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Data fusion strategies to combine sensor and multivariate model outputs for multivariate statistical process control
Data fusion strategies to combine sensor and multivariate model outputs for multivariate statistical process control
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Data fusion strategies to combine sensor and multivariate model outputs for multivariate statistical process control

de Oliveira, Rodrigo R. ; Avila, Claudio ; Bourne, Richard ; Muller, Frans ; de Juan, Anna

Analytical and bioanalytical chemistry, 2020-04, Vol.412 (9), p.2151-2163 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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2
A smart process controller framework for Industry 4.0 settings
A smart process controller framework for Industry 4.0 settings
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A smart process controller framework for Industry 4.0 settings

Cohen, Yuval ; Singer, Gonen

Journal of intelligent manufacturing, 2021-10, Vol.32 (7), p.1975-1995 [Periódico revisado por pares]

New York: Springer US

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3
In-process sensing in selective laser melting (SLM) additive manufacturing
In-process sensing in selective laser melting (SLM) additive manufacturing
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In-process sensing in selective laser melting (SLM) additive manufacturing

Spears, Thomas G. ; Gold, Scott A.

Integrating materials and manufacturing innovation, 2016-02, Vol.5 (1), p.16-40 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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4
Pharmaceutical Quality by Design: Product and Process Development, Understanding, and Control
Pharmaceutical Quality by Design: Product and Process Development, Understanding, and Control
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Pharmaceutical Quality by Design: Product and Process Development, Understanding, and Control

Yu, Lawrence X.

Pharmaceutical research, 2008-04, Vol.25 (4), p.781-791 [Periódico revisado por pares]

Boston: Springer US

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5
Integration of multivariate statistical process control and engineering process control: a novel framework
Integration of multivariate statistical process control and engineering process control: a novel framework
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Integration of multivariate statistical process control and engineering process control: a novel framework

Siddiqui, Yasir A. ; Saif, Abdul-Wahid A. ; Cheded, Lahouari ; Elshafei, Moustafa ; Rahim, Abdur

International journal of advanced manufacturing technology, 2015-04, Vol.78 (1-4), p.259-268 [Periódico revisado por pares]

London: Springer London

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6
Online low-field NMR spectroscopy for process control of an industrial lithiation reaction—automated data analysis
Online low-field NMR spectroscopy for process control of an industrial lithiation reaction—automated data analysis
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Online low-field NMR spectroscopy for process control of an industrial lithiation reaction—automated data analysis

Kern, Simon ; Meyer, Klas ; Guhl, Svetlana ; Gräßer, Patrick ; Paul, Andrea ; King, Rudibert ; Maiwald, Michael

Analytical and bioanalytical chemistry, 2018-05, Vol.410 (14), p.3349-3360 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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7
Adaptive process control of wire and arc additive manufacturing for fabricating complex-shaped components
Adaptive process control of wire and arc additive manufacturing for fabricating complex-shaped components
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Adaptive process control of wire and arc additive manufacturing for fabricating complex-shaped components

Li, Fang ; Chen, Shujun ; Wu, Zhaoyang ; Yan, Zhihong

International journal of advanced manufacturing technology, 2018-04, Vol.96 (1-4), p.871-879 [Periódico revisado por pares]

London: Springer London

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8
SSMSPC: self-supervised multivariate statistical in-process control in discrete manufacturing processes
SSMSPC: self-supervised multivariate statistical in-process control in discrete manufacturing processes
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SSMSPC: self-supervised multivariate statistical in-process control in discrete manufacturing processes

Biegel, Tobias ; Helm, Patrick ; Jourdan, Nicolas ; Metternich, Joachim

Journal of intelligent manufacturing, 2024-08, Vol.35 (6), p.2671-2698 [Periódico revisado por pares]

New York: Springer US

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9
High cell density perfusion process for high yield of influenza A virus production using MDCK suspension cells
High cell density perfusion process for high yield of influenza A virus production using MDCK suspension cells
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High cell density perfusion process for high yield of influenza A virus production using MDCK suspension cells

Wu, Yixiao ; Bissinger, Thomas ; Genzel, Yvonne ; Liu, Xuping ; Reichl, Udo ; Tan, Wen-Song

Applied microbiology and biotechnology, 2021-02, Vol.105 (4), p.1421-1434 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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10
Control chart pattern recognition using the convolutional neural network
Control chart pattern recognition using the convolutional neural network
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Control chart pattern recognition using the convolutional neural network

Zan, Tao ; Liu, Zhihao ; Wang, Hui ; Wang, Min ; Gao, Xiangsheng

Journal of intelligent manufacturing, 2020-03, Vol.31 (3), p.703-716 [Periódico revisado por pares]

New York: Springer US

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