Deep learning-based domain adaptation for a generalized detection of wear phenomena during blanking

Abstract

Blanking plays an important role in most manufacturing chains as this forming operation defines the final geometry as well as functional properties of the product. As a result of high production rates combined with excessive mechanical and dynamic loads, wear is an unavoidable phenomenon in such processes and directly worsens their economic efficiency. Although wear plays this crucial role, its detection is mainly limited to the manual control or inline binary fault detection. In this context, data-driven approaches such as conventional machine or deep learning allow to overcome this challenge by an inline wear state estimation. Unfortunately, their application in practice is limited since changes to the system configuration significantly degrade the performance of the implemented data-driven model. To address this problem, an adapted deep learning-based domain adaptation (DA) technique is proposed in this paper. With this new procedure, sensorial acquired force singles are combined with a deep network architecture to enable an inline detection of finely graded wear states in a blanking process, even if there has been a change in system configuration. As a result, high accuracies for classifying the 11 wear states are achieved without retraining the underlying models, in case of a distribution discrepancy between source and target domain. In contrast to no DA technique as well as a conventional DA technique applied to transfer tasks, the proposed method improves the classification accuracy in the target domain up to 95%. Validation of this approach based on different real-world data sets demonstrates its suitability for a generalized inline wear state estimation, even under varying process boundaries.