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Pubblications
Exploring the cognitive workload assessment according to human-centric principles in Industry 5.0.
A. Nadaffard, L.M. Oliveri, D. D’Urso, F. Facchini, C. Sassanelli
Abstract
Industry 4.0 and 5.0 paradigms have been crucial for companies in employing digital technologies as an ally for men to free them from dangerous and routine tasks in favour of higher value tasks, putting humans at the center of the organization as the decision maker. However, the well-being of industrial workers is still under analysis, unveiling the need to understand how to incorporate human-centric principles and technological advancements in Industry 5.0. For this reason, this research reveals current research trajectories and explores the cognitive workload using subjective and objective indicators. Structured around two research inquiries, the investigation navigates through a literature review, an examination of cognitive workload, evaluation methods employed, results, and a discussion. The discussion highlights cognitive ergonomics and advocates for a harmonious balance between human and machine capabilities. It identifies factors contributing to cognitive overload in manufacturing and maps their interconnections. The investigation recommends technological proposals based on user per-ceptions and user participation in technology implementation, emphasising accurate information, effective communication, and aligning cognitive workload with users’ capacities. The analysis of recent research trends reveals a growing adoption of augmented reality and EEG approaches. This investigation offers insights into future research directions, urging a nuanced exploration of industrial activities and addressing cognitive work-load across organisational layers in the context of Industry 5.0.
An assignment model for high-cognitive-workload maintenance activities in Industry 5.0.
M. Vitti, F. Facchini, C. Sassanelli, G. Mummolo
Abstract
Industry 5.0 paradigm emphasises human-centricity, sustainability, and resilience in production systems. If, on the one hand, Industry 4.0 (I4.0) promoted production efficiency and quality through the development and implementation of advanced technologies, on the other hand, this paradigm has main limitations due to the limited consideration of industrial sustainability and workers’ welfare. In the I4.0 context, the operator may face cognitive overload due to the inherent complexity of ordinary activities. In this scenario, maintenance operations are of utmost relevance. They are indeed critical in any production context, as they are not value-adding but directly determine factors such as the safety and performance of industrial systems. In the context of I4.0, a paradigm known as Maintenance 4.0 has developed, which involves adopting advanced technologies for maintenance activities. While this paradigm allowed for advantages such as the implementation of predictive maintenance policies, it has also complicated ordinary activities, especially from a cognitive point of view. To this concern, the objective of the present work consists of a task assignment model that supports the company in identifying the proper operator/s to accomplish maintenance tasks with high cognitive workloads. Identifying the proper operator for each task led to reducing the probability of accidents, increasing human well-being, and improving the reliability of the maintained assets. A numerical application of the proposed model proved its effectiveness in identifying the operator to be assigned a specific maintenance activity based on its skills and considering the cognitive workload of previous maintenance tasks assigned to the same operator.
Investigating maintenance operations in Industry 5.0: a cognitive-oriented tasks framework.
M. Vitti, F. Osnato, F. Facchini, C. Sassanelli, D. Romero
Abstract
The Industry 5.0 paradigm aims to improve, through a human-centric approach, the performance of the cyber-physical production systems promoted by the Fourth Industrial Revolution. If, on the one hand, the digitalisation promoted by the Industry 4.0 paradigm provides many opportunities for improving the performance of production systems, on the other hand, it introduces a high level of complexity for operators in the execution of ordinary activities mainly from a cognitive point of view. The complexity of tasks and the increasing use of innovative technologies could overload the operator with numerous options and efforts to be made in a limited time, requiring decisions that may lead to an excessive cognitive workload and reduced human well-being in work environments. In this context, maintenance activities are of utmost relevance; their inherent complexity and the direct dependence of the production performance on their proper and timely execution led to the development of dedicated support technologies and techniques known as “Maintenance 4.0”. Notably, Maintenance 4.0 activities are strongly characterised by the above-outlined complexities, especially from a cognitive point of view. To this concern, the present research work consists of developing, through a systematic literature review, a “Cognitive-Oriented Maintenance 4.0 Tasks Framework” aimed at identifying the perceived cognitive workload according to an operator’s competencies profile. This conceptual framework represents the starting point for more in-depth analyses that will allow the identification of the proper operators to accomplish high-cognitive Maintenance 4.0 tasks, always ensuring their well-being and industrial performance.
A Decision Support System tailored to the Maintenance Activities of Industry 5.0 Operators.
L.M. Oliveri, F. Chiacchio, F. Facchini, G. Mossa
Abstract
Industry 5.0 addresses the human challenges of Industry 4.0 as a human-centric solution, placing the worker’s well-being at the centre of the production process. Consistent with this end, Industry 5.0 represents a paradigm shift that continuously emphasises human collaboration with technology. If, on the one hand, the last technologies supporting operator 5.0 to embrace the collaborative potential of human-machine cooperation, on the other hand, the complexity, as well as the rapid evolution of new technologies, could have the potential to produce immediate stress reaction leading to reduce the workers’ well-being. Recent studies have proved that many innovations in terms of approaches and technologies concern the maintenance area. In this field, the increasing evolution due to the rapid progress of technological equipment (e.g., augmented reality, digital twins, IoT, etc.) is leading to an increase in the human operators’ mental workload. This paper presents preliminary concepts and objectives of a Decision Support System (DSS) for the Diagnosis and Evaluation of Maintenance Operations. The DSS intends to assist industrial professionals and stakeholders in maintenance tasks characterised by significant cognitive demands. Considering variables such as task complexity and the mental and physical condition of operators involved in a maintenance task, the DSS will provide real-time recommendations for selecting the most suitable operators by promoting a gradual technology introduction, increasing industrial performance, and ensuring the well-being of the workers. The DSS design and implementation have been described, and the expected impacts in occupational, economic, and social terms proved the model’s effectiveness in most common industrial maintenance tasks.
Digital twin supporting environmental performance evaluation according to ISO certification: an application case in the tire industry.
R. Tota, F. Facchini,R.P. Iavagnilio, G. Mossa, C. Sassanelli,
Abstract
The link between digital evolution and environmental sustainability is reshaping how companies enhance their processes, contributing to address circular manufacturing (CM). The extant literature does not explain how to improve the tire production process to limit environmental negative impacts, which are its most critical phases, which Industry 4.0 technologies could be exploited and how they could intervene in the process to facilitate effective data management. This research proposes a framework indicating how digital technologies could support tire producers in reducing the ecological footprint of their operations. The proposed framework addresses ISO 14,046 and 14,067 International Organization for Standardization (ISO) certifications. Digital twin (DT) was chosen as the most suitable technology. The related framework was further detailed according to data-driven CM principles, providing the set of sensors to be embedded on the production process and defining the data and information to be gathered through them to address the requested ISO certifications
Enhancing Maintenance Operations in Industry 5.0: A Conceptual User Interface Design for Task Assignment
L. olivieri, F. chiacchio, N. Lo iacono, m. vitti, f. facchini
Abstract
The Fifth Industrial Revolution, or Industry 5.0, fosters an innovative, resilient, competitive, and society-centered industry. This era emphasizes enhanced human-machine interactions, enabling individuals to manifest their creativity through personalized products and services. As smart factories evolve, the demand for flexibility and adaptability necessitates increased cognitive efforts, particularly in maintenance tasks critical to the flexibility of production systems. Despite the potential of emerging technologies like Augmented Reality and Artificial Intelligence to aid operators, the complexity of tasks combined with the novelty of such technologies can overwhelm workers, thereby impacting workplace well-being. To tackle these challenges, the DESDEMONA project, funded by the European Union through PRIN as part of NextGenerationEU, is developing a Decision Support System (DSS). This system aims to provide real-time suggestions for assigning the most suitable operators for maintenance tasks characterized by high cognitive demands. The DSS considers three primary factors: the operator’s profile (including skills and age), their emotional state, and the availability of smart devices. This manuscript details the project’s initial results, presenting a simplified mathematical model capable of ranking the optimal list of operators. To demonstrate the effectiveness of the DSS, it is compared, through a simulation approach, with a simulated maintenance supervisor. This comparison highlights the system’s ability to identify, from the k-permutations of N operators, the number of optimal tuples that best fit the operational needs.
Exploring the Impact of Emotional States on Human Performance in Production Systems: A Simulation Approach
M. Campanella, A. Cimino, A. Padovano, V. Solina, A.O. Solis
Abstract
The Fifth Industrial Revolution, or Industry 5.0, fosters an innovative, resilient, competitive, and society-centered industry. This era emphasizes enhanced human-machine interactions, enabling individuals to manifest their creativity through personalized products and services. As smart factories evolve, the demand for flexibility and adaptability necessitates increased cognitive efforts, particularly in maintenance tasks critical to the flexibility of production systems. Despite the potential of emerging technologies like Augmented Reality and Artificial Intelligence to aid operators, the complexity of tasks combined with the novelty of such technologies can overwhelm workers, thereby impacting workplace well-being. To tackle these challenges, the DESDEMONA project, funded by the European Union through PRIN as part of NextGenerationEU, is developing a Decision Support System (DSS). This system aims to provide real-time suggestions for assigning the most suitable operators for maintenance tasks characterized by high cognitive demands. The DSS considers three primary factors: the operator’s profile (including skills and age), their emotional state, and the availability of smart devices. This manuscript details the project’s initial results, presenting a simplified mathematical model capable of ranking the optimal list of operators. To demonstrate the effectiveness of the DSS, it is compared, through a simulation approach, with a simulated maintenance supervisor. This comparison highlights the system’s ability to identify, from the k-permutations of N operators, the number of optimal tuples that best fit the operational needs.
Contact Info
- Email: desdemonaproject2024@gmail.com