A Multidisciplinary Design and Optimization (MDO) Agent Driven by Large Language Models Article Swipe
Guo, Bingkun
,
Li Wentian
,
Liu Xiaojian
,
Luo, Jiaqi
,
YU Zibin
,
Dong Dalong
,
Zhang Shuyou
,
Zhang Yiming
·
YOU?
·
· 2025
· Open Access
·
YOU?
·
· 2025
· Open Access
·
To accelerate mechanical design and enhance design quality and innovation, we present a Multidisciplinary Design and Optimization (MDO) Agent driven by Large Language Models (LLMs). The agent semi-automates the end-to-end workflow by orchestrating three core capabilities: (i) natural-language-driven parametric modeling, (ii) retrieval-augmented generation (RAG) for knowledge-grounded conceptualization, and (iii) intelligent orchestration of engineering software for performance verification and optimization. Working in tandem, these capabilities interpret high-level, unstructured intent, translate it into structured design representations, automatically construct parametric 3D CAD models, generate reliable concept variants using external knowledge bases, and conduct evaluation with iterative optimization via tool calls such as finite-element analysis (FEA). Validation on three representative cases - a gas-turbine blade, a machine-tool column, and a fractal heat sink - shows that the agent completes the pipeline from natural-language intent to verified and optimized designs with reduced manual scripting and setup effort, while promoting innovative design exploration. This work points to a practical path toward human-AI collaborative mechanical engineering and lays a foundation for more dependable, vertically customized MDO systems.
Related Topics
Concepts
Scripting language
Computer science
Workflow
Software engineering
Pipeline (software)
Parametric design
Multidisciplinary design optimization
Iterative design
Systems engineering
Engineering design process
Parametric statistics
Multidisciplinary approach
Software
Software design
Parametric model
CAD
Python (programming language)
Orchestration
Iterative and incremental development
Design language
Design knowledge
Path (computing)
Object-oriented design
Construct (python library)
Computer Aided Design
Engineering
Emulation
Engineering drawing
Modeling language
Integrated design
Work (physics)
Domain knowledge
Artificial intelligence
Metadata
- Type
- article
- Landing Page
- http://arxiv.org/abs/2511.17511
- https://arxiv.org/pdf/2511.17511
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7106782211
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W7106782211Canonical identifier for this work in OpenAlex
- Title
-
A Multidisciplinary Design and Optimization (MDO) Agent Driven by Large Language ModelsWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-10-06Full publication date if available
- Authors
-
Guo, Bingkun, Li Wentian, Liu Xiaojian, Luo, Jiaqi, YU Zibin, Dong Dalong, Zhang Shuyou, Zhang YimingList of authors in order
- Landing page
-
https://arxiv.org/abs/2511.17511Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2511.17511Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2511.17511Direct OA link when available
- Concepts
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Scripting language, Computer science, Workflow, Software engineering, Pipeline (software), Parametric design, Multidisciplinary design optimization, Iterative design, Systems engineering, Engineering design process, Parametric statistics, Multidisciplinary approach, Software, Software design, Parametric model, CAD, Python (programming language), Orchestration, Iterative and incremental development, Design language, Design knowledge, Path (computing), Object-oriented design, Construct (python library), Computer Aided Design, Engineering, Emulation, Engineering drawing, Modeling language, Integrated design, Work (physics), Domain knowledge, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.we | 10 |
| abstract_inverted_index.(i) | 36 |
| abstract_inverted_index.CAD | 78 |
| abstract_inverted_index.MDO | 168 |
| abstract_inverted_index.The | 25 |
| abstract_inverted_index.and | 4, 8, 15, 47, 57, 88, 114, 132, 139, 159 |
| abstract_inverted_index.for | 44, 54, 163 |
| abstract_inverted_index.the | 28, 122, 125 |
| abstract_inverted_index.via | 94 |
| abstract_inverted_index.(ii) | 40 |
| abstract_inverted_index.This | 147 |
| abstract_inverted_index.core | 34 |
| abstract_inverted_index.from | 127 |
| abstract_inverted_index.heat | 117 |
| abstract_inverted_index.into | 70 |
| abstract_inverted_index.lays | 160 |
| abstract_inverted_index.more | 164 |
| abstract_inverted_index.path | 153 |
| abstract_inverted_index.sink | 118 |
| abstract_inverted_index.such | 97 |
| abstract_inverted_index.that | 121 |
| abstract_inverted_index.tool | 95 |
| abstract_inverted_index.with | 91, 135 |
| abstract_inverted_index.work | 148 |
| abstract_inverted_index.(MDO) | 17 |
| abstract_inverted_index.(RAG) | 43 |
| abstract_inverted_index.(iii) | 48 |
| abstract_inverted_index.Agent | 18 |
| abstract_inverted_index.Large | 21 |
| abstract_inverted_index.agent | 26, 123 |
| abstract_inverted_index.calls | 96 |
| abstract_inverted_index.cases | 106 |
| abstract_inverted_index.setup | 140 |
| abstract_inverted_index.shows | 120 |
| abstract_inverted_index.these | 62 |
| abstract_inverted_index.three | 33, 104 |
| abstract_inverted_index.using | 84 |
| abstract_inverted_index.while | 142 |
| abstract_inverted_index.(FEA). | 101 |
| abstract_inverted_index.Design | 14 |
| abstract_inverted_index.Models | 23 |
| abstract_inverted_index.bases, | 87 |
| abstract_inverted_index.blade, | 110 |
| abstract_inverted_index.design | 3, 6, 72, 145 |
| abstract_inverted_index.driven | 19 |
| abstract_inverted_index.intent | 129 |
| abstract_inverted_index.manual | 137 |
| abstract_inverted_index.points | 149 |
| abstract_inverted_index.toward | 154 |
| abstract_inverted_index.(LLMs). | 24 |
| abstract_inverted_index.Working | 59 |
| abstract_inverted_index.column, | 113 |
| abstract_inverted_index.concept | 82 |
| abstract_inverted_index.conduct | 89 |
| abstract_inverted_index.designs | 134 |
| abstract_inverted_index.effort, | 141 |
| abstract_inverted_index.enhance | 5 |
| abstract_inverted_index.fractal | 116 |
| abstract_inverted_index.intent, | 67 |
| abstract_inverted_index.models, | 79 |
| abstract_inverted_index.present | 11 |
| abstract_inverted_index.quality | 7 |
| abstract_inverted_index.reduced | 136 |
| abstract_inverted_index.tandem, | 61 |
| abstract_inverted_index.Language | 22 |
| abstract_inverted_index.analysis | 100 |
| abstract_inverted_index.external | 85 |
| abstract_inverted_index.generate | 80 |
| abstract_inverted_index.human-AI | 155 |
| abstract_inverted_index.pipeline | 126 |
| abstract_inverted_index.reliable | 81 |
| abstract_inverted_index.software | 53 |
| abstract_inverted_index.systems. | 169 |
| abstract_inverted_index.variants | 83 |
| abstract_inverted_index.verified | 131 |
| abstract_inverted_index.workflow | 30 |
| abstract_inverted_index.completes | 124 |
| abstract_inverted_index.construct | 75 |
| abstract_inverted_index.interpret | 64 |
| abstract_inverted_index.iterative | 92 |
| abstract_inverted_index.knowledge | 86 |
| abstract_inverted_index.modeling, | 39 |
| abstract_inverted_index.optimized | 133 |
| abstract_inverted_index.practical | 152 |
| abstract_inverted_index.promoting | 143 |
| abstract_inverted_index.scripting | 138 |
| abstract_inverted_index.translate | 68 |
| abstract_inverted_index.Validation | 102 |
| abstract_inverted_index.accelerate | 1 |
| abstract_inverted_index.customized | 167 |
| abstract_inverted_index.end-to-end | 29 |
| abstract_inverted_index.evaluation | 90 |
| abstract_inverted_index.foundation | 162 |
| abstract_inverted_index.generation | 42 |
| abstract_inverted_index.innovative | 144 |
| abstract_inverted_index.mechanical | 2, 157 |
| abstract_inverted_index.parametric | 38, 76 |
| abstract_inverted_index.structured | 71 |
| abstract_inverted_index.vertically | 166 |
| abstract_inverted_index.dependable, | 165 |
| abstract_inverted_index.engineering | 52, 158 |
| abstract_inverted_index.gas-turbine | 109 |
| abstract_inverted_index.high-level, | 65 |
| abstract_inverted_index.innovation, | 9 |
| abstract_inverted_index.intelligent | 49 |
| abstract_inverted_index.performance | 55 |
| abstract_inverted_index.Optimization | 16 |
| abstract_inverted_index.capabilities | 63 |
| abstract_inverted_index.exploration. | 146 |
| abstract_inverted_index.machine-tool | 112 |
| abstract_inverted_index.optimization | 93 |
| abstract_inverted_index.unstructured | 66 |
| abstract_inverted_index.verification | 56 |
| abstract_inverted_index.automatically | 74 |
| abstract_inverted_index.capabilities: | 35 |
| abstract_inverted_index.collaborative | 156 |
| abstract_inverted_index.optimization. | 58 |
| abstract_inverted_index.orchestrating | 32 |
| abstract_inverted_index.orchestration | 50 |
| abstract_inverted_index.finite-element | 99 |
| abstract_inverted_index.representative | 105 |
| abstract_inverted_index.semi-automates | 27 |
| abstract_inverted_index.natural-language | 128 |
| abstract_inverted_index.representations, | 73 |
| abstract_inverted_index.Multidisciplinary | 13 |
| abstract_inverted_index.conceptualization, | 46 |
| abstract_inverted_index.knowledge-grounded | 45 |
| abstract_inverted_index.retrieval-augmented | 41 |
| abstract_inverted_index.natural-language-driven | 37 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.64129203 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |