Layh, Michael
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Aligning a lens system relative to an imager is a critical challenge in camera manufacturing. While optimal alignment can be mathematically computed under ideal conditions, real-world deviations caused by manufacturing tolerances often render this approach impractical. Measuring these tolerances can be costly or even infeasible, and neglecting them may result in suboptimal alignments. We propose a reinforcement learning (RL) approach that learns exclusively in the pixel space of the sensor output, eliminating the need to develop expert-designed alignment concepts. We conduct an extensive benchmark study and show that our approach surpasses other methods in speed, precision, and robustness. We further introduce relign, a realistic, freely explorable, open-source simulation utilizing physically based rendering that models optical systems with non-deterministic manufacturing tolerances and noise in robotic alignment movement. It provides an interface to popular machine learning frameworks, enabling seamless experimentation and development. Our work highlights the potential of RL in a manufacturing environment to enhance efficiency of optical alignments while minimizing the need for manual intervention.
Synthetic Data Generation for AI-Based Quality Inspection of Laser Welds in Lithium-Ion Batteries
(2025)
Manufacturing companies are increasingly confronted with critical challenges such as a shortage of skilled labor, rising production costs, and ever-stricter quality requirements. These challenges become particularly acute when defect types exhibit high visual variance, making consistent and accurate inspection difficult. Traditionally, visual inspection of high variance errors is performed manually by human operators—a process that is both costly and prone to errors. Consequently, there is a growing interest in replacing human inspection with AI-based visual quality control systems. However, the adoption of such systems is often hindered by limited access to training data, labor-intensive labeling processes, or the absence of real production data during early development stages. To address these challenges, this paper presents a methodology for training AI models using synthetically generated image data. The synthetic images are created using Physically Based Rendering, which enables precise control over rendering parameters and facilitates automated labeling. This approach allows for a systematic analysis of parameter importance and bypasses the need for large real training datasets. As a case study, the focus is on the inspection of laser welds in battery connectors for fully electric vehicles—a particularly demanding application due to the criticality of each weld. The results demonstrates the effectiveness of synthetic data in training robust AI models, thereby providing a scalable and efficient alternative to traditional data acquisition and labeling methods. The trained binary classifier reaches a precision of 0.94 with a recall of 0.98 solely trained on synthetic data and tested on real image data.
Die individuelle Markierung von Gussteilen ermöglicht die direkte Zuordnung von Prozesseinflussgrößen aus Teilprozessen wie Formen, Schmelzen und Gießen zu beliebigen Qualitätskenngrößen. Sie bietet damit den Schlüssel zu einer datenbasierten Analyse der Wirkzusammenhänge zwischen Prozess und technischen, ökologischen und ökonomischen Eigenschaften eines Gussteils sowie einer darauf basierenden KI-gestützten Prozesskontrolle.
In an industrial context, AI-based methods are becoming increasingly important in the optical systems used for identification, inspection and classification. The reasons for this are that AI-based image processing algorithms are easy to use on the operator side and often achieve superior results. E.g. in complex classification tasks. In the sand cast industry, the complexity in optical inspection of cast parts is connected with strong variations in the local surface topography and in the global object geometry change. Despite the great potential of AI-based methods, application is often hindered by the immense effort involved in acquiring a suitable training dataset. This refers not only to the acquisition of the required number of images but also to the tedious labelling. In this work, we investigate the capabilities and limits of synthetic training data on an AI-based optical scanner used to identify and track cast parts. The optical scanner is capable of detecting and classifying a codification specifically designed for the casting industry. By reading the code, the scanner can deduce the specific number of the cast part. For synthetic image generation, we use physically based rendering, which has advantage of full control over all rendering parameters. This allows for both a systematic investigation of the importance of the parameters and, an automatic labelling process of the training datasets. Our results show that, in particular, a detailed geometric modelling of the local surface topography and global object geometry of the pins have a positive influence on the recognition rate of the neural network. With that accuracy rates up to 56 % are achieved using synthetic training datasets, only.
The spectrometer-free chromatic confocal measurement technique enables 3D surface measurements with just one exposure and without scanning. To reduce the need for a spectrometer for the spectral analysis of the reflected light composition and thus the extraction of the local surface height, an optical spectral analysis unit is used. This unit determines the first momentum of the spectral composition reflected from the surface under probe for a large number of lateral measurement points simultaneously. This work investigates the impact of the spectral composition and light power of the light source on the sensitivity and accuracy of this method. A thorough optimization of the light source will be conducted, demonstrating the impact of various spectral compositions and light source power on the system performance, taking into account the system-related etendue. In addition, the optimization of the spectral transmission filter used in the optical spectral analysis unit and its influence on the accuracy and sensitivity of the system over the entire measurement range is shown.
The spectrometer free areal chromatic confocal metrology (ChromaCAM) is an optical 3D surface measurement technology, which allows a simultaneous measurement of a large array of measuring points within a single exposure. In this work, we investigate the accuracy of a first prototype sensor system utilizing this new singleshot 3D measurement technique. It is found that surface height measurement errors smaller 1μm within a total measurement range of about 1000 μm are achievable. Furthermore, several influential factors are investigated showing the advantages and limits of the presented system. Investigating different surface materials it is found that frame rates up to approximately 800 fps for highly reflecting surfaces and up to 30 fps for ceramics, aluminum, and plastics are achievable.
Chromatic confocal metrology suffers from a limitation in the number of measurement points that can be measured simultaneously in a single frame acquisition. We propose chromatic confocal areal metrology (ChromaCAM), in which the surface height for each point in a 2D grid of measurement spots, generated by a rectangular micro-lens array, is parallely analyzed through the utilization of a pinhole multiplexer unit, an analog optical analysis unit, and postprocessing algorithms. An experiment shows the viability of the simultaneous acquisition of multiple measurement points and the advantages over exisiting areal chromatic confocal approaches. Compared with conventional chromatic confocal metrology, the increase in the acquisition rate is significant and enables one-shot measurements.
Zunächst wird die aktuelle Situation der Schnittflächenkenngrößenermittlung innerhalb des Stanzprozesses in der Industrie beleuchtet und den steigenden Qualitätsanforderungen sowie anwendungsindividualisierbaren Produkten/Prozessen gegenübergestellt.
Auf dieser Basis wird ein optisches Inline-System entwickelt. Dieses System ermöglicht die Aufzeichnung der Schnittflächenqualität in einer Prozessgeschwindigkeit von 250 Hub/min mittels Bildaufnahme. Zusätzlich werden über einen integrierten Triangulationssensor dreidimensionale Schnitte der Schnittkante aufgenommen. Eine entwickelte Bildverarbeitung ermittelt aus den Daten der Bildaufnahme automatisiert und reproduzierbar die Glattschnitthöhe. Die Ermittlung weiterer Schnittflächenkenngrößen ist denkbar, wobei dreidimensionale Daten aus dem Triangulationssensor entstehen. Zusätzlich wird die erreichte Genauigkeit der Systems validiert. Abschließend wird ein Ausblick gegeben, welcher im Besonderen dieses System als Grundlage für die Öffnung der Steuerung des Stanzprozesses mittels intelligenter Methoden sieht. Dies wird an einem Praxisbeispiel unterstrichten.
A novel tracking system for the iron foundry field based on deep convolutional neural networks
(2022)
In modern manufacturing the ability of retracing produced components is crucial for quality management and process optimization. Tracking is essential, especially for analyzing the influence of the production parameters on the final quality of the castings. In the iron foundry industry, common marking methods, such as a datamatrix code, cannot be used due to harsh environmental conditions and the rough surface of the cast parts. This work presents a new coding and reading system that guarantees unique marking in the casting process.The coding is built up over several beveled pins and is read out using an optical 2D handheld scanner. With a deep convolutional neural network approach of object detection and classification, a stable image processing algorithm is presented. With a first prototype a reading accuracy of 99.86% for each pin was achieved with an average scanning time of 0.43 s. The presented code is compatible with existing foundry processes, while the handheld scanner is intuitive and reliable. This allows immediate benefits for process optimization.
Purposefully induced axial chromatic aberration is the core of the chromatic confocal metrology technique.Through the resulting generation of separated focal planes for each wavelength of a broadband light sourcea measurement volume is created and a three-dimensional reconstruction of the topography of technical and biomedical surfaces and layers can be performed. Based on the chromatic confocal metrology technique various metrology sensors and measurement systems have been developed, with high axial and lateral resolution, accuracy and precision. For a significant increase in measurement points, that can be measured simultaneously and the resulting reduction in measurement time, a chromatic confocal method utilizing a micro-lens array in combination with a improved spectral peak detection, has been developed. Through a single image acquisition, the object topography can be measured for multiple points simultaneously and therefore mitigating the need for axial aswell as lateral scanning of the object. For this reason in-situ applications have become a viable domain. First preliminary results of testing a laboratory setup of the proposed system design are presented.
