15 citations as recorded by crossref.

1. Predictive Maintenance and Intelligent Sensors in Smart Factory: Review M. Pech et al. https://doi.org/10.3390/s21041470
2. A review of digital twin in product design and development C. Lo et al. https://doi.org/10.1016/j.aei.2021.101297
3. Impact of Using a Predictive Neural Network of Multi-Term Zenith Angle Function on Energy Management of Solar-Harvesting Sensor Nodes M. Al-Omary et al. https://doi.org/10.2139/ssrn.4144365
4. Artifact-free coordinate registration of heterogeneous Large-Scale Metrology systems T. Pfeifer et al. https://doi.org/10.1016/j.cirp.2019.04.077
5. Development of data collection system for monitoring the atmospheric air state in Krasnoyarsk region A. Kadochnikov https://doi.org/10.35595/2414-9179-2021-2-27-205-217
6. Industrial condition monitoring with smart sensors using automated feature extraction and selection T. Schneider et al. https://doi.org/10.1088/1361-6501/aad1d4
7. Inspection procedures in manufacturing processes: recent studies and research perspectives G. Genta et al. https://doi.org/10.1080/00207543.2020.1766713
8. Production digital twin: a systematic literature review of challenges S. Kerrouchi et al. https://doi.org/10.1080/0951192X.2024.2314792
9. Prototype for dual digital traceability of metrology data using X.509 and IOTA M. Peterek & B. Montavon https://doi.org/10.1016/j.cirp.2020.04.104
10. Prototype of city environmental monitoring system based on geoportal technologies A. Kadochnikov et al. https://doi.org/10.1088/1757-899X/537/6/062052
11. SENSORY METHODS THAT SUPPORT PREDICTIVE MAINTENANCE IN AN IOT OPTICS D. Magalhães Viegas Junior https://doi.org/10.51249/jid.v5i01.1919
12. MONITORING OF OIL ANALYSIS THROUGH SENSORS D. Magalhães Viegas Junior https://doi.org/10.51249/jid.v5i01.1931
13. Holarchy for line-less mobile assembly systems operation in the context of the internet of production A. Buckhorst et al. https://doi.org/10.1016/j.procir.2021.03.064
14. A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics Z. Huang et al. https://doi.org/10.3390/s21196340
15. Automated Parameterization of Errors in Additive Manufacturing Parts for Data Feedback in Digital Manufacturing Systems C. Peña et al. https://doi.org/10.1109/ACCESS.2025.3633685