early-Stage Design Process | Journal of Mechanical Design |
| 2024 | |
| Bio-inspired generative design for engineering products: A case study for flapping wing shape exploration | Advanced Engineering Informatics |
| 3 | 2023 |
Citations Analysis
The category
Technology: Engineering (General). Civil engineering (General) 8
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Toward Artificial Empathy for Human-Centered Design and was published in 2023. The most recent citation comes from a 2024 study titled The innovation paradox: concept space expansion with diminishing originality and the promise of creative artificial intelligence. This article reached its peak citation in 2023, with 9 citations. It has been cited in 8 different journals, 25% of which are open access. Among related journals, the Journal of Mechanical Design cited this research the most, with 3 citations. The chart below illustrates the annual citation trends for this article.