Altmann Mika León, Benthien Thiemo, Ellendt Nils, Toenjes Anastasiya
Leibniz Institute for Materials Engineering-IWT, Badgasteiner Straße 3, 28359 Bremen, Germany.
Faculty of Production Engineering, University of Bremen, Badgasteiner Straße 1, 28359 Bremen, Germany.
Materials (Basel). 2023 Sep 16;16(18):6242. doi: 10.3390/ma16186242.
Additive manufacturing offers significant design freedom and the ability to selectively influence material properties. However, conventional processes like laser powder bed fusion for metals may result in internal defects, such as pores, which profoundly affect the mechanical characteristics of the components. The extent of this influence varies depending on the specific defect type, its size, and morphology. Furthermore, a single component may exhibit various defect types due to the manufacturing process. To investigate these occurrences with regard to other target variables, this study presents a random forest tree model capable of classifying defects in binary images derived from micrographs. Our approach demonstrates a classification accuracy of approximately 95% when distinguishing between keyhole and lack of fusion defects, as well as process pores. In contrast, unsupervised models yielded prediction accuracies below 60%. The model's accuracy in differentiating between lack of fusion and keyhole defects varies based on the manufacturing process's parameters, primarily due to the irregular shapes of keyhole defects. We provide the model alongside this paper, which can be utilized on a standard computer without the need for in situ monitoring systems during the additive manufacturing process.
增材制造提供了显著的设计自由度以及选择性地影响材料性能的能力。然而，诸如金属激光粉末床熔融等传统工艺可能会导致内部缺陷，例如气孔，这会深刻影响部件的机械特性。这种影响的程度取决于特定的缺陷类型、其尺寸和形态。此外，由于制造工艺的原因，单个部件可能会呈现出各种缺陷类型。为了针对其他目标变量研究这些情况，本研究提出了一种随机森林树模型，该模型能够对源自显微照片的二值图像中的缺陷进行分类。当区分匙孔缺陷、未熔合缺陷以及工艺气孔时，我们的方法展示出了约95%的分类准确率。相比之下，无监督模型的预测准确率低于60%。该模型在区分未熔合缺陷和匙孔缺陷时的准确率会因制造工艺参数而有所不同，主要是由于匙孔缺陷形状不规则。我们随本文提供了该模型，其可在标准计算机上使用，在增材制造过程中无需原位监测系统。
