Industry 4.0 has become one of the most dominant research areas in industrial science
today. Many industrial machinery units do not have modern standards that allow for the use …

The use of synthetic data is a promising solution to the problem of the availability of real data
needed for the development of robotic systems. However, the precision of the systems …

As the world moves towards mass customization, there is a need for a manufacturing system
that can quickly adapt to market changes. Reconfigurable manufacturing systems (RMS) …

Deep learning-based methods have shown excellent potential on object detection and pose
estimation with vast amounts of training data to achieve good performance. Obtaining …

The image-to-image translation is one of the deep learning applications using image data. In
this paper, we aim at improving the performance of object transfiguration which transforms a …

Factories that prioritize digital transformation experience heightened production rates and
gain a competitive advantage by enhancing control and operational efficiency. In this study …

The usefulness of deep learning models in robotics is largely dependent on the availability
of training data. Manual annotation of training data is often infeasible. Synthetic data is a …

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 …

In image classification tasks, the training of deep convolutional neural networks generally
requires a large amount of data, and due to the constraints of environment, resources and …

Synthetic data for training deep neural networks is increasingly used in computer vision.
Several strategies, such as domain randomization or domain adaptation (sim2real), exist to …