The concept of Industry 4.0 originates from the will to introduce the benefits of digital computation into new and existing industrial plants to save time, materials and energy. The digital transformation requires that all machinery of the production line are connected together and with the enterprise applications, to capture and analyze data across all manufacturing stages. Then, such collected data can be exploited to take strategic decision on the production and to monitor it, reacting to unexpected behaviors and thus reducing downtime and maintenance costs. This article aims at supporting production engineers approaching digital transformation by exemplifying its key elements on a real life scenario, the Industrial Computer Engineering laboratory of the University of Verona. First of all, the article discusses network design, as communication is an enabler of the other technologies. Network is realized through automatic network synthesis from requirements and characteristics of the production line data flow. Then, the paper discusses data collection and the construction of a digital twin monitoring power consumption of the production line, with the goal of detecting any discrepancy between real time data and digital twin data. This allows to trigger an early intervention on the line, to guarantee an effective maintenance.

Analog components are fundamental blocks of smart systems, as they allow a tight interaction with the environment, in terms of both sensing/actuation and communication. This impacts on the design of the overall system, and mainly on the validation phase, that thus requires the joint simulation of digital and analog aspects. In this scenario, this paper proposes the automatic conversion of analog models to C++-based languages, to remove the overhead of co-simulation with traditional virtual platform tools. The proposed methodology allows to convert a given analog description to either: 1) a fully equivalent description or 2) an abstract representation for faster simulation which models only the aspects of interest. Effectiveness and correctness have been proved on a number of case studies that highlight the effectiveness and potentiality of the proposed methodology.

The revolutionary technologies behind Industry 4.0 have opened a new era for manufacturing: connected and autonomous machines, collaborative robotics, and monitoring techniques are spreading to increase productivity and sustainability. From the workers’ perspective, they bring new safety threats but also opportunities to solve old ones, while concerns about workers’ privacy arise due to the increase of data sensed and transferred from the shop floor. This paper presents the results of a research project addressing the prediction of dangerous conditions through workplace monitoring with privacy guarantees. This work is driven by a realistic approach starting from the fact that it is entirely centered on a real 14-meter production line equipped with an extensive array of top-tier devices, including robotic arms, autonomous mobile robots, a reconfigurable moving belt, a multi-camera system, and a highly efficient data transport and computation infrastructure. This project shows safety and privacy achievements over six representative use cases such as man-on-the-ground, environmental events (e.g., fire incidents), workers’ errors that can lead to potential accidents, compliance of Personal Protective Equipment (PPE), and gatherings restrictions. The benefits of this study extend to stakeholders such as manufacturers and workers offering safety systems that can be deployed in industrial settings while addressing privacy concerns and providing compliance with regulations. The industrial laboratory at the heart of this study represents with realism a dynamic and interconnected Industry 4.0 and 5.0 environment.