publications

2024

1. RRPDG: A Graph Model to Enable AI-Based Production Reconfiguration and Optimization

   Sebastiano Gaiardelli ,  Michele Lora ,  Stefano Spellini , and 1 more author

   IEEE Transactions on Industrial Informatics, 2024

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   This article introduces the regionalized resource process dependence graphs (RRPDGs): a manufacturing processes representation inspired by the regionalized value state dependence graphs traditionally used in software compilers. An RRPDG is an ordered sequence of nodes, each characterized by stereotyped input and output parameters, encapsulating a transformation of the process state (e.g., a manufacturing operation). RRPDG allow defining complex transformations by composing a set of nodes (i.e., regions), hiding the internal details. Then, RRPDGs are used to automatically reasoning over dynamic reconfiguration and process optimization: an instance of the Astar search algorithm is used to search for possible transformations while pursuing an optimization function. The rules defined in this article over RRPDG models enforce the transformations’ correctness. We use RRPDGs to model a real production system while the transformation rules are applied to optimize the system’s processes. The proposed representation reduced the search complexity in each experiment, allowing to reach an optimal solution also in the case for which classical approaches were unable to complete before reaching the timeout. In all the experiments, the cost of the solution produced by using the regionalized representation is minor than the the solution produced by using the classical representation.

2. Integrating Modeling Languages with Ontologies in the Context of Industry 4.0

   Mario Libro ,  Sebastiano Gaiardelli ,  Michele Lora , and 1 more author

   In 2024 IEEE International Conference on Industrial Technology (ICIT) , 2024

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   The evolving landscape of manufacturing systems and the increasing complexity of production lines necessitate innovative approaches for efficient information management and process modeling. The System Modeling Language (SysML) provides a powerful language to express such information. However, the expressiveness comes at a cost: on the one hand, the modeling phase requires a deep understanding of the domain; on the other, SysML lacks rigorous semantics. This work introduces a novel methodology that enriches the SysML with ontology reasoning in the context of manufacturing systems. The approach uses ontologies as a comprehensive knowledge base that encapsulates essential details about the machinery, their provided functions, and the associated constraints. The approach offers a reliable and efficient way to verify the consistency and correctness of production recipes: it ensures recipes’ practical applicability in the manufacturing process while reducing errors that can occur in the modeling phase. The proposed methodology has been validated through its application to a fully-fledged manufacturing line, showing its applicability in real-world scenarios.

3. A Data Fusion Service-Oriented Infrastructure for Production Line Monitoring

   Sebastiano Gaiardelli ,  Nicola Dall’Ora ,  Francesco Ponzio , and 4 more authors

   In 2024 IEEE International Conference on Industrial Technology (ICIT) , 2024

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   The Industry 4.0 paradigm has deeply changed classical manufacturing by introducing data-based analytics and decision-support strategies. At the state of the art, data used for manufacturing monitoring is mostly originated by sensors, that undergo a fusion step to align different data sources. However, this data is only relative to the monitored process, and it does not include the corresponding operating conditions and parameters, that are known by the Manufacturing Execution System (MES). Such information is currently either not included or labeled by hand, thus incurring in errors and limiting the amount of available labeled data. To overcome this issue and go beyond the sole data fusion of sensor data, this paper proposes an infrastructure that automatically label time series generated by sensors with information extracted from the MES, to achieve enhanced monitoring of the production process. The relevance of the proposed solution and the possibilities opened by its application are stressed with the application to a robotic arm.

4. VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge

   Alessio Mascolini ,  Sebastiano Gaiardelli ,  Francesco Ponzio , and 5 more authors

   In 2024 61th ACM/IEEE Design Automation Conference (DAC) , 2024

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   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 autore- gressive 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.

5. Design Automation fo Cyber-Physical Production Systems: Lessons Learned from the DeFacto Project

   Michele Lora ,  Sebastiano Gaiardelli ,  Chanwook Oh , and 3 more authors

   In 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE) , 2024

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   The DeFacto project, supported by the European Commission via a Marie Skłodowska-Curie Global Individual Fellowship, tackles the complexity arising from the transformation of industrial manufacturing systems into intricate cyber-physical systems. This evolution offers unprecedented oppor- tunities but also poses intellectual and engineering challenges. DeFacto aims to advance the design automation of cyber-physical production systems by developing innovative modeling paradigms, scalable algorithms, software architectures, and tools. In the DeFacto approach, production systems are managed through service-oriented manufacturing software architectures. System-level models capture the features and the requirements of production systems, representing both production and computational processes as services provided by the infrastructure. Methodologies for system analysis and optimization rely on compositional abstractions of system behaviors grounded in assume-guarantee contracts. This paper outlines key research endeavors, findings, and lessons learned from the DeFacto project.

6. An AI-Enabled Framework for Smart Semiconductor Manufacturing

   Khaled Sidahmed Sidahmed Alamin ,  Davide Appello ,  Alessandro Beghi , and 12 more authors

   In 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE) , 2024

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   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 300kC 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.

7. Dynamic Job and Conveyor-Based TransportJoint Scheduling in Flexible Manufacturing Systems

   Sebastiano Gaiardelli ,  Damiano Carra ,  Stefano Spellini , and 1 more author

   Applied Sciences, 2024

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   Efficiently managing resource utilization is critical in manufacturing systems to optimize production efficiency, especially in dynamic environments where jobs continually enter the system and machine breakdowns are potential occurrences. In fully automated environments, co-ordinating the transport system with other resources is paramount for smooth operations. Despite extensive research exploring the impact of job characteristics, such as fixed or variable task-processing times and job arrival rates, the role of the transport system has been relatively underexplored. This paper specifically addresses the utilization of a conveyor belt as the primary mode of transportation among a set of production machines. In this configuration, no input or output buffers exist at the machines, and the transport times are contingent on machine availability. In order to tackle this challenge, we introduce a randomized heuristic approach designed to swiftly identify a near-optimal joint schedule for job processing and transfer. Our solution has undergone testing on both state-of-the-art benchmarks and real-world instances, showcasing its ability to accurately predict the overall processing time of a production line. With respect to our previous work, we specifically consider the case of the arrival of a dynamic job, which requires a different design approach since there is a need to keep track of partially processed jobs, jobs that are waiting, and newly arrived jobs. We adopt a total rescheduling strategy and, in order to show its performance, we consider a clairvoyant scheduling approach, in which job arrivals are known in advance. We show that the total rescheduling strategy yields a scheduling solution that is close to optimal.

8. Enabling Service-oriented Manufacturing through Architectures, Models and Protocols

   Sebastiano Gaiardelli ,  Stefano Spellini ,  Marco Panato , and 4 more authors

   IEEE Access, 2024

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   Modern production lines are often composed of machinery from different vendors that must be connected with each other to increase the overall interoperability and flexibility. A common problem that arises in such systems is the complexity of the configuration task: they usually require each component to be manually configured. Thus, machinery requires different configuration strategies, negatively impacting scalability and increasing the chance of human errors. Furthermore, each time the manufacturing system has to be updated, the entire procedure must be repeated. This paper proposes a software architecture abstracting the complexity of existing production lines by enabling the service-oriented manufacturing paradigm. Then, it presents a strategy to model manufacturing systems, covering the topology of the production plant, machinery, and production recipes. The paper also proposes a model-based methodology to automatically configure the reference software architecture and hence the machines in the system. The application of the contributions to a fully-fledged production line shows the effectiveness of relying on model-based automatic configuration.

2023

1. Neuro-Symbolic Empowered Denoising Diffusion Probabilistic Models for Real-Time Anomaly Detection in Industry 4.0: Wild-and-Crazy-Idea Paper

   Luigi Capogrosso ,  Alessio Mascolini ,  Federico Girella , and 10 more authors

   In 2023 Forum on Specification & Design Languages (FDL) , 2023

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   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.

2. Robotic Arm Dataset (RoAD): A Dataset to Support the Design and Test of Machine Learning-Driven Anomaly Detection in a Production Line

   Alessio Mascolini ,  Sebastiano Gaiardelli ,  Francesco Ponzio , and 5 more authors

   In IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society , Oct 2023

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   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.

2022

1. Integrating Smart Contracts in Manufacturing for Automated Assessment of Production Quality

   Sebastiano Gaiardelli ,  Stefano Spellini ,  Michele Pasqua , and 2 more authors

   In IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society , Oct 2022

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   Products and materials traceability is essential in modern manufacturing, where the production must meet certain standards that range from Quality Control (QC) to the quality of the used materials. In this environment, blockchain applications allow certifying data provenience and subsequent modification, offering trust and security along the entire supply chain. Nonetheless, the design and the development of such applications are usually performed manually and, thus, subject to errors.

2. SMART-IC: Smart Monitoring and Production Optimization for Zero-waste Semiconductor Manufacturing

   Khaled Sidahmed Sidahmed Alamin ,  Yukai Chen ,  Sebastiano Gaiardelli , and 7 more authors

   In 2022 IEEE 23rd Latin American Test Symposium (LATS) , Sep 2022

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   The Industry 4.0 revolution introduced decentralized, self-organizing, and self-learning systems for production control. New machine learning algorithms are getting increasingly powerful to solve real-world problems, like predictive maintenance and anomaly detection. However, many data-driven applications are still far from being optimized to cover many aspects and the complexity of modern industries; correlations between smart monitoring, production scheduling, and anomaly detection/predictive maintenance have only been partially exploited. This paper proposes to develop new data-driven approaches for smart monitoring and production optimization, targeting semiconductor manufacturing, one of the most technologically advanced and data-intensive industrial sectors, where process quality, control, and simulation tools are critical for decreasing costs and increasing yield. The goal is to reduce defect generation at the electronic component level and its propagation to the system- and system-of-systems- level by working on (1) enhanced anomaly detection, based on the human-in-the-loop concept and on advanced treatment of multiple time-series and of domain adaptation, (2) smart and predictive maintenance based on both objective data traces and simulated ones, to mitigate the risk of degrading product quality, and (3) the construction of an extended manufacturing software stack that allows anomaly- and maintenance-aware policies to enhance production line scheduling and optimization.

3. On the Impact of Transport Times in Flexible Job Shop Scheduling Problems

   Sebastiano Gaiardelli ,  Damiano Carra ,  Stefano Spellini , and 1 more author

   In 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA) , Sep 2022

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   Manufacturing systems require a careful scheduling of the resource usage to maximize the production efficiency. In a completely automated environment, the transport system should be orchestrated to work smoothly with the other resources. While the impact of job characteristics, such as fixed or variable processing times of the tasks composing the jobs, or task dependencies, has been extensively studied, the role of the transport system has received less attention.

4. A Hierarchical Modeling Approach to Improve Scheduling of Manufacturing Processes

   Sebastiano Gaiardelli ,  Stefano Spellini ,  Michele Lora , and 1 more author

   In 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE) , Jun 2022

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   Timely response to sudden production events and requirements shifts is a key feature of Industry 4.0. It requires techniques to manipulate and optimize the production processes, and components providing an high degree of reconfigurability.

5. A Software Architecture to Control Service-Oriented Manufacturing Systems

   Sebastiano Gaiardelli ,  Stefano Spellini ,  Marco Panato , and 2 more authors

   In 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE) , Mar 2022

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   This paper presents a software architecture extending the classical automation pyramid to control and reconfigure flexible, service-oriented manufacturing systems. At the Planning level, the architecture requires a Manufacturing Execution System (MES) consistent with the International Society of Automation (ISA) standard. Then, the Supervisory level is automated by introducing a novel component, called Automation Manager. The new component interacts upward with the MES, and downward with a set of servers providing access to the manufacturing machines. The communication with machines relies on the OPC Unified Architecture (OPC UA) standard protocol, which allows exposing production tasks as “services”.

2021

1. Enabling Component Reuse in Model-based System Engineering of Cyber-Physical Production Systems

   Stefano Spellini ,  Sebastiano Gaiardelli ,  Michele Lora , and 1 more author

   In 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ) , Sep 2021

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   Manufacturing lines are evolving into complex cyber-physical production systems. However, their growth in complexity is not matched by the development of structured modeling and design methodologies. In particular, approaches exploiting both models typical of the manufacturing domain and models used by computer engineers are still missing.

2. Modeling in Industry 5.0: What Is There and What Is Missing: Special Session 1: Languages for Industry 5.0

   Sebastiano Gaiardelli ,  Stefano Spellini ,  Michele Lora , and 1 more author

   In 2021 Forum on specification & Design Languages (FDL) , Sep 2021

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   The Industry 4.0 trend speeds up the adoption of a variety of technologies. In modern manufacturing, system data are collected both from the field through sensors and by exploiting complex simulations. Data analysis techniques became crucial to build and maintain any efficient production line, while autonomous systems and robots are the main focus of researchers and practitioners.