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Manufacturing process signatures reflect the process stability and anomalies that potentially lead to detrimental effects on the manufactured outcomes. Sensing technologies, especially in-situ image sensors, are widely used to capture process signatures for diagnostics and prognostics. This imaging data is crucial evidence for process signature characterization and monitoring. A critical aspect of process signature analysis is identifying the unique patterns in an image that differ from the generic behavior of the manufacturing process in order to detect anomalies. It is equivalent to separating the “unique features” and process-wise (or phase-wise) “shared features” from the same image and recognizing the transient anomaly, i.e., recognizing the outlier “unique features”. In state-of-the-art literature, image-based process signature analysis relies on conventional feature extraction procedures, which limit the “view” of information to each image and cannot decouple the shared and unique features. Consequently, the features extracted are less interpretable, and the anomaly detection method cannot distinguish the abnormality in the current process signature from the process-wise evolution. Targeting this limitation, this study proposes personalized feature extraction (PFE) to decouple process-wise shared features and transient unique features from a sensor image and further develops process signature characterization and anomaly detection strategies. The PFE algorithm is designed for heterogeneous data with shared features. Supervised and unsupervised anomaly detection strategies are developed upon PFE features to remove the shared features from a process signature and examine the unique features for abnormality. The proposed method is demonstrated on two datasets (i) selected data from the 2018 AM Benchmark Test Series from the National Institute of Standards and Technology (NIST), and (ii) thermal measurements in additive manufacturing of a thin-walled structure of Ti–6Al–4V. The results highlight the power of personalized modeling in extracting features from manufacturing imaging data. 

Over the years, Internet of Things (IoT) devices have become more powerful. This sets forth a unique opportunity to exploit local computing resources to distribute model learning and circumvent the need to share raw data. The underlying distributed and privacy-preserving data analytics approach is often termed federated learning (FL). A key challenge in FL is the heterogeneity across local datasets. In this article, we propose a new personalized FL model, PFL-DA, by adopting the philosophy of domain adaptation. PFL-DA tackles two sources of data heterogeneity at the same time: a covariate and concept shift across local devices. We show, both theoretically and empirically, that PFL-DA overcomes intrinsic shortcomings in state of the art FL approaches and is able to borrow strength across devices while allowing them to retain their own personalized model. As a case study, we apply PFL-DA to distributed desktop 3D printing where we obtain more accurate predictions of printing speed, which can help improve the efficiency of the printers.