Towards reproducible machine learning-based process monitoring and quality prediction research for additive manufacturing
Jiarui Xie; Mutahar Safdar; Andrei Mircea; Bi Cheng Zhao; Yan Lu; Hyunwoong Ko; Zhuo Yang; Yaoyao Fiona Zhao · 2024 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Machine learning (ML)-based cyber-physical systems (CPSs) have been extensively developed to improve the print quality of additive manufacturing (AM). However, the reproducibility of these systems, as presented in published research, has not been thoroughly investigated due to a lack of formal evaluation methods. Reproducibility, a critical component of trustworthy artificial intelligence, is achieved when an independent team can replicate the findings or artifacts of a study using a different experimental setup and achieve comparable performance. In many publications, critical information nec
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Metadata source: arXiv