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Analysis of robustness and transferability in feature-based grinding burn detection

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Abstract

Grinding burn is a common problem in high-performance industrial manufacturing. Usually destructive (e.g., nital etching) or non-destructive (e.g., Barkhausen noise analysis) methods are used to detect these unwanted changes of the workpiece properties. In recent years, different investigations for the in-process monitoring of grinding burn are conducted in a research environment. One main drawback of most of these detection methods is the lack of robustness and transferability. Therefore, this study provides a new feature-based approach to detect thermal damages in external cylindrical rough grinding using machine learning. To evaluate the robustness properties of the learning algorithm, a large series of experiments is conducted comprising different process parameters and system variables such as workpiece materials, grain sizes and bonding types. Using the burn threshold diagram, a linear separation boundary for parts with and without thermal damage is identified for one process setup. Due to the missing generalization property of the burn threshold analysis, multiple machine learning models are trained and optimized according to three levels of generalization. After achieving an accuracy of more than \(98~\%\) for a constant process setup, the model is expanded to make predictions independently from the values of the system variables showing only a slightly reduced accuracy. In addition, the obtained model is also able to generalize to new values of the system variables by maintaining the high recall of the classification model.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Institute of Machine Tools and Manufacturing (IWF), ETH Zürich, Leonhardstrasse 21, Zurich, 8092 , Switzerland

    Emil Sauter & Konrad Wegener

  2. Corporate Research, Robert Bosch GmbH, Robert-Bosch-Campus 1, Renningen, 71272, Germany

    Emil Sauter & Marius Winter

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  1. Emil Sauter
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Sauter, E., Winter, M. & Wegener, K. Analysis of robustness and transferability in feature-based grinding burn detection. Int J Adv Manuf Technol 120, 2587–2602 (2022). https://doi.org/10.1007/s00170-022-08834-9

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