Proceedings of the

The 33rd European Safety and Reliability Conference (ESREL 2023)
3 – 8 September 2023, Southampton, UK

Uncertainty Quantification of Different Data Sources with Regard to a LSTM Analysis of Grinded Surfaces

Marcin Hinz1, Jannis Pietruschka2,a and Stefan Bracke2,b

1Chair of Artificial Intelligence in Mechanical Engineering, Department of Mechanical, Automotive and Aeronautical Engineering, Munich University of Applied Sciences, Germany.

2Chair of Reliability Engineering and Risk Analytics, Faculty of Mechanical and Safety Engineering, University of Wuppertal, Germany.


To improve the conventional methods of condition monitoring, a new image processing analysis approach is needed to get a faster and more cost-effective analysis of produced surfaces. For this reason, different optical techniques based on image analysis have been developed over the past years.

In this study, fine grinded surface images have been generated under constant boundary conditions in a test rig built up in a lab. The gathered image material in combination with the classical measured surface topography values is used as the training data for machine learning analyses. The image of each grinded surface is analyzed regarding its measured arithmetic average roughness value (Ra) by the use of Recurrent Neural Networks (in this case LSTM).

LSTMs are a type of machine learning algorithms which can particularly be applied for any kind of analysis based on time series. In this paper a possible optimization potential of the available databases is analyzed. For this purpose, two different sets of images with various resolutions were taken under the same conditions. Since the data plays an essential role for the training of machine learning models, the challenge in the application is often to find costefficient, fast and at the same time process-adaptable measurement methods that also have sufficient accuracy. Thus, the target values recorded with tactile measurement method are compared to a more precise confocal / optical measurement method. This results in two data sets with unequal distributions and different statistical variance.

Keywords: Machine learning, Neural networks, LSTM, Uncertainty quantification, Konfocal measurements.

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