International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

This paper presents a comprehensive concept for the control system of a four-wheeled omnidirectional mobile platform equipped with mecanum wheels, intended for industrial applications under the Industry 4.0 paradigm. The platform is modeled both kinematically and dynamically, with a nonlinear rigid-body formulation that incorporates Coriolis effects and rolling resistance. Particular attention is paid to the challenges arising from strong coupling between translational and rotational motion. To overcome the limitations of conventional control methods in complex and dynamic environments, a reinforcement learning strategy based on an actor–critic architecture is proposed. The agent is trained in a virtual warehouse scenario using simulated lidar data as sensory input, allowing it to learn effective policies for collision-free navigation. The continuous action space is mapped to wheel angular velocities through scaled hyperbolic tangent activations, enabling direct and fine-grained control of the platform. The proposed control system is designed for modularity, robustness, and scalability, making it a promising candidate for autonomous logistics and adaptive robotic applications in smart manufacturing environments.

The Industry 4.0 revolution has significantly influenced the control of non-linear Multiple Input Multiple Output (MIMO) systems, particularly through the application of artificial neural networks (ANNs). This paper explores current trends in the control of non-linear MIMO objects, emphasizing the role of ANNs in enhancing performance and efficiency. Key developments, methodologies, and case studies are reviewed to illustrate the impact of ANNs on non-linear MIMO control

Industry 4.0 and the rise of predictive maintenance have led to increased interest in developing efficient and accurate methods for detecting tool wear in manufacturing processes. In this work, we investigate the use of machine learning techniques, specifically Long Short- Term Memory (LSTM) networks, for the detection of tool wear using NASA Milling Data Set. We first preprocess the raw milling data and extract relevant features using signal processing techniques. Then, we train and evaluate the performance of the LSTM network for detecting tool wear in the milling process. Our results demonstrate the effectiveness of the LSTM network for detecting tool wear, achieving high accuracy and recall scores. Additionally, we compare our results to those obtained using traditional statistical process control methods and highlight the advantages of using machine learning techniques for tool wear detection in the context of Industry 4.0 and predictive maintenance. Our work provides a practical example of how machine learning techniques can be applied to manufacturing processes to improve efficiency and reduce downtime.