Refine
Document Type
Publication reviewed
- begutachtet (12)
Keywords
- EffPro (2)
- CMOS sensors (1)
- Confocal Metrology (1)
- Light sources (1)
- Microlens array (1)
- Optical filters (1)
- Tunable filters (1)
- automatic labelling (1)
- cast parts (1)
- classification (1)
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.
Um eine hohe Qualität und Zuverlässigkeit der gefertigten Produkte in einer Gießerei sicherzustellen, muss innerhalb der Serienprozesse eine zuverlässige Identifizierung eines jeden Gussteiles und die Rückverfolgbarkeit dessen Produktionsparameter möglich sein. Dabei stellen die oft rauen Umgebungsbedingungen sehr hohe Anforderungen an die Robustheit der Markierungseinheit und die Genauigkeit der zugehörigen Ausleseeinheit. Gleichzeitig müssen eine hohe Anzahl an individuellen Markierungsmöglichkeiten bei geringem Flächenbedarf realisiert werden. An der Hochschule Kempten wird im Forschungsprojekt CastCode in Kooperation mit Partnern aus der Gießereibranche ein ganzheitliches Markierungs- und Auslesekonzept entwickelt, welches diesen hohen Anforderungen genügen soll. Dabei wird die Markierung bereits im Formkasten durchgeführt,sodass sie direkt nach dem Abguss auf der Gussteiloberfläche abgebildet ist und ausgelesen werden kann.
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.
Punching is a wide-spread production process, applied when massive amounts of the ever-same cheap parts are needed. The punching process is sensitive to a multitude of parameters. Unfortunately, the precise dependencies are often unknown. A prerequisite for optimal, reproducible and transparent process alignment is the knowledge of how exactly parameters influence the quality of a punching part, which in turn requires a quantitative description of the quality of a part. We developed an optical inline monitoring system, which consists of a combined imaging and triangulation sensor as well as subsequent image processing. We show that it is possible to capture images of the cutting surface for every part within production. We automatically derive quality parameters using the example of the burnish height from 2D images. In addition, the 3D parameters are calculated and verified from the triangulation images. As an application, we show that the status of tool wear can be inferred by monitoring the burnish height, with immediate consequences for predictive maintenance. Although limited by slow images processing in our prototype, we conclude that connecting machine and process parameters with quality metrics in real time for every single part enables data-driven process modelling and ultimately the implementation of intelligent punching machines.
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.
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.
Increasing miniaturization requires improved and highly miniaturized optical 3D metrology systems. In this paper a basic measurement principle and a proposed optical design of a highly miniaturized endoscopic spatial confocal point distance sensor are presented. The sensor uses a, to our knowledge new technique called spatial confocal point distance measurement. A special feature of the proposed sensor design is the high degree of miniaturization, through femtosecond direct laser writing and the use of optical fiber bundles, which enable an
endoscopic application.
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.
We present a highly miniaturized endoscopic point distance sensor based on a spatial confocal measurement principle. The sensor uses a new technique called spatial confocal point distance measurement. A special feature of the proposed sensor design is the high degree of miniaturization through femtosecond direct laser writing and the use of optical fiber bundles, which enable an endoscopic application. We show the complete sensor measurement principle, sensor head design, experimental setup, and experimental results.
Chromatic confocal metrology is a widely established optical metrology technique, that allows for non-contact high-speed three-dimensional surface profiling without the need of mechanical depth scanning. However current methods are limited by the use of some sort of surface scanning method with mechanically moving parts. Furthermore the setups involve a spectrometer setup, either through prisms, gratings or multi-spectral cameras. This drastically limits the simultaneously measureable positions in lateral direction, as the spectrometer setup
will utilize one spatial dimension for the wavelength domain. We present a novel method for chromatic confocal metrology, that enables high-speed and high resolution one-shot aerial surface metrology. This method is scalable with respect to measurement range in axial as well as in lateral direction and in the number of measurement points that can be measured simultaneously. After deriving the theoretical basis of the approach a virtual optical design with a FOV of 10mm by 10mm and a depth range of 1.5mm with roughly 1000 measurement points, based mainly on off-the-shelf components will be presented. This virtual system design was used to perform various simulations and explain the design process and considerations as well as the expected system response of the proposed system.
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.
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.