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：增材制造工艺（如激光粉末床熔融）通过逐层铺粉熔化直至形成自由形态零件，可实现物体的制造。为提升增材制造过程中材料的性能，需根据工艺条件预测材料表征特性。对于热电材料，功率因子是衡量材料热-电转换效率的关键参数。尽管已有研究采用多种技术预测不同热电材料的性能表征特性，但尚未探索利用机器学习模型预测增材制造过程中碲化铋（Bi₂Te₃）的功率因子。由于Bi₂Te₃是低温应用的标准材料，此项研究具有重要意义。为此，我们利用Bi₂Te₃增材制造过程中的工艺参数数据及原位传感器监测数据，训练多种机器学习模型以预测其热电功率因子。我们采用监督学习方法，以80%数据训练、20%数据测试，并利用置换特征重要性方法识别对功率因子预测最重要的工艺参数及原位传感器特征。基于集成学习的方法（随机森林、AdaBoost分类器与装袋分类器）在预测功率因子方面表现最优，其中装袋分类器模型实现了90%的最高准确率。此外，我们确定了15个关键工艺参数及原位传感器特征，用于表征材料制造性能（如功率因子）。这些特征可进一步优化以最大化热电材料的功率因子，进而提升该材料所制产品的质量。