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Uncertainty is unavoidable in forming processes due to fluctuating properties in the semi-finished product, the tool system and the environment. For this reason, numerous scientists have addressed this issue by developing control approaches like self-optimizing machine tools or the control of product properties. Machine learning algorithms, in particular reinforcement learning (RL) methods, show promising results for controlling production processes in this way. In this paper, the application of RL is demonstrated on an industrially commonly used process, V-die bending. For this purpose, first a flexible tool system is developed that allows the bending angle to be adjusted continuously between 80 and 110°. The developed tool is initially simulated through an FEM model in order to create a sufficient database for the training of an RL agent for springback compensation. The pre-trained agent is then used to control the springback in the real process. To close the resulting sim-to-real gap, it is then retrained on the experimentally generated data. It is shown that the springback can be significantly reduced compared to the uncontrolled case in both the simulative and the experimental process.

Wear is one of the key factors that determine the efficiency of multi-stage processes that include blanking operations. Since wear in these processes not only causes unplanned downtime but also directly affects product quality, inline detection of wear and its effect on product quality is of major importance. However, current quality assurance (QA) methods are limited to manual offline inspection by operators at predefined intervals, so that 100% inspection of the product and description of the state of wear is not found in industrial practice. The aim of this work is therefore to develop an optical system that enables in-line acquisition of product images and the associated control of blanking-specific quality features up to stroke rates of 300 strokes per minute (spm). In order to make the system attractive to small- and medium-sized enterprises (SME), the system is designed to minimize integration and investment costs using commercially available components. By combining the system with a methodology for extracting blanking-specific features, so-called key performance parameters (KPPs), the condition of the blanked surface as a relevant quality parameter is derived directly from the workpiece image. To demonstrate the transferability of the methodology to industrial applications, two use cases are investigated. In the first case, the KPPs are used directly to determine the quality of the blanked workpiece and are compared with reference measurements. Here, the KPPs are quantified with a mean absolute error of 18 μm compared to a ground truth. In the second case, the KPPs are used to build a machine learning (ML) model to estimate the wear of the blanking tool. Here, an accuracy of 92% is achieved in classifying the actual wear state.

Closed-loop control of product properties is becoming increasingly important in forming technology research and enables users to counteract unavoidable uncertainties in semi-finished product properties and process environments. Therefore, closed-loop controlled forming processes are considered to have the potential to reduce tolerances on desired product properties, resulting in consistent qualities. The achievement of associated increases in robustness and reliability is linked to enormous requirements, which in particular include the inline recording of the product properties to be controlled and the subsequent adaptation of the process control through the targeted derivation of manipulated variables. The present paper uses the example of an air bending process to show how the bending angle can be controlled camera-based and how springback can be compensated within a stroke by recording force signals and subsequently predicting the loaded bending angle using machine learning algorithms. The results show that the combined application of camera-based control and machine learning assisted springback compensation leads to highly accurate bending angles, whereby the results strongly depend on the machine learning algorithms and associated data transformation processes used.

The extend of tool wear significantly affects blanking processes and has a decisive impact on product quality and productivity. For this reason, numerous scientists have addressed their research to wear monitoring systems in order to identify or even predict critical wear at an early stage. Existing approaches are mainly based on indirect monitoring using time series, which are used to detect critical wear states via thresholds or machine learning models. Nevertheless, differentiation between types of wear phenomena affecting the tool during blanking as well as quantification of worn surfaces is still limited in practice. While time series data provides partial insights into wear occurrence and evolution, direct monitoring techniques utilizing image data offer a more comprehensive perspective and increased robustness when dealing with varying process parameters. However, acquiring and processing this data in real-time is challenging. In particular, high dynamics combined with increasing strokes rates as well as the high dimensionality of image data have so far prevented the development of direct image-based monitoring systems. For this reason, this paper demonstrates how high-resolution images of tools at 600 spm can be captured and subsequently processed using semantic segmentation deep learning algorithms, more precisely Fully Convolutional Networks (FCN). 125,000 images of the tool are taken from successive strokes, and microscope images are captured to investigate the worn surfaces. Based on findings from the microscope images, selected images are labeled pixel by pixel according to their wear condition and used to train a FCN (U-Net).

Blanking processes belong to the most widely used manufacturing techniques due to their economic efficiency. Their economic viability depends to a large extent on the resulting product quality and the associated customer satisfaction as well as on possible downtimes. In particular, the occurrence of increased tool wear reduces the product quality and leads to downtimes, which is why considerable research has been carried out in recent years with regard to wear detection. While processes have widely been monitored based on force and acceleration signals, a new approach is pursued in this paper. Blanked workpieces manufactured by punches with 16 different wear states are photographed and then used as inputs for Deep Convolutional Neural Networks to classify wear states. The results show that wear states can be predicted with surprisingly high accuracy, opening up new possibilities and research opportunities for tool wear monitoring of blanking processes.