A time series classification approach to non-destructive hardness testing using magnetic Barkhausen noise emission

Abstract

The process setup of manufacturing processes is generally knowledge-based and carried out once for a material batch. Industry experts observe fluctuations in product quality and tool life, albeit the process setup remains unchanged. These fluctuations are mainly attributed to fluctuations in material parameters. An in-situ detection of changes in material parameters would enable manufacturers to adapt process parameters like forces or lubrication before turbulences like unexpectedly high tool wear or degradation in product quality occurs. This contribution shows the applicability of a deep learning time series classification architecture that does not rely on handcrafted feature engineering for the classification of hardness fluctuations in a sheet-metal coil using magnetic Barkhausen noise emission. This methodology is not limited to the detection of hardness fluctuations in sheet-metal coils and can potentially be applied for the in-situ material property classification in different manufacturing processes and for different material parameters.

Materials in the Drive Chain – Modeling Materials for the Internet of Production

“…Experiments with an adapted version of Li et al's model architecture were conducted with the aim to classify the hardness of specimen from a 16MnCr15 fine blanking steel based on MBN measurements. Figure 4 The reached validation accuracies are considerably lower than those of Unterberg et al (2021). However, it becomes possible to compare the neural network's decision logic to existing domain knowledge, by checking whether the prototypes are consistent with findings reported in the literature on MBN.…”

“…These variations occur on batch level, but also along single sheet metal coils. For other sheet-metal processing manufacturing processes, research has already shown that deviations in product quality occur due to variations in material properties (Unterberg et al 2021). A fine blanking line was equipped with sensors to capture material, process, and quality data to allow for a data-driven modeling of dependencies between material state, process state, and the resulting product quality in the long term (Niemietz et al 2020).…”

“…The measurement of MBN is even fast enough to be applied in production lines (Franco et al 2013). Unterberg et al (2021) conducted experiments on deep learning to classify the hardness of specimen from a 16MnCr5 (AISI: 5115) sheet-metal coil used for fine blanking based on MBN signals. They demonstrate that deep learning models, more precisely InceptionTime (Ismail Fawaz et al 2020), allow to distinguish different classes of hardness.…”