Designing lattices for impact protection using transfer learning
Designing lattices for impact protection using transfer learning
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DOI：
10.1016/j.matt.2022.06.051
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发表时间：
2022-07
期刊：
影响因子：
18.9
通讯作者：
Aldair E. Gongora;Kelsey L. Snapp;Richard Pang;T. Tiano;Kristofer G. Reyes;E. Whiting;T. Lawton;E. Morgan;Keith A. Brown
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作者：
Aldair E. Gongora;Kelsey L. Snapp;Richard Pang;T. Tiano;Kristofer G. Reyes;E. Whiting;T. Lawton;E. Morgan;Keith A. Brown
与许多专业应用一样，设计冲击保护结构的速度受到其对专业测试的依赖的限制。在这里，我们开发了一种迁移学习方法，以确定如何更广泛地使用准静态测试来预测冲击保护。我们首先在冲击和准静态域中广泛测试了一个参数化的晶格家族，并训练了一个模型，该模型仅使用准静态测量来预测冲击性能在8%以内。接下来，我们使用一个不同的晶格家族来测试这个模型的可转移性，并发现即使对于其行为外推到训练集之外的结构，性能排名也得到了很好的预测。最后，我们结合联合收割机812准静态和141冲击测试来训练一个模型，该模型预测了新型晶格的绝对冲击性能，误差为18%。这些结果突出了加速专业应用设计的途径，并且可以以数据驱动的方式获得可转移的机械见解。
Like many specialty applications, the pace of designing structures for impact protection is limited by its reliance on specialized testing. Here, we develop a transfer learning approach to determine how more widely available quasi-static testing can be used to predict impact protection. We first extensively test a parametric family of lattices in both impact and quasi-static domains and train a model that predicts impact performance to within 8% using only quasi-static measurements. Next, we test the transferability of this model using a distinct family of lattices and find that performance rank was well predicted even for structures whose behavior extrapolated beyond the training set. Finally, we combine 812 quasi-static and 141 impact tests to train a model that predicts absolute impact performance of novel lattices with 18% error. These results highlight a path for accelerating design for specialty applications and that transferrable mechanical insight can be obtained in a data-driven manner.