Identification of parameter sets for the selection of microelectronic components for sensor-integrating machine elements

Journal
IEEE sensors journal  
Volume
24
Issue
7
Start Page
11174
End Page
11183
Citation
IEEE Sensors Journal 24 (7): 11174-11183 (2024)
Publisher DOI
10.1109/JSEN.2024.3365271
Scopus ID
2-s2.0-85185382944
Publisher
IEEE
The potentials opening up by cyber-physical systems in the form of sensor-integrating machine elements (SiMEs) allowing for in situ measurements result in more complex systems with a multitude of different microelectronic components, nevertheless providing the database usable for digitalization of in situ processes as well as allowing the Internet of Things implementation for processes. Increasing system complexity again results in a more complex system design and configuration process, taking into account the properties of each subsystem. Obtaining a framework for selecting the individual components for creating a SiME therefore is crucial for simplifying the design process. Thereby, this article presents a first step toward such a framework, by identifying parameter sets which can be used for selecting the microelectronic components to be used in the machine element in design, where the parameters are seen as characteristics of the individual components, limiting the number of usable components and therefore allowing the design and configuration process. The method used in this article is based on use case-specific as well as general parameter analysis, which is designed to be applied to a multitude of use cases for different machine elements (MEs). Being applied to the use case of a fastening bolt, two parameter sets, first for creating a component database and second selecting components to be used, are derived, demonstrating the applicability of the suggested methods. Finally, the parameter sets are validated by an exemplary component selection process, highlighting the potential of the concept as well as showing room for further research.
Subjects
Component parametrization
component selection process
design of cyber-physical systems
digitalization
microelectronical systems design
modular product architecture design
sensor-integrating machine elements (SiMEs)
DDC Class
004: Computer Sciences
621: Applied Physics