| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A Bayesian framework to estimate part quality and associated uncertainties in multistage manufacturing
|
Papananias, Moschos
|
|
2019
|
105 |
C |
p. 35-47
|
artikel
|
| 2 |
A Big Data platform for smart meter data analytics
|
Wilcox, Tom
|
|
2019
|
105 |
C |
p. 250-259
|
artikel
|
| 3 |
A conceptual scheme improvement approach based on the performance value of the principle solution taking a coal mining machine as a case study
|
Jiang, Shaofei
|
|
2019
|
105 |
C |
p. 17-34
|
artikel
|
| 4 |
A data-driven simulation to support remanufacturing operations
|
Goodall, Paul
|
|
2019
|
105 |
C |
p. 48-60
|
artikel
|
| 5 |
A deep learning approach to condition monitoring of cantilever beams via time-frequency extended signatures
|
Onchis, Habil. Darian M.
|
|
2019
|
105 |
C |
p. 177-181
|
artikel
|
| 6 |
A machine learning case study with limited data for prediction of carbon fiber mechanical properties
|
Golkarnarenji, Gelayol
|
|
2019
|
105 |
C |
p. 123-132
|
artikel
|
| 7 |
A novel convolutional neural network based fault recognition method via image fusion of multi-vibration-signals
|
Wang, Huaqing
|
|
2019
|
105 |
C |
p. 182-190
|
artikel
|
| 8 |
A product life cycle ontology for additive manufacturing
|
Mohd Ali, Munira
|
|
2019
|
105 |
C |
p. 191-203
|
artikel
|
| 9 |
Augmented reality via expert demonstration authoring (AREDA)
|
Bhattacharya, Bhaskar
|
|
2019
|
105 |
C |
p. 61-79
|
artikel
|
| 10 |
Big data analytics and business failures in data-Rich environments: An organizing framework
|
Amankwah-Amoah, Joseph
|
|
2019
|
105 |
C |
p. 204-212
|
artikel
|
| 11 |
Collaborative design process and product knowledge methodology for mechatronic systems
|
Mcharek, Mehdi
|
|
2019
|
105 |
C |
p. 213-228
|
artikel
|
| 12 |
Designing and deploying a business process for product recovery and repair at a servicing organization: A case study and framework proposal
|
Kodhelaj, Iva
|
|
2019
|
105 |
C |
p. 80-98
|
artikel
|
| 13 |
Digitalizing Swedish industry: What is next?
|
Gürdür, Didem
|
|
2019
|
105 |
C |
p. 153-163
|
artikel
|
| 14 |
Dynamic condition monitoring for 3D printers by using error fusion of multiple sparse auto-encoders
|
Zhang, Shaohui
|
|
2019
|
105 |
C |
p. 164-176
|
artikel
|
| 15 |
Editorial Board
|
|
|
2019
|
105 |
C |
p. ii
|
artikel
|
| 16 |
Emergency preparedness in industrial plants: A forward-looking solution based on industry 4.0 enabling technologies
|
Longo, Francesco
|
|
2019
|
105 |
C |
p. 99-122
|
artikel
|
| 17 |
Ensemble deep learning-based fault diagnosis of rotor bearing systems
|
Ma, Sai
|
|
2019
|
105 |
C |
p. 143-152
|
artikel
|
| 18 |
Event processing in supply chain management – The status quo and research outlook
|
Konovalenko, Iurii
|
|
2019
|
105 |
C |
p. 229-249
|
artikel
|
| 19 |
Safety analysis of train control system based on model-driven design methodology
|
Baouya, Abdelhakim
|
|
2019
|
105 |
C |
p. 1-16
|
artikel
|
| 20 |
The costs and benefits of product configuration projects in engineer-to-order companies
|
Haug, Anders
|
|
2019
|
105 |
C |
p. 133-142
|
artikel
|
| 21 |
The design space of production planning and control for industry 4.0
|
Bendul, Julia C.
|
|
2019
|
105 |
C |
p. 260-272
|
artikel
|
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