Comparative Study on Different Methods to Generate Synthetic Data for the Classification of THT Solder Joints - Thielen, Nils; Rachinger, Ben; Schröder, Felix; Preitschaft, Anja; Meier, Sven; Seidel, Reinhardt; Reinhardt, Andreas; Franke, Jörg (2024 1st International Conference on Production Technologies and Systems for E-Mobility (EPTS), 2024)
Automated Optical Inspection (AOI) is still one of the major tools for the quality control of solder joints; especially, if the requirements for the solder joints' mechanical and electrical properties are high. This is the case for products in the automotive sector, e.g. air bags or brake systems. Conventional test routines are time-consuming to define and not flexible regarding quality limits and lead to a high amount of manual reinspection. Thus, Artificial Intelligence offers the potential to address these disadvantages. Artificial Intelligence, on the other hand, suffers from highly imbalanced datasets resulting from the low amount of defects on solder joint level. One possibility to face this challenge is the utilization of synthetic data. This work compares different methods to generate data using both conventional data augmentation and deep generative models as well as virtual world rendering in order to enhance dataset quality in general. These are applied to an industrial dataset of THT solder joints. All tested approaches lead to an improvement in model quality. The best results are achieved with synthetic images from a Generative Adversarial Network, relatively increasing performance by around 6 % while minimizing error slip.<br
Keywords: Safety devices; Production; Quality control; Optical imaging; Rendering (computer graphics); Adaptive optics; Mechanical factors; Synthetic Data; Artificial Intelligence; Automated Optical Inspection; Electronics Production

Design and Validation of an Inline Curvature Measurement for Flat Wire Straightening in Hairpin Stator Production - Preitschaft, Anja; Baader, Marcel; Mayr, Andreas; Bader, Thomas; Franke, Jörg; Kühl, Alexander (2024 14th International Electric Drives Production Conference (EDPC), 2024)
This paper presents a novel inline sensor-based method for measuring the curvature of straightened flat wires during hairpin stator production. The accurate control of the wire curvature is essential to ensure precise alignment in subsequent assembly steps. Existing methods for curvature assessment often rely on an external measurement in dedicated metrology stations or subjective visual inspection, both of which have significant limitations in terms of accuracy, consistency and time efficiency. The inline method proposed in this work offers a promising alternative that enables continuous, high-precision curvature measurement immediately after the straightening process. To evaluate the feasibility of the system, an experimental study is conducted in which straightened wire samples are analyzed using both the developed inline sensor system and an offline 3D optical profilometer as a reference. The results are then compared to determine the suitability and the accuracy of the inline measurement technique for industrial applications. The results indicate that the inline system provides reliable curvature measurements, demonstrating its potential to significantly improve quality control, reduce scrap and increase the overall efficiency of wire straightening processes in electric motor productio
Keywords: electric motor production; hairpin technology; straightening process; curvature measurement

Insulation Thickness Measurement in Hairpin Stator Production Using a Confocal Chromatic Line Sensor - Preitschaft, Anja; Baader, Marcel; Wagner, Gerald; Franke, Jörg; Kühl, Alexander (Proceedings of the Conference on Production Systems and Logistics: CPSL 2025, 2025)
The insulation thickness of enameled flat wire is a critical parameter in electric motor production based on hairpin technology, directly affecting key manufacturing processes such as straightening, bending, stripping and welding. Adapting these processes based on accurate insulation measurements can significantly improve production efficiency and reduce scrap. This paper presents an inline confocal chromatic line sensor designed to measure the insulation thickness of polyamide-imide (PAI) coated flat wires. Unlike conventional point-based sensors, this advanced system measures a 1.8 mm line, capturing insulation thickness data over a wider range and providing comprehensive insight into wire properties. To investigate the applicability of this line sensor for the use case at hand, the measurement system is integrated into a hairpin production line before the straightening process, providing real-time measurements of the insulation thickness of the raw material prior to further processing. The sensor is first calibrated to determine its optimum operating range. A series of inline measurements are then taken and microsections of the measured wire samples are analyzed using a laser microscope to obtain ground truth values. The comparison between the inline and the offline measurements shows high accuracy, confirming the feasibility and reliability of the sensor for the application at hand. This study highlights the potential of confocal chromatic line sensors as a powerful tool for inline insulation thickness measurement, offering opportunities to optimize electric motor production processes such as straightening, bending, stripping and welding. The integration of such sensors into hairpin stator production lines represents a significant step towards data-driven process optimization, improving process quality, sustainability and efficienc
Keywords: Electric Motor Production; Hairpin Technology; Enameled Copper Wire; Insulation Thickness; Confocal Chromatic Sensor