Efficient searching of processing parameter space to enable inverse microstructural design of materials
Efficient searching of processing parameter space to enable inverse microstructural design of materials
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DOI：
10.1016/j.actamat.2023.119562
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发表时间：
2024-01
期刊：
影响因子：
9.4
通讯作者：
Dung-Yi Wu;Todd C. Hufnagel
中科院分区：
文献类型：
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作者：
Dung-Yi Wu;Todd C. Hufnagel
通过使用预测新材料性能的基于物理的正向模型，可以加速材料设计。在材料的性能取决于其微观结构的情况下，这些模型可以用作优化方案的一部分，以预测实现设计目标所需的微观结构特征。然而，生产这些微结构需要我们确定产生目标微结构所需的工艺参数。在这里，我们演示了使用简单的解析正向模型的贝叶斯优化的使用，使得在商业铝合金等温热处理的优化背景下的反向工艺设计能够实现(I)特定金属间化合物相的目标体积分数，(Ii)特定的铝晶粒度分布，以及(Iii)同时实现这两个目标。我们讨论了具有标量期望函数的贝叶斯优化在解决多目标问题中的用途和局限性，并展示了具有期望超体积改进的贝叶斯优化来确定描述多目标之间权衡的帕累托前沿。我们将贝叶斯优化算法与遗传算法(NSGA-II)进行了比较，结果表明，虽然这两种方法产生了相似的帕累托前沿，但贝叶斯优化算法更快地收敛于解。我们进一步说明了正演模型中的参数不确定性对帕累托前沿不确定性的影响。最后，我们通过实验证明，优化的工艺参数确实允许我们制造出具有所需微结构的样品。
Materials design can be accelerated by the use of physics-based forward models that predict the properties of new materials. In cases where the properties of the material depend on its microstructure, the models can be used as part of an optimization scheme to predict the microstructural features that are required to achieve the design objectives. Producing these microstructures, however, requires that we determine the processing parameters necessary to produce the target microstructure. Here we demonstrate the use of Bayesian optimization using simple analytic forward models to enable this inverse process design in the context of optimization of isothermal heat treatment of a commercial aluminum alloy to achieve (i) a target volume fraction of a specific intermetallic phase, (ii) a specified aluminum grain size distribution, and (iii) both objectives simultaneously. We discuss the use, and limitations, of Bayesian optimization with a scalar desirability function for solving multi-objective problems, and demonstrate Bayesian optimization with expected hypervolume improvement to determine the Pareto front describing the trade-off between multiple objectives. We compare Bayesian optimization with a genetic algorithm (NSGA-II) and show that while the two approaches produce similar Pareto fronts, the Bayesian optimization converges on the solution more quickly. We further illustrate the effect of parametric uncertainty in the forward models on the uncertainty of the Pareto front. Finally, we demonstrate experimentally that the optimized process parameters do indeed allow us to make samples with the desired microstructure.