An additive manufacturing (AM) process, like laser powder bed fusion, allows for the fabrication of objects by spreading and melting powder in layers until a freeform part shape is created. In order to improve the properties of the material involved in the AM process, it is important to predict the material characterization property as a function of the processing conditions. In thermoelectric materials, the power factor is a measure of how efficiently the material can convert heat to electricity. While earlier works have predicted the material characterization properties of different thermoelectric materials using various techniques, implementation of machine learning models to predict the power factor of bismuth telluride (Bi2Te3) during the AM process has not been explored. This is important as Bi2Te3 is a standard material for low temperature applications. Thus, we used data about manufacturing processing parameters involved and in-situ sensor monitoring data collected during AM of Bi2Te3, to train different machine learning models in order to predict its thermoelectric power factor. We implemented supervised machine learning techniques using 80% training and 20% test data and further used the permutation feature importance method to identify important processing parameters and in-situ sensor features which were best at predicting power factor of the material. Ensemble-based methods like random forest, AdaBoost classifier, and bagging classifier performed the best in predicting power factor with the highest accuracy of 90% achieved by the bagging classifier model. Additionally, we found the top 15 processing parameters and in-situ sensor features to characterize the material manufacturing property like power factor. These features could further be optimized to maximize power factor of the thermoelectric material and improve the quality of the products built using this material.
翻译:一种像激光粉床熔化这样的添加制造(AM)过程可以通过在层中散布和熔化粉末来制造物品,直到形成自由形态的部件。为了改进参与AM过程的材料的性能,重要的是要预测材料特性随处理条件的变化。在热电材料中,功率因子是材料将热能转化为电能的效能的一种度量方式。尽管早期作品使用各种技术预测了不同热电材料的材料特性,但尚未探索使用机器学习模型来预测铋碲化物(Bi2Te3)在AM过程中的功率因子。这很重要,因为Bi2Te3是低温应用的标准材料。因此,我们使用所涉及的生产加工参数和在AM Bi2Te3过程期间收集的原位传感器监测数据的数据来训练不同的机器学习模型,以预测其热电功率因子。我们使用80%的训练数据和20%的测试数据实施了监督式机器学习技术,并进一步使用排列特征重要性方法来识别最适合预测材料功率因子的重要加工参数和原位传感器特征。集成学习方法如随机森林,AdaBoost分类器和装袋分类器在预测功率因子方面表现最佳,袋装分类器模型的最高准确度达到90%。此外,我们发现了前15个加工参数和原位传感器特征,以表征材料制造特性,如功率因子。这些特征可以进一步优化,以最大化热电材料的功率因子,并提高使用该材料制造的产品的质量。