VIABILITY OF SIEMENS INDUSTRIAL EDGE TO DEPLOY AI INTO AUTOMATION PROCESSES.

Pankaj Mishra, B.Tech.

Master's thesis

Abstract:

The study explores the potential of Siemens Industrial Edge to integrate artificial intelligence (AI) methods into automation processes for Vertical Form Fill Seal (VFFS) machines. The focus is on the deployment of a Convolutional Neural Networks (CNN) for production predictive purposes. The research analyses the benefits of utilizing Siemens Industrial Edge in terms of predicting the quality of packaging belonging to different class. The study also discusses the challenges of implementing AI into automation processes and the importance of data accuracy in predictive maintenance. The findings suggest that deploying Siemens Industrial Edge with AI for predictive maintenance can significantly improve the performance and efficiency of VFFS machines. Overall, the research provides insights into the viability of Siemens Industrial Edge in deploying AI for automation processes and highlights the potential benefits of using Machine learning for predictive maintenance in industrial settings.

Keywords

Siemens, Industrial Edge, Artificial Intelligence, Viability, Industry 4.0, Edge computing, Edge devices, Industrial IoT, Real-time analytics, Data processing, Machine learning, Predictive maintenance, Smart manufacturing, Operational efficiency

Language used: English

Date on which the thesis was submitted / produced: 4. 4. 2023

Thesis defence

• Date of defence: 22. 5. 2023
• Supervisor: Ing. Josef Pavlíček, Ph.D.
• Reader: Tomáš Hájek, externi

Czech University of Life Sciences Prague

Faculty of Economics and Management

Master programme:
Informatics