Sheared edge defect segmentation using a convolutional U-Net for quantified quality assessment of fine blanked workpieces

Abstract

Fine blanking allows for the economic mass production of sheet metal parts of high dimensional accuracy. The smooth cut section at the sheared edge is an important quality characteristic that is, however, reduced by the formation of tearings and fractures in the cut-off zone. At present, the smooth cut section cannot be assessed immediately as no capable methods for a 100% inline quality control of sheared edges for fine blanking exist, causing high scrap rates. To address this deficit this work proposes U-Nets to segment tearings and measure the cut-off height at sheared edges. A systematic model search together with a transfer learning strategy is conducted based on a dataset of 80 images of sheared edges of fine blanked reference workpieces. After a hyperparameter optimization, trained models are able to measure the cut-off height on the test dataset with a mean relative error of 1.1% and a mean absolute error of <1 px corresponding to an average measurement error of 7.5 μm. Besides the natural ability to incorporate additional disturbances in a real-world application, investigations of the reproducibility and the impact of the dataset size indicate that the proposed U-Net model is easy to set up and achieves a comparable average performance of 1.14 ± 0.31 px (17.1 ± 3.7 μm) even for non-optimal model initialization and small training datasets. For this reason, the shown approach is a promising solution for an inline machine vision system.