Fast optimisation of the formability of dry fabric preforms: A Bayesian approach
Fast optimisation of the formability of dry fabric preforms: A Bayesian approach
复制标题
快速优化干织物预成型件的成型性：贝叶斯方法
DOI：
10.1016/j.matdes.2023.111986
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发表时间：
2023
影响因子：
8.4
通讯作者：
Chen S
中科院分区：
文献类型：
--
作者：
Chen S
在这项工作中探索了一个新的框架，用于优化形成干燥的纺织材料的过程中，使用有限元（FE）分析和高斯过程（GP）回归。建立了有限元模型，模拟了无卷曲织物在半球形模具上的双隔膜成形过程。开发了GP仿真器，对有限元模型生成的数据进行回归，并用于成形工艺的优化。重要的是，有限元模拟可以捕捉在不同的成形配置和边界条件下的过程中的皱纹的形成。在成形过程中引入刚性块，通过控制块体位置来影响缺陷的产生。从有限元输出文件中提取的几个指标被用来评估成形模拟的起皱程度，并进行比较，作为模型输出。通过拉丁超三次采样（LHS）生成一个小数据集，以训练初始GP代理模型。然后，通过贝叶斯主动学习方法将模型的预测误差降到可接受的水平（<10%）。然后，经过训练的代理模型仅使用数十次模拟来优化成形过程，而不是传统优化方法所需的数百甚至数千次。
A new framework for optimising the process of forming dry textile materials using finite element (FE) analysis and Gaussian Process (GP) regression is explored in this work. FE models were generated to simulate the double diaphragm forming process of non-crimp fabric over a hemisphere tool. A GP emulator was developed to regress the dataset generated by FE model, then used to optimise the forming process. Importantly the FE simulations can capture the formation of wrinkles during the process under different forming configurations and boundary conditions. Rigid blocks (risers) were introduced to the forming process to affect the defects generation by controlling the block positions. Several indices abstracted from FE output files were used to assess the wrinkle level of the forming simulations and compared, as the model output. A small dataset was generated by Latin hypercubic sampling (LHS) to train an initial GP surrogate model. Then, the prediction error of the model was reduced to an acceptable level (<10 %) through a Bayesian active learning method. The trained surrogate model was then used to optimise a forming process using only tens of simulations, rather than hundreds or even thousands, as required by traditional optimisation methods.