Our paper titled “Hierarchical data models improve the accuracy of feature level predictions for additively manufactured parts” has been published on Additive Manufacturing. The paper was co-authored by Yuhang Yang and Prof. Chenhui Shao.
Industrial-scale production applications of additive manufacturing (AM) are growing rapidly, and scalable AM production requires quality systems that monitor and control part geometric accuracy across multiple machines that operate within a factory. This research develops a hybrid hierarchical modeling (HHM) approach to characterize the geometric accuracy of parts produced across multiple identical AM machines. Our approach organizes the geometric accuracy data into a hierarchy that represents data from individual parts, the positions of parts within the builds, and the machines that produced those parts. By leveraging data from different printers, the performance of the part-level geometric accuracy modeling is substantially improved compared with competing methods. The modeling approach is extensible to other types of AM and could be used as part of a quality system within AM factory.
Read the full paper here.