Towards Reproducible Machine Learning-Based Process Monitoring and Quality Prediction Research for Additive Manufacturing

Published

November 13, 2024

Author(s)

Yan Lu, Zhuo Yang, Jiarui Xie, Mutahar Safdar, Andrei Mircea Romascanu, Hyunwoong Ko, Yaoyao Fiona Zhao

Abstract

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.

Proceedings Title

ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference

Conference Dates

August 25-28, 2024

Conference Location

Washington DC, DC, US

Conference Title

ASME IDETC-CIE 2024

Pub Type

Conferences

Download Paper

https://doi.org/10.1115/DETC2024-144461

Local Download

Keywords

additive manufacturing, machine learning, reproducibility, process monitoring, quality prediction

Citation

Lu, Y. , Yang, Z. , Xie, J. , Safdar, M. , Romascanu, A. , Ko, H. and Zhao, Y. (2024), Towards Reproducible Machine Learning-Based Process Monitoring and Quality Prediction Research for Additive Manufacturing, ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Washington DC, DC, US, [online], https://doi.org/10.1115/DETC2024-144461, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957901 (Accessed March 14, 2025)