Moving beyond the automation-driven paradigm of Industry 4.0, Industry 5.0 emphasizes human-centric industrial systems where human creativity and instincts complement precise and advanced machines. With this new paradigm, there is a growing need for resource-efficient and user-preferred manufac-turing solutions that integrate humans into industrial processes. Unfortunately, methodologies for incorporating human elements into industrial processes remain underdeveloped. In this work, we present the first pipeline for the creation of a human-centered Digital Twin (DT), leveraging Unreal Engine's MetaHuman technology to track worker alertness in real-time. Our findings demonstrate the potential of integrating Artificial Intelligence (AI) and human-centered design within Industry 5.0 to enhance both worker safety and industrial efficiency.

Detecting complex anomalies on massive amounts of data is a crucial task in Industry 4.0, best addressed by deep learning. However, available solutions are computationally demanding, requiring cloud architectures prone to latency and bandwidth issues. This work presents VARADE, a novel solution implementing a light autoregressive framework based on variational inference, which is best suited for real-time execution on the edge. The proposed approach was validated on a robotic arm, part of a pilot production line, and compared with several state-of-the-art algorithms, obtaining the best trade-off between anomaly detection accuracy, power consumption and inference frequency on two different edge platforms.

With the rise of Machine Learning (ML) and Artificial Intelligence (AI), the semiconductor industry is undergoing a revolution in how it approaches manufacturing. The SMART-IC project (DATE'24 MPP category: initial stage) works in this direction, by proposing an AI-enabled framework to support the smart monitoring and optimization of the semiconductor manufacturing process. An AI-powered engine examines sensor data recording physical parameters during production (like gas flow, temperature, voltage, etc.) as well as test data, with different goals: (1) the identification of anomalies in the production chain, either offline from collected data-traces or online from a continuous stream of sensed data; (2) the forecasting of new data of the future production; and (3) the automatic generation of synthetic traces, to strengthen the data-based algorithms. All such tasks provide valuable information to an advanced Manufacturing Execution System (MES), which reacts by optimizing the production process and management of the equipment maintenance policies. SMART-IC is a 300k€ academic project funded by the Italian Ministry of University and supported by STMicroelectronics and Technoprobe with industrial expertise and real-world applications. This paper shares the view of SMART-IC on the future of semiconductor manufacturing, the preliminary efforts, and the future results that will be reached by the end of the project, in 2025.

Creating a virtual model of a real system brings several advantages before its effective fabrication, especially in the design phases. Simulating a system is helpful for determining whether and what improvements need to be brought to the prototype or for testing changes given by the presence of faults. In this context exploiting the features of a gaming engine created mainly for video game purposes for simulating physical prototypes could reveal new research perspectives. Advanced rendering capabilities, physical simulation, and accuracy in describing different materials obtainable make this environment very attractive even beyond the gaming world. Another key feature that can be obtained with these simulators is the combination of accurately modeled physical properties and graphical rendering of physical behaviors. Together, they generate a visualization that is physically accurate and graphically realistic. For this reason, this article examines the development of behavioral models inside the Unreal Engine gaming environment, mainly based on the C++ language. The models created will be used for design analysis, performance monitoring, and improvement research. The model chosen as a case study is a digital twin of a DC Motor simulated in normal operating conditions and in the presence of faults.

Under the pressure of the Industry 4.0 revolution, and now with the European Chips Act, smart systems are becoming omnipresent in all industrial sectors, e.g., automotive and aerospace. Such systems contain digital and analog components belonging to several physical domains, e.g., electrical and mechanical. To ensure robustness, the whole system must be validated as early as possible in the development cycle, by taking into account all such domains, as recommended by the ISO 26262 standard in the case, e.g., of automotive systems. Unfortunately, validation techniques, including fault injection and simulation are not as advanced on the analog side as the digital counterpart: i) they are not fully standardized ii) they are highly domain-dependent, and iii) they are performed separately from the digital flow. This article proposes to improve the design of smart systems by generating faulty scenarios through analog fault injection across several physical domains. By exploiting these faulty scenarios, it is possible to improve the robustness of the analog part and, at the same time, to improve the quality of the digital part that controls the system functionality. A multi-domain case study containing a microcontroller and a three-axis accelerometer is presented to demonstrate the validity of the proposed approach in many industrial contexts.

The continuous evolution of Industry 4.0 demands advanced solutions for optimizing production processes, reducing energy consumption, and enhancing the capabilities of both human operators and production devices in smart factories. As factory automation software becomes increasingly complex, ensuring its robustness through extensive testing is critical. However, testing on actual production systems is often impractical due to the critical nature of the software.This paper explores the use of digital twins to simulate factory environments, enabling thorough validation of automation software. We propose leveraging Lingua Franca and the Functional Mock-up Interface (FMI) standard to create comprehensive digital twins. Lingua Franca ensures deterministic execution in simulations, while FMI facilitates the integration of diverse simulators for accurate modeling of heterogeneous systems. We introduce an interfacing method to integrate FMI components within Lingua Franca and present a strategy for modeling software-machinery interactions using the OPC Unified Architecture (OPC UA) protocol. The methodology is applied to develop a digital twin of a logistics subsystem in a manufacturing system, demonstrating the effectiveness of the simulation environment through various scenarios.

The safety of aircraft heavily depends on the in-tegrity of the Landing Gear System (LGS). However, gathering real-world fault data to support effective Prognostic and Health Management (PHM) practices presents significant challenges. This work proposes a novel methodology for generating synthetic fault data using a multi-physics Simscape model of a landing gear deployment/retraction mechanism. The model incorporates specialized fault blocks designed to replicate various hydraulic failure modes, aiming to broaden the pool of fault data covering the most common failures. This approach promises to enhance maintenance strategies and facilitate the development of hybrid Model-Based and Data-Driven solutions. Ultimately, the results of this study will be used to understand the physics within the landing gear better and gather the necessary data to create an effective Digital Twin for predictive maintenance.

Smart systems contain digital and analog components of several physical domains, e.g., electrical and mechanical During the design phase, the fault injection, which checks the system functionality following the guidelines of ISO standard 26262, enhances the system’s robustness. Unfortunately, fault injection and simulation on the analog side are i) not fully standardized compared to their digital counterparts, ii) highly domain-dependent, and iii) performed separately from the digital. This article proposes to improve the design of smart systems by generating faulty scenarios through analog fault injection across several physical domains. By exploiting these faulty scenarios it is possible to improve the robustness of the analog part and simultaneously improve the quality of the digital part that controls the system functionality.

Over the last decade, the industrial world has been involved in a massive revolution guided by the adoption of digital technologies. In this context, complex systems like cyber-physical systems play a fundamental role since they were designed and realized by composing heterogeneous components. The combined simulation of the behavioral models of these components allows to reproduce the nominal behavior of the real system. Similarly, a smart system is a device that integrates heterogeneous components but in a miniaturized form factor. The development of smart or cyber-physical systems, in combination with faulty behaviors modeled for the different physical domains composing the system, enables to support advanced functional safety assessment at the system level. A methodology to create and inject multi-domain fault models in the analog side of these systems has been proposed by exploiting the physical analogy between the electrical and mechanical domains to infer a new mechanical fault taxonomy. Thus, standard electrical fault models are injected into the electrical part, while the derived mechanical fault models are injected directly into the mechanical part. The entire flow has been applied to two case studies: a direct current motor connected with a gear train, and a three-axis accelerometer.

Since the last century, the exponential growth of the semiconductor industry has led to the creation of tiny and complex integrated circuits, e.g., sensors, actuators, and smart power. Innovative techniques are needed to ensure the correct functionality of analog devices that are ubiquitous in every smart system. The ISO 26262 standard for functional safety in the automotive context specifies that fault injection is necessary to validate all electronic devices. For decades, standardization of defect modeling and injection mainly focused on digital circuits and, in a minor part, on analog ones. An initial attempt is being made with the IEEE P2427 draft standard that started to give a structured and formal organization to the analog testing field. Various methods have been proposed in the literature to speed up the fault simulation of the defect universe for an analog circuit. A more limited number of papers seek to reduce the overall simulation time by reducing the number of defects to be simulated. This literature survey describes the state-of-the-art of analog defect injection and fault simulation methods. The survey is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow, allowing for a systematic and complete literature survey. Each selected paper has been categorized and presented to provide an overview of all the available approaches. In addition, the limitations of the various approaches are discussed by showing possible future directions.

In this paper, we present the project “VIR2EM: VIrtualization and Remotization for Resilient and Efficient Manufacturing” by providing details on its research themes and its scientific and technological output. The project, centered on virtualization and remotization in the industrial sector, was promoted by Regione Veneto in Italy, and it has seen the participation and collaboration of 3 universities, 1 public research entity, and 10 companies composed of end users of digital solutions and high knowledge-intensive service providers. The project aims to develop and use tools for the virtualization of processes, systems, resources, and remoting of operations in order to: (1) maximize the efficiency of manufacturing systems under normal operating conditions; (2) maintain operations in case of emergency situations; (3) facilitate the restart of operations downstream of emergency situations by ensuring flexibility and predictive capability. Each theoretical proposal has been validated in distinct industrial facilities by constructing ten different prototypes.

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before.

With functional safety being increasingly important in the development of mixed-signal products for automotive applications, EDA solutions have appeared striving to help designers in the setup and execution of fault injection campaigns. Despite the ongoing work to standardize the definition of defect models and coverage calculation methods in the IEEE P2427 draft standard, there is a lack of a unified and portable method to define defect templates that can be used to inject in a systematic way defects in an analog circuit. Each of the existing EDA tool sets for fault injection proposes its own proprietary method to specify how defects should be defined and injected. The proposed paper describes a Verilog-A-based approach to coding defect templates, which through compliance with the Verilog-A standard, warrants portability across compatible simulators. The approach has been validated on the circuits from the Analogue Benchmark Circuits made available by the IEEE P2427 working group.

The early detection of anomalous behaviors from a production line is a fundamental aspect of Industry 4.0, facilitated by the collection of massive amounts of data enabled by the Industrial Internet of Things. Nonetheless, the design and validation of anomaly detection algorithms, mostly based on sophisticated Machine Learning models, heavily rely on the availability of annotated datasets of realistic anomalies, which is very difficult to obtain in a real production line. To address this problem, we introduce the Robotic Arm Dataset (RoAD), specifically designed to support the development and validation of Multivariate Time Series Anomaly Detection (MTSAD) algorithms. We collect and annotate a large number of data and metadata to characterize the motion and energy consumption of a collaborative robotic arm in a full-fledged production line and annotate a comprehensive set of healthy as well as realistic anomalies scenarios. To prove the significance of RoAD and encourage future developments, we benchmark several state-of-the-art anomaly detection algorithms on our newly introduced dataset, and we freely release it to the scientific community.

Constructing a holistic digital twin of a system composed of multiple physical domains is crucial for various tasks. In particular, when the simulation is extended with faults, it becomes a very important resource to achieve robust functional safety analysis. This article proposes a new methodology to build non-electrical fault models for the thermal domain. Such thermal faults are defined through an electrical circuit representing the thermal behavior of the system, known as the Cauer network, based on the physical analogies between the two domains. Including this thermal representation in a multi-domain system allows to simulate the interconnections between different physical domains, thus achieving a more realistic system behavior and evaluating the mutual impact of different domains (e.g., mechanical, electrical and thermal). The entire methodology is applied to a complex case of study implemented by using Verilog-AMS as a proof of concept.

There are several languages for modeling a Cyber-Physical System (CPS). One of them is Verilog-AMS, which allows representing a system belonging to the electrical and mechanical physical domains in a single model through different disciplines. A framework for the automatic fault injection in the electrical and mechanical domains is proposed in this context. In particular, starting from a mechanical system, it is possible to represent it as an electrical circuit by exploiting the physical analogies. In the electrical domain, fault modeling and injection techniques are more advanced than in other physical domains. Extending the analogies to fault models makes it possible to apply the electrical fault models in the equivalent circuit to the mechanical system. These yields mechanical-level faulty behaviors, which can be injected into the mechanical domain, resulting in mechanical (physical) faults, depending on the component. It is finally shown an example of execution of this flow through a model of an electric motor, in which mechanical faults are injected. Simultaneously, the equivalent electrical faults are injected into the equivalent electrical circuit.

Digital twins of production lines do not focus solely on the management of the production process, they can also monitor and optimize other extra-functional aspects such as energy consumption and communications. This paper proposes the extension of digital twin concept in such directions. First, we extend the digital twin with models of energy consumption, that allow the monitoring of production line components throughout production lifetime. Then, we propose a flow to design the communication network starting from information obtained from the digital twin concerning the production, usage and flowing of information through the plant. All these methodologies start from the production line specification, then they enrich it with data collected during operation, and finally information is used to perform design and optimization. Results have been shown on a real Industry 4.0 research facility.

In this article, a predictive fault grouping based on the collection of faulty AC matrices at fault-free operating points is presented as a means to approximate the final distribution of faults in equivalence classes using a minimal computational effort. The method is computationally cheap because it avoids performing DC or transient simulations with faults injected and limits itself only to AC simulations with faults activated. The technique provides an approximation, since it does not characterize faults at the corresponding faulty operating point but instead looks at how they would modify the fault-free operating point once injected.The approximate grouping achieves an excellent correlation to the final classification based on the comparison of faulty transient wave-forms. It is not meant as a substitute for the traditional fault injection simulations but as a support to decision making. It allows prioritizing faults to characterize the possible failure modes with a minimum number of fault injections, pushing out fault injections which are estimated to marginally increase the learning.

There are plentiful successors of SPICE language for describing transistor-level designs. For most of them, the semantic matches those of SPICE, and only the syntax is changed. Others instead provide more default models or analysis tools. Consequently, a commercial tool is usually required for simulating, analyzing, and especially manipulating these languages. This article proposes a framework that relies on the shared semantic for reading, writing, or manipulating transistor-level designs. The ultimate goal of the framework is: reading an input design written in a specific syntax and then allowing to write the same design in another syntax. First, the input description is parsed by a language-specific front-end which turns it into an in-memory abstract syntax tree that follows the common semantic. Then, the in-memory description can be subject to different user-defined manipulations built on top of a series of API or visitor/listener classes. Finally, the description goes through the desired back-end, transforming the in-memory description into the target transistor-level language. As a use-case for the proposed framework, we chose the process of analog fault injection. This activity requires adding, removing, or replacing nodes, components, or even entire sub-circuits. Therefore, the framework is completely written in C++, and its APIs are also interfaced with python. The entire framework is open-source and available on GitHub.

Predictive maintenance in a manufacturing company is strategic, in order to maintain high production quality and to avoid unexpected production downtimes. In this scenario, the prediction of future machineries health status is necessary in order to plan maintenance cycles and to optimize the production. The proposed approach relies on the use of Electronic Design Automation (EDA) techniques mapped from the electronic domain to the production line domain. This paper proposes a general framework based on the EDA approach that allows to set-up a maintenance strategy by analyzing data retrieved from sensors. An MSM, is associated to each observable measurement, while a Supervisor monitors the current state of each Monitoring State Machine (MSM) by raising alerts when the monitored equipment is deviating from its normal behavior. This framework is the Digital-Twin of the plant devoted to its monitoring. It has some execution modalities ranging from online monitoring to predictive maintenance. The methodology has been applied to a mechanical transmission system showing its effectiveness.

In the context of industry 4.0, it is strategic to support factories with innovative maintenance approaches, so to avoid faults and decrease the risks of a production stop. The first step of the digitization of factories has been the collection of large amounts of data monitoring the health status of the plant. However, such data is of little use unless it is clearly correlated with information about faults occurred on the line: some faults may be sporadic, or happen only in extremely critical conditions, and thus no data may be available related to their occurrence. Artificially generating such data would force to actually damage the plant, that is of course not a viable solution. The goal of this work is to generate faulty temporal series, that reproduce the behavior of a component on the occurrence of specific faults. The innovative approach models the component of interest in Verilog-AMS (VAMS) and systematically injects the faults of interest, by keeping a direct link with the real possible cause of such faulty behavior on the plant. To prove the effectiveness of the proposed solution, the approach is applied to a direct current motor (DC motor), an electromechanical system that converts electrical energy into mechanical energy.

Customizing computer vision applications for embedded systems is a common and widespread problem in the cyber-physical systems community. Such a customization means parametrizing the algorithm by considering the external environment and mapping the Software application to the heterogeneous Hardware resources by satisfying non-functional constraints like performance, power, and energy consumption. This work presents a framework for the design and simulation of embedded vision applications that integrates the OpenVX standard platform with the Robot Operating System (ROS). The paper shows how the framework has been applied to tune the ORB-SLAM application for an NVIDIA Jetson TX2 board by considering different environment contexts and different design constraints.