生成设计
水准点(测量)
计算机科学
复制
人工智能
工程设计过程
机器学习
生成语法
过程(计算)
生成模型
替代模型
国家(计算机科学)
概率设计
工程类
程序设计语言
机械工程
公制(单位)
运营管理
统计
数学
大地测量学
地理
作者
Luka Gradišar,Matevž Dolenc,Robert Klinc
标识
DOI:10.1016/j.autcon.2024.105284
摘要
Machine learned generative design is an extension of the generative design process, addressing its inherent limitations, particularly those of interoperability. The proposed approach uses machine learning-based surrogate models, trained on computational model data, to replicate design evaluations and integrate them into a common environment. In this way, design alternatives can be generated and tested that satisfy all design requirements and considerations. The effectiveness of this approach is demonstrated by the design and optimisation of the enclosure structure for the New Robotic Telescope. Its complexity is characterised by multiple operating states that the enclosure can assume, in particular the closed state and the opening/closing state, each of which has a different structural behaviour. Using our approach, the results from each state were replicated with machine learning models and combined into a single evaluation model. This resulted in finding multiple solutions that outperformed the benchmark design. The results demonstrate not only the success of our method over conventional strategies, but also highlight its potential to redefine future design optimisation processes.
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