In-situ material classification in sheet-metal blanking using deep convolutional neural networks

Sheet-metal blanking is a class of metal fabricating processes that separate a metal workpiece from a primary metal sheet through a shearing process. Industry experts observe fluctuations in tool life and product quality, which is associated with fluctuations in microstructural parameters between and along material coils. With a methodology, that provides reliable information about the current material state in situ through a non-destructive testing method, process parameters, such as forces, speed, and lubrication could be adapted accordingly. This would enhance the productivity of sheet-metal blanking processes or other sheet-metal forming processes with a high process adaptability. The findings of this paper suggest that fluctuations of microstructural parameters along a material coil follow a pattern that is detectable for ferromagnetic materials using magnetic barkhausen noise emission. The preprocessed signal of 18 specimens stemming from six different coils have been encoded to recurrence plots and classified with a deep convolutional neural network with regard to the position on the coil that the respective specimen stem from. The neural network was able to reliably distinct between the recurrence plot of a specimen stemming from the beginning, the middle, or the end of a material coil. Through an advanced labelling approach and classifying for specific microstructural parameters, the same methodology could possibly be used to detect certain material states and quality deficits and to adjust the sheet-metal blanking process accordingly to enhance tool life and product quality.