The results for different scenarios are presented and an overview of similar research topics on neural networks is provided.

As an area of application, we present the preparation of a robot unit which at the time it was originally produced and commissioned was not capable of using machine learning technology for object-detection purposes.

There are significant benefits to the incorporation of this technology, as old machines can be smartened and made more efficient without additional costs. The image datasets required for learning were generated using data synthesis. In this paper, we describe one possible application of deep learning in an Industry 4.0 environment for robotic units. With this aim, an experimental application of an industrial robot unit in its own isolated environment was carried out using neural networks. In the spirit of innovation, the development of an intelligent robot system incorporating the basic principles of Industry 4.0 was one of the objectives of this study. The short implementation time of the application, its many features, and the 3D models created by the study participants set an example of what can be achieved with open source software and off-the-shelf hardware. We also examined the participants' beliefs about future use of immersive technology in 3D modeling. The study participants thought that the application was fun and intuitive to use, but accurate posing of objects was difficult. We conducted a basic user study where novice and professional 3D artists created 3D models with our application. Our goal was to create an easy to use 3D modeling environment that employs both 2D and 3D interaction techniques and contains several modeling tools.

The application was built in three weeks on top of Blender, an open source 3D modeling software, and relies solely on affordable, off-the-shelf hardware like PlayStation Move controllers. We present an immersive 3D modeling application with stereoscopic graphics, head tracking, and 3D input devices.

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