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. With this aim, an experimental application of an industrial robot unit in its own isolated environment was carried out using neural networks. In this paper, we describe one possible application of deep learning in an Industry 4.0 environment for robotic units. The image datasets required for learning were generated using data synthesis. There are significant benefits to the incorporation of this technology, as old machines can be smartened and made more efficient without additional costs. 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. The results for different scenarios are presented and an overview of similar research topics on neural networks is provided. A method for synthetizing datasets of any size is described in detail. Specifically, the working domain of a given robot unit, a possible solution to compatibility issues and the learning of neural networks from 3D CAD models with rendered images will be discussed.
The main goal of the project was to carry out a handling task using a color sensor and thermal sensor, as well as an industrial robotic unit. The selection by color made it possible to represent the sub-process of the production of a sample, where each color can be matched to the corresponding or rejected product. The function of the thermal sensor is to show the delay function for the pro-cess, which occurs when the product does not reach a point in the process at the wrong tempera-ture and needs cooling between the two workflows. The thermal sensor and color sensor were pro-grammed using the ATmega microprocessor, while the manipulator was controlled with industrial PLC. The completed project will serve educational purposes in training for students.
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