State-of-the-art style-based generative adversarial networks (StyleGANs) synthesize high-quality images by learning a mapping from a disentangled latent space onto the image manifold. Thereby, learned representations can be analyzed by interpreting the latent space and used subsequently to control the properties of the synthesized industrial machine vision data. StyleGANs in combination with an embedding into the latent space enable the assessment of the properties of embedded images by means of their latent space representations, however, a trade-off between the dimensionality of the StyleGAN’s latent space and the quality of generated images must be found. While a smaller latent space is easier to interpret, it might not capture all quality characteristics if lossless compression cannot be achieved. This work presents an evaluation scheme that uses statistical hypothesis testing to identify an advantageous dimensionality of the latent space for industrial machine vision applications. As quality measure of images synthesized by GANs, often the Fr´echet Inception distance (FID) based on features learned from the ImageNet dataset is used. However, the features of the underlying Inception network are opaque and might not be representative for application specific quality characteristics. Herein, synthetic data is evaluated instead by means of a Fr´echet distance based on selected and application specific features extracted from the used industrial machine vision dataset. With these application specific features, the image quality of multiple StyleGANs trained with different latent space dimensionalities is compared using statistical tests to select an advantageous latent space dimension.
KEYWORDS: 3D metrology, Data modeling, Control systems, 3D modeling, Data processing, Data storage, Visualization, Additive manufacturing, Data fusion, 3D optical data storage
Quality assurance of complex parts, produced by elaborate process chains such as in industrialized additive manufacturing, requires the repeated digitization of products before and after each process step in order to detect any deviation from the planned geometry. This is necessary to enable the efficient production of small lot sizes in smart factories by counteracting those deviations through adaptive process control of downstream processes. The digitization of complex parts during production is often only feasible through a combination of different geometrical measurement technologies, e.g. CMM, xCT or optical 3D scanners, resulting in convoluted data sets consisting of incompatible measurement data from different points in time. In order to effectively utilize the generated data to its full potential, it is necessary to link related data while minimizing redundancy. For this purpose, the “Geometrical Digital Shadow” is proposed as a framework, which provides a way to condense all acquired geometrical data related to a physical object into a single source of truth. This work presents a methodology for a reversible fusion of geometrical data in form of meshes from different measurement technologies but also from different production steps along the process chain into a single evolving 3D model. By calculating and only saving the differences between the existing mesh and a newly generated measurement, just the relevant data is taken into account for further processing. The resulting 3D model encapsulates the data and origin of multiple measurements while reducing the overall data footprint and therefore offers the envisioned increase in information density.
Metrology assisted assembly systems constitute cyber physical production systems relying on in-process sensor data as input to model-based control loops. These range from local, physical control loops, e.g. for robots to closed-loop product lifecycles including quality management. The variety and amount of involved sensors, actors and data sources require a distinct infrastructure to ensure efficient, reliable and secure implementation. Within the paradigm of Internet of Production a reference architecture for such an infrastructure is established by four layers: Raw data (1), provisioning of proprietary systems (2), data aggregation and brokering (3) and decision support (4). In modern metrology assisted assembly systems, a virtual reference frame is constituted by one or multiple predominantly optical Large-Scale Metrology instruments, e.g. laser trackers, indoor GPS or multilateration based on ultra wideband communication. An economically efficient implementation of the reference frame can be achieved using cooperative data fusion, both by increasing the operative volume with existing systems and by optimizing the utilization of highly precise and therefor typically cost-intensive instruments. Herewith a harmonization is required as well from a physical perspective as in terms of communication interfaces to the raw data provided by the individual instruments. The authors propose a model-based approach to obtain a protocol-agnostic interface description, viewing a Large-Scale Metrology instrument as an abstract object oriented system consisting of one or multiple base units and mobile entities. Its object-oriented structure allows a realization of the interface in arbitrary structured communication protocols by adhering to fixed data transformation schemes. This approach is evaluated for MQTT (structured by topics), OPC UA (structured by data model) and HTTP/REST (structured by URLs) as key protocols within the internet of things. Moreover, the transformation between different protocols decouples software requirements of measurement instruments and actors, generally allowing a more efficient integration into cyberphysical production systems.
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