FIRST STEPS TOWARD LEVERAGING ARTIFICIAL INTELLIGENCE FOR PRECISE CHARACTERISATION OF FORCE TRANSDUCERS |
|---|
| D. Mirian, R. Kumme, R. Tutsch |
- Abstract:
- This work is dedicated to the demonstration of a dynamic force measurement system for precise characterisation of the force transducers. The rocking motion of the system as a main dominant source of uncertainty in the acceleration is investigated. We propose a novel method based on the application of an artificial neural network for evaluation of the data as an alternative to traditional approaches to get low-uncertainty calibration measurements. In the end, two special architectures of the artificial neural network, namely Long Short-Term Memory LSTM and Gated Recurrent Network GRU are introduced, and their appropriateness for our use case is discussed.
- Keywords:
- dynamic force calibration; rocking motion; measurement uncertainty; machine learning; deep learning; recurrent neural networks
- Download:
- IMEKO-TC3-2022-075.pdf
- DOI:
- 10.21014/tc3-2022.075
- Event details
- IMEKO TC:
- TC3
- Event name:
- TC3 Conference 2022
- Title:
24th Conference on the Measurement of Force, Mass and Torque (together with the 14th TC5 Conference on the Measurement of Hardness, the 6th TC16 Conference on Pressure and Vacuum Measurement, and the 5th TC22 Conference on Vibration Measurement)
- Place:
- Cavtat-Dubrovnik, CROATIA
- Time:
- 11 October 2022 - 13 October 2022