Fähigkeit von Bluetooth Low Energy zur signalstärkebasierten Rückverfolgung von Bauteilen in der Einzel- und Kleinserienfertigung

• Ability of bluetooth low energy for signal strength-based tracing of components in single and small batch production

Elser, Hannes; Schmitt, Robert H. (Thesis advisor); Kampker, Achim (Thesis advisor)

1. Auflage. - Aachen : Apprimus Verlag (2022)
Book, Dissertation / PhD Thesis

In: Ergebnisse aus der Produktionstechnik 30/2022
Page(s)/Article-Nr.: xv, 120 Seiten : Illustrationen, Diagramme

Dissertation, RWTH Aachen University, 2021

Abstract

The overall objective of this thesis is to investigate the suitability of Bluetooth Low Energy as a technology for an indoor real-time locating system for tracing components in single and small batch production. To achieve this goal, an indoor real-time locating system based on Bluetooth Low Energy was first defined and prototypically implemented, considering the general conditions in single and small batch production and the state of the art. With the help of this prototype, the influencing variables on the system behavior were investigated by means of tests in the production and laboratory environment. Based on the findings, the performance of the indoor real-time locating system was determined in an extended test under industry-like conditions using established key figures. Therefore, the localization method of fingerprinting was applied using artificial neural networks. Within an industry-like environment, a component could be correctly assigned to a grid with an edge length of 1 m with a probability of 69,58 %. Furthermore, a component could be correctly located within a self-selected area, such as an assembly workstation, with a probability of 72,60 %. The prediction of an exact position within a cartesian coordinate system was possible with an average accuracy of 0,74 m. As a conclusion, it can be stated that Bluetooth Low Energy is basically suitable as a technology for an indoor real-time locating system for tracing components in single and small-batch production, if the accuracy requirement is in the range of a few meters. However, generating the required training data and training the artificial neural networks is a very time-consuming process. For a practical implementation in single and small batch production, this process still must be simplified significantly.

Institutions

• Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University [417200]
• Chair of Production Metrology and Quality Management [417510]