ADs: Active Data-sharing for Data Quality Assurance in Advanced Manufacturing Systems

Abstract--Machine learning (ML) methods are widely used most informative data points benefiting the downstream tasks in manufacturing applications, which usually require a large and mitigate the impact of low-quality data. We collected realworld amount of training data. However, data collection needs extensive in-situ monitoring data of the same additive manufacturing costs and time investments in the manufacturing system, and process from three different machines, two of which are more data scarcity commonly exists. The proposed method is applied to train an industrial internet of things (IIoT), data-sharing is widely enabled anomaly detection model for those two similar machines, and the among multiple machines with similar functionality to augment entire data pool from all three machines is available for selecting the dataset for building ML models. The results demonstrated that our proposed designed similarly, the distribution mismatch inevitably exists in method outperforms the benchmark methods by only requiring their data due to different working conditions, process parameters, 26% of labeled training samples. In addition, all selected data measurement noise, etc. However, the effective application samples are from machines with similar conditions, while the of ML methods is built upon the assumption that the training data from the different machines are prevented from misleading and testing data are sampled from the same distribution. In this work, we propose an Active Data-sharing (ADs) framework to ensure the quality of the shared data among multiple machines. Low-quality data here refers to data samples collected from machines/processes different from the target one.