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Especially in manufacturing systems with small batches or customized products, as well as in remanufacturing and recycling facilities, there is a wide variety of part types that may be previously unseen. It is crucial to accurately identify these parts based on their type for traceability or sorting purposes. One approach that has shown promising results for this task is deep learning–based image classification, which can classify a part based on its visual appearance in camera images. However, this approach relies on large labeled datasets of real-world images, which can be challenging to obtain, especially for parts manufactured for the first time or whose appearance is unknown. To overcome this challenge, we propose generating highly realistic synthetic images based on photo-realistically rendered computer-aided design (CAD) data. Using this commonly available source, we aim to reduce the manual effort required for data generation and preparation and improve the classification performance of deep learning models using transfer learning. In this approach, we demonstrate the creation of a parametric rendering pipeline and show how it can be used to train models for a 30-class classification problem with typical engineering parts in an industrial use case. We also demonstrate how our method’s entropy gain improves the classification performance in various deep image classification models.

Abstract. Uncertainty in manufacturing processes is as old as the manufacturing process itself. Simulations on the other hand are always certain in their outcome based on the chosen parameters. Nevertheless it makes sense to incorporate uncertainties in the simulation for validation and analysis of the real and simulated processes. This paper aims on highlighting the importance of an accurate understanding and measurement of uncertainty for simulation validation and thus to increase the significance and acceptance of simulation results in the working environment.

The stunning success of deep image classification approaches relies heavily on huge, labeled datasets based on real-world images. Nonetheless, the latest generation of neural network architectures provides pre-trained models that allow the training and classification of new classes with only minor example data. In most application use cases, the prediction performance is still too low to meet the high standards of manufacturing systems, e.g., for visual part classification. Furthermore, it is often difficult to acquire sufficient image data of objects in manufacturing systems due to customized and small batch production. While data augmentation techniques like viewport transformations or artificial noise barely increase the data set's overall entropy, we aim to generate highly realistic synthetic images only based on photorealistic rendered CAD. Additionally, this approach can reduce manual effort in data generation and preparation.First, we demonstrate the creation of a parametric rendering pipeline. Then the models are trained for a 30-class classification problem with typical engineering parts from an industrial use case. Finally, we show how the entropy gain, using our proposed method, improves the classification performance in any of the investigated deep image classification models.

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