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.

One of the key targets of Industry 4.0 and digital production, in general, is the support of faster, cleaner, and increasingly customizable manufacturing processes. Additive manufacturing (AM) is a natural fit in this context, as it offers the possibility to produce complex parts without the design constraints of traditional manufacturing routes, typically reducing both material waste and time to market. Nonetheless, the lack of repeatability of the manufacturing process, which typically translates into a lack of reproducibility and reliability of the quality of the final products compared to traditional subtractive technologies, is currently one of the major barriers to the widespread adoption of AM in mass production. To overcome this limitation, there are growing efforts in recent years toward better integration of advanced information technologies into AM, exploiting the layer-by-layer nature of the build. The consequence of these efforts is twofold: 1) the integration of advanced sensing technologies into the AM systems, making possible the in situ monitoring of huge amounts of data at multiple time scales and resolutions and 2) the ever-increasing role of data-driven approaches [especially machine learning (ML)] in the analysis of such data to provide real-time quality monitoring and process optimization. This article introduces and reviews the key technological developments of this phenomenon, with a special focus on metal powder bed fusion (PBF) technologies that are attracting the highest attention by the industrial AM community. After introducing the main manufacturing quality issues and needs that have to be developed and optimized, we provide a wide overview of the latest progress of in situ monitoring and control in metal PBF, with special regards to sensing technologies and ML approaches. Finally, we identify the open challenges and future research directions in this field.

The global shift towards electric vehicles (EVs) represents a critical strategy for fighting climate change and reducing both reliance on fossil fuels and $CO_{2}$ emissions. In this scenario, battery management systems (BMS) become crucial in EVs to ensure battery safety, reliability, and efficiency. Recently, data-driven estimation techniques have been proposed to estimate battery-related metrics, such as remaining capacity or aging condition. These techniques emerged as an answer to batteries' intrinsic variability, but they rely primarily on full charge/discharge cycles, overlooking the nuances of partial charging, which is prevalent in real-world usage. This paper presents a novel BMS architecture for EVs, leveraging digital twin technology and data-driven modeling to address these challenges. The proposed dynamic dual-model approach seamlessly integrates real-time monitoring with cloud-based analytics to continuously evaluate and predict battery capacity degradation. Key innovations include sophisticated feature engineering and segmentation strategies, which enable precise state of health (SoH) estimation across a wide range of driving conditions. Additionally, the architecture incorporates a dynamically retraining State of Charge/Energy (SoC/SoE) model that adapts to battery aging, thereby maintaining high accuracy throughout the battery's life cycle. Extensive validation using datasets from public institutions demonstrates the effectiveness and robustness of the proposed system.

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 aging of rechargeable batteries, with its associated replacement costs, is one of the main issues limiting the diffusion of electric vehicles (EVs) as the future transportation infrastructure. An effective way to mitigate battery aging is to act on its charge cycles, more controllable than discharge ones, implementing so-called battery-aware charging protocols. Since one of the main factors affecting battery aging is its average state of charge (SOC), these protocols try to minimize the standby time, i.e., the time interval between the end of the actual charge and the moment when the EV is unplugged from the charging station. Doing so while still ensuring that the EV is fully charged when needed (in order to achieve a satisfying user experience) requires a “just-in-time” charging protocol, which completes exactly at the plug-out time. This type of protocol can only be achieved if an estimate of the expected plug-in duration is available. While many previous works have stressed the importance of having this estimate, they have either used straightforward forecasting methods, or assumed that the plug-in duration was directly indicated by the user, which could lead to sub-optimal results. In this paper, we evaluate the effectiveness of a more advanced forecasting based on machine learning (ML). With experiments on a public dataset containing data from domestic EV charge points, we show that a simple tree-based ML model, trained on each charge station based on its users’ behaviour, can reduce the forecasting error by up to 4× compared to the simple predictors used in previous works. This, in turn, leads to an improvement of up to 50% in a combined aging-quality of service metric.

Virtual platforms are a powerful support for the development and early validation of embedded SW. However, complex smart devices are built by aggregating heterogeneous components provided by different vendors, thus requiring the development of custom ad-hoc virtual platforms. Even worse, components of the underneath HW platform may belong to different design domains, that are usually expressed using a huge variety of different languages. This high degree of heterogeneity in terms of both design and specification languages must be effectively managed by the design flow so to help engineers in assembling the final system. This paper proposes a meet-in-the-middle approach to create virtual platforms of heterogeneous systems. The starting point is a set of heterogeneous models, developed by adopting the designer's favorite language and formalism. The methodology envisions the adoption of existing automatic translation and abstraction tools to automatically integrate models of components into a single homogeneous system-level executable description. The approach is supported by an analysis of the typical design flow, that leads to the definition of design domain/abstraction level taxonomies. Such taxonomies are then used to identify what characteristics would allow efficient system-level simulation, and the corresponding transformations to be applied to the starting models to achieve a “holistic” system executable representation. The benefit of such an approach is particularly evident on any kind of highly heterogeneous systems, such as smart devices. The proposed methodology has been applied to two case studies with different degrees of heterogeneity, with the goal exemplifying its adoption on concrete scenarios and to prove its effectiveness.