Defect Classification for Additive Manufacturing with Machine Learning.

ABSTRACT: 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 be

SUBMITTER: Altmann ML 

PROVIDER: S-EPMC10533092 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature