Author, Institution: Paulius Karpavičius, Kaunas University of Technology
Science area, field of science: Technological Sciences, Mechanical Engineering, T009
Scientific Supervisor: Prof. Dr. Habil. Vytautas Ostaševičius (Kaunas University of Technology, Technological Sciences, Mechanical Engineering, T009)
Dissertation Defence Board of Mechanical Engineering Science Field:
Prof. Dr. Habil. Arvydas Palevičius (Kaunas University of Technology, Technological Sciences, Mechanical Engineering, T009) – chairman
Prof. Dr. Habil. Vladimir Babitsky (Loughborough University, Technological Sciences, Mechanical Engineering, T009)
Prof. Dr. Habil. Marijonas Bogdevičius (Vilnius Gediminas Technical University, Technological Sciences, Transport Engineering, T003)
Dr. Gintautas Dundulis (Kaunas University of Technology, Technological Sciences, Mechanical Engineering, T009)
The doctoral dissertation is available at the library of Kaunas University of Technology (K. Donelaičio g. 20, Kaunas).
Annotation:
“Industry 4.0” is a new industrial revolution, which is centred on such concepts as connectivity, Big Data and Cloud computing. The adoption of this new concept offers enormous opportunities to deliver the initiatives of the Circular Economy Action Plan for a cleaner and more competitive Europe, while promising to improve the quality, flexibility and efficiency of any production process that implements these concepts. This research work is dedicated to R&D, one of the key milestones in “Industry 4.0”, where digital and physical devices for monitoring the cutting process are developed by using the cyber-physical components.
Another indispensable aspect of “Industry 4.0”, which is the Internet of Things (IoT), enables machine-to-machine connectivity and communication by creating networks that overcome geographical constraints and barriers, allowing dynamic and efficient use of available manufacturing resources. Therefore, this research develops a web-based wireless monitoring system for technological processes with distributed cloud-based Big data applications.
Combining the strengths of IoT platform and Artificial intelligence enabled rapid access to the real-world data and its processing, thus providing a virtual view of the real technological process. Product quality, a key factor in smart manufacturing, is pursued through the research of the tool-workpiece interaction dynamics and innovation of self-powering wireless monitoring of the milling process. The research on the dynamics of tool-workpiece interaction and innovation of the self-powering wireless monitoring of the milling process ensures that the product quality, a key factor in smart manufacturing, is achieved.
The R&D results that are presented in this research will be accessible to the developers of Future Smart Factories; furthermore, it will facilitate the supply of the required products under critical conditions imposed by the Globalization processes.
This dissertation consists of an introduction, main part, which is split into four chapters, conclusions, references and the list of publications.
The first chapter of the dissertation reviews the research literature on “Industry 4.0”, its concept and key components: connectivity, Cloud manufacturing, Big Data and how on-line and real-time machining process monitoring based on self-powering wireless sensor networks fits in with “Industry 4.0”. The literature, concerning ambient energy harvesters, with the main focus on vibrational energy harvesting with piezoelectric transducers, has been reviewed. The application of horn-type waveguide resonators in machining environments with shank-type rotating tools has been investigated.
The second chapter presents the theoretical investigation and modelling of horn-type tool holder for the generation of the L&T mode coupling and energy harvesting from vibrations, excited in the tool, during milling operation.
The third chapter of the dissertation is concerned with the design of self-powering, wireless sensor node. The development of an embedded system used in the device is presented and discussed in detail. The architecture for using the sensor as a monitoring device for condition of rotating shank-type tools has been presented.
In the fourth chapter, the results of the theoretical investigation are verified by the performed experimental studies and application of machine learning models.
Conclusions present the results that were obtained during the theoretical and experimental investigations.
August 26 d.
Dissertation Defense Hall at Kaunas University of Technology (K. Donelaičio g. 73-403, Kaunas)
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