Bayesian diagnostic learning for a costly composite manufacturing: critical role of dataset size and auxiliary in situ measurements

Author:

Crawford Bryn,Ramezankhani Milad,Milani Abbas S.

Funder

New Frontiers in Research Fund (NFRF) of Canada – Exploration stream

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering

Reference54 articles.

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2. Lee J, Lapira E, Bagheri B, Kao H (2013) Recent advances and trends in predictive manufacturing systems in big data environment. Manuf Lett 1:38–41

3. Manyika J, Chui M, Brown B et al (2011) Big data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute

4. Zeid A, Sundaram S, Moghaddam M et al (2019) Interoperability in smart manufacturing: research challenges. Machines 7:21

5. Castelo-Branco I, Cruz-Jesus F, Oliveira T (2019) Assessing Industry 4.0 readiness in manufacturing: evidence for the European Union. Comput Ind 107:22–32. https://doi.org/10.1016/j.compind.2019.01.007

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