A Data-Driven Framework to Select a Cost-Efficient Subset of Parameters to Qualify Sourced Materials
A Data-Driven Framework to Select a Cost-Efficient Subset of Parameters to Qualify Sourced Materials
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DOI：
10.1007/s40192-022-00266-3
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发表时间：
2022-07
影响因子：
3.3
通讯作者：
N. M. Senanayake;Jennifer L. W. Carter;C. Bowman;D. Ellis;J. Stuckner
中科院分区：
文献类型：
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作者：
N. M. Senanayake;Jennifer L. W. Carter;C. Bowman;D. Ellis;J. Stuckner
用于制造的粉末质量可以通过数百种不同的变量进行认证。评估所有这些不同指标对增材制造工程产品性能的影响是一个宝贵但耗时的规范过程。在这项工作中，实施了一个全面的，可推广的，数据驱动的框架，以选择预测目标性能变量所需的最佳粉末加工和微观结构变量。该框架在从选择性激光熔化、增材制造的Inconel 718收集的高维数据集上进行了演示。129个粉末质量变量，包括颗粒形态，流变学，化学成分，和构建组合物进行了评估，其对8个微观结构特征和16个机械性能的影响。通过使用统计分析和机器学习模型确定每个粉末和微观结构变量的重要性。经训练的模型预测目标机械性能的R2值为0.9或更高。结果表明，可以通过控制只有少数关键粉末性能，而不需要收集微观结构数据，实现所需的机械性能。该框架通过确定预测给定源材料将导致期望结果所需的实验的最佳子集，显著减少了使源材料合格用于生产的时间和成本。这个一般框架可以很容易地应用于其他材料系统。
The quality of powder processed for manufacturing can be certified by hundreds of different variables. Assessing the impact of all these different metrics on the performance of additively manufactured engineered products is an invaluable, but time intensive specification process. In this work, a comprehensive, generalizable, data-driven framework was implemented to select the optimal powder processing and microstructure variables that are required to predict the target property variables. The framework was demonstrated on a high-dimensional dataset collected from selective laser melted, additively manufactured, Inconel 718. One hundred and twenty-nine powder quality variables including particle morphology, rheology, chemical composition, and build composition were assessed for their impact on eight microstructural features and sixteen mechanical properties. The importance of each powder and microstructure variable was determined by using statistical analysis and machine learning models. The trained models predicted target mechanical properties with an R2value of 0.9 or higher. The results indicate that the desired mechanical properties can be achieved by controlling only a few critical powder properties and without the need for collecting microstructure data. This framework significantly reduces the time and cost of qualifying source materials for production by determining an optimal subset of experiments needed to predict that a given source material will lead to a desired outcome. This general framework can be easily applied to other material systems.