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• W4285192286 abstract "Detecting abnormal conditions in manufacturing processes is a crucial task to avoid unplanned downtimes and prevent quality issues. The increasing amount of available high-frequency process data combined with advances in the field of deep autoencoder-based monitoring offers huge potential in enhancing the performance of existing Multivariate Statistical Process Control approaches. We investigate the application of deep auto encoder-based monitoring approaches and experiment with the reconstruction error and the latent representation of the input data to compute Hotelling’s T2 and Squared Prediction Error monitoring statistics. The investigated approaches are validated using a real-world sheet metal forming process and show promising results." @default.
• W4285192286 created "2022-07-14" @default.
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• W4285192286 date "2022-01-01" @default.
• W4285192286 modified "2023-10-05" @default.
• W4285192286 title "Deep learning for multivariate statistical in-process control in discrete manufacturing: A case study in a sheet metal forming process" @default.
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• W4285192286 doi "https://doi.org/10.1016/j.procir.2022.05.002" @default.
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