Università degli studi di Modena e Reggio Emilia

Industry 4.0 has driven the development of important technologies for industrial applications, but the focus has often been on technological advancement rather than on how operators interact with these systems. With the emergence of Industry 5.0, attention has shifted toward the role of the operators and their interaction with emerging technologies. This paper explores the automation of a fully manual operation in the logistics field while adopting a human-centered approach to reduce risky tasks and enhance the operator’s well-being. A motion capture system and digital human simulation software are utilized to create a digital twin of a real-world industrial case study. This approach enables the virtual testing of various automation solutions to identify the optimal scenario that meets the performance indicator parameters. This study highlights the importance of integrating ergonomic considerations into automation strategies.

This paper proposes a software architecture for monitoring and diagnostics of failures in industrial systems. The architecture aims to support the operator's decision-making process by enabling a real-time and intuitive understanding of system faults. The paper describes the methodology and implementation process applied to a real industrial case: a welding collaborative robotic application. However, the proposed software architecture can be easily extended to a broader number of industrial systems. The core of the idea is based on an ecosystem of Internet of Things (IoT) elements deployed in the automation systems that collect the system status and alarms to stream them to the cloud server. The industrial use case described in the paper is a collaborative robot-assisted welding solution for automated MIG/MAG welding produced by 'Indus-tria Tecnologica Italiana', an Italian SME company, with the brand name'MyWelder'. We investigated the system's impact on the operator's work and its effectiveness in supporting his/her decision-making process. Additionally, the validation process assessed the system's functionalities within this specific use case. The primary objective related to the use case is to establish a strategy that minimizes the production of defective parts, ultimately reducing waste.

This article describes a methodology for the diagnosis of failures in multi-AGV (Automatic Guided Vehicles). Today, AGVs are establishing themselves in the most advanced automatic logistics solutions, providing performance and safety that cannot be achieved with handling solutions with manual forklifts. Furthermore, thanks to the application of Industry 4.0 digital technologies, very advanced tools are available to monitor the performance and diagnose faults of fleets of AGV. In particular, studies on fault diagnosis have mainly focused on (1) the diagnosis of internal components of the automatic truck and (2) the identification of failures in the functionality of the AGV in its interaction with the surrounding environment. This paper shows an approach to fault diagnosis in multi-AGVs system, considering the interaction between each single AGV and the environment, with the scope to help the user increase the system efficiency in an existing layout. The objective of the paper is to introduce and discuss a methodology to study the failure and the available recovery actions of the AGV navigation system. Moreover, the paper presents the real AGV data acquisition and processing architecture actually deployed on the factory shop floor, as well as the result from the experimental study in a real industrial environment. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

This paper is focused on Wireless Sensor Network (WSN) leveraging on Bluetooth Low Energy (BLE) connectivity for low energy applications which is fault tolerant versus communication path failures. The topic is important to create a robust sensorized environment to be applied in industrial context or smart infrastructure to enable scheduled monitoring with low power consumption applications. Currently BLE applications are mainly thought for smart home solutions, health care and positioning systems. In those applications the BLE nodes are continuously supplied by external power suppliers. Our goal is to design a self-configuring network with a synchronized deep sleep behavior, aimed to optimize the energy consumption, with an overall active time interval constraint optimized with a data-driven method. The aim is to find a tradeoff between the on time and the ability to collect all the nodes data, pursuing a low power consumption. Our research is based on BLE protocols, interaction between edge systems for data collection and cloud system for data analysis and software agent optimization system. The paper analyses different configurations and describes the possible optimization algorithm to be used for the software agent design, in order to reach a fine-tuned control to improve the fault tolerance and fault diagnosis of the system. Finally experimental results are compared with the estimates obtained via a software simulation tool implemented for this architectural pattern.