Towards reproducible machine learning-based process monitoring and quality prediction research for additive manufacturing

Machine learning (ML)-based monitoring systems have been extensively developed to enhance the print quality of additive manufacturing (AM). In-situ and in-process data acquired using sensors can be used to train ML models that detect process anomalies, predict part quality, and adjust process parameters. However, the reproducibility of the proposed AM monitoring systems has not been investigated. There has not been a method to evaluate and improve reproducibility in the joint domain of AM and ML. Consequently, some crucial information for reproducing the research is usually missing from the publications; thus, systems reproduced based on the publications often cannot achieve the claimed performance. This paper establishes the definition of reproducibility in this domain, proposes a reproducibility investigation pipeline, and composes a reproducibility checklist. A research is reproducible if a performance comparable to the original research can be obtained when reproduced by a different team using a different experiment setup. The reproducibility investigation pipeline sequentially guides the readers through all the necessary reproduction steps, during which the reproducibility checklist will help extract the reproducibility information from the publication. A case study that reproduced a vision-based warping detection system demonstrated the usage and validated the efficacy of the proposed pipeline and checklist. It has been observed that the reproducibility checklist can help the authors verify that all the information critical to reproducibility is provided in the publications. The investigation pipeline can help identify the missing reproducibility information, which should be acquired from the original authors to achieve the claimed performance.