Invited Sessions' Summary:

1.    Cobot systems design and management from the human factors perspective: new paradigms for intelligent manufacturing – code 5v285

2.    Intelligent production systems for the iron and steel sector – code 9q914

3.    Intelligent Digital Twins for Cyber-Physical Manufacturing Systems - code k95nf

4.    Digital Twins for the control of advanced production systems – code qm6c1  

5.    Advances in complex assembly systems: new approaches for strategic decision making, tactical planning, and operational control - code s62p7 


Special Sessions' Summary:

1.    Self-healing and Self-organizing Techniques for Sustainable Manufacturing Systems – code k2e4m

2.    The Future of Human Work in Smart Manufacturing Environments – code f48s1



Cobot systems design and management from the human factors perspective: new paradigms for intelligent manufacturing

Proposed by Maurizio Faccio, Matteo Bottin, Riccardo Minto (Italy)

Keywords: Intelligent manufacturing; Collaborative manufacturing; Robotics

Code: 5v285


Among the recent technologies and methods constituting Industry 4.0, collaborative robots (or cobots) provide unique advantages. Introduced in the last decade, this new category of robots aims to physically interact with human operators in a shared environment thus, avoiding the need for the safety measures typical of traditional robotic systems. Workspace sharing, under proper condition, improves flexibility and reduces the cycle time. Moreover, the absence of safety fences allows to quickly change the layout, helping to implement a dynamic productive cell, capable of adapting to volume and model changes.

However, the cobots' ability to cooperate with human operators could decrease their efficiency if this interaction is not properly studied. When an operator works near an automatic system, safety is always a concern. Moreover, the needs and perceptions of the operator must be considered. Indeed, several works showed the human factors impact on the system performance in terms of productivity and production costs. This is in accordance with the human-centered direction of Industry 4.0, which introduces the concept of Operator 4.0, i.e., the augmentation of the human capabilities through these novel technologies.

This open special session aims to gather the latest research achievements, findings, and ideas regarding collaborative robots, with particular attention to the influence of human factors. These include advanced technologies, mathematical models and methods, automation, management techniques and approaches; moreover, industrial case studies are also welcome.

Code: 5v285

Intelligent production systems for the iron and steel sector 

Proposed by Valentina Colla, Marco Vannucci (Italy)

Keywords: Intelligent manufacturing; Monitoring; Optimization

Code: 9q914


The twofold challenge of digitalization and decarbonization is investing worldwide the steel sector. Intelligent production systems play a key role within the strategies implemented by the sector to face such challenges, as they are nowadays recognized as strong enablers for new production technologies improving economic, social and environmental sustainability of steelworks. However, the peculiar features of the steel sector must be considered, by avoiding straightforward deployment of solutions developed within other industrial sectors. Moreover, human factors need careful consideration, to include novel solutions into a broader social innovation process.

The session aims are gathering latest research achievements and findings concerning design, development and implementation of intelligent production systems in the steel field. This include novel technological frameworks, smart solutions for efficient data handling and exploitation, advanced monitoring and control tools and solutions, Artificial Intelligence (AI) and Machine Learning (ML). Moreover, industrial case studies are also welcome.

Original manuscripts are sought on research activities and industrial cases studies related (but not limited) to:

• intelligent data management and handling to improve production efficiency and product quality;

• continuous data access from IT systems to manufacturing devices;

• connecting MES and IT infrastructures with unified interfaces;

• innovative sensing equipment for improved process monitoring and control

• intelligent process monitoring strategies and tools

• AI and ML-based approaches

• Multi-Agent Systems deployment in the steel sector

• Quantum computing applications for the steel sector

• Digital Twins supporting intelligent management of production processes

• Total product quality control and management

• Lot-Size-One Production,

• New generation production planning and scheduling systems

• Advanced maintenance schemes in the steelmaking fields.

• Intelligent decision support systems specifically targeting the steel sector

• Robotic and Autonomous systems designed for steelmaking applications

• Intelligent systems supporting novel C-lean production routes

• IoT and seamless integration of workers into production

Code: 9q914

Intelligent Digital Twins for Cyber-Physical Manufacturing Systems 

Proposed by Vicente Lucena, Carlos Eduardo Pereira, Nasser Jazdi, Jose Barata, Marco Macchi, Sergio Cavalieri (Brazil)

Keywords: Digital twin; Digital manufacturing; Intelligent manufacturing

Code: k95nf


Cyber-Physical Manufacturing Systems (CPMS) are integrated systems consisting of decision-making skills, networking via digital communication technologies within physical processes. An Industrial Digital Twin is a virtual representation of a physical asset in a CPMS, capable of mirroring its static and dynamic characteristics using digital communication technologies. Several architectures for the realization of Digital Twins have been proposed over the last years. However, there is still room for new proposals to figure out the real needs of the modern industry. For example, a Digital Twin of a production system can address the challenge of making systems easily and quickly reconfigurable. Furthermore, we may improve various functions in a flexible manufacturing system using Artificial Intelligence approaches, e.g., by including machine learning for big data analysis. As a last exemplary case, taking the perspective of the products and services, digital twinning has a high potential for the support of the operations of individually produced artifacts, the management of fleets of such artifacts, the management of services, and the related value chain, thus guaranteeing advanced capabilities of the product-service systems.

On the whole, here raises the concept of an Intelligent Digital Twin, an asset able to apply appropriate algorithms to the real data to extract new knowledge from the physical system, that can help improve and optimize their physical counterparts' behavior. Intelligent Digital Twins can be adopted in several important industrial scenarios, such as process flow, energy consumption, predictive maintenance, dynamic calculation of reliability, advanced scheduling, collaborative fleet management, etc. Intelligence can be embedded at different levels, ranging from a seamless integration of humans in the loop to automated and even autonomous intelligence achievable thanks to growth from rule-based approaches to fully Artificial Intelligence and cognitive solutions.

This invited session aims to gather researchers working with these two main subjects, Digital Twins and Intelligence (especially based upon AI), aiming to share and possibly integrate both areas' advances in different application scenarios.

Code: k95nf

Digital Twins for the control of advanced production systems 

Proposed by Benoît Iung, Jay Lee, Marco Macchi, Elisa Negri, Georg Weichhart (Italy)

Keywords: Digital twin; Smart factory; Cobots (collaborative robots)

Code: qm6c1


The digitalization of manufacturing processes is advancing at an increasing pace. Digital Twins (DT) rely on different simulation techniques to replicate the behavior of physical objects or systems, such as manufacturing assets. DTs are grounded on Cyber-Physical Systems (CPS) capabilities, including computing and communication, and enable continuous synchronization of virtual space with physical systems. Research in DT includes various technologies and tools from diverse fields, comprising statistics and artificial intelligence. These shall be integrated to fully exploit the potential of business analytics to extract valuable information from real-time data for decision-making. In order to adopt DT to enhance plant control in CPS-based production systems, further investigations should clarify their role and integration / interoperability with shop floor control within extant production systems with different configurations and automation levels. Flexible and reconfigurable manufacturing systems would be enhanced by the real-life synchronization and intelligence connected to the DT, allowing dynamically optimized decision making, and resilient performance. Where human operators and collaborative robots collaborate, new approaches to DTs would augment operators supporting their decision making and actions, while exploiting the full coordination as well as allowing a proper management and control of the manufacturing activities of humans and robots. Overall, different configurations of automated or semi-automated production systems, will be upgraded thanks to the presence of DT, their simulation capabilities and the connected intelligence, helpful to finally operate advanced production systems that foster a step forward in the achievable performances.

Code: qm6c1


Advances in complex assembly systems: new approaches for strategic decision making, tactical planning, and operational control

Proposed by Nico André Schmid, Veronique Limère (Belgium)

Keywords: Assembly planning; Material handling; Collaborative manufacturing

Code: s62p7


Assembly systems have undergone massive changes due to an increase in product and process complexity, and competition. This creates many challenges for assembly systems’ management on the strategic, tactical, and operational levels as well as the intersections of those levels.

Industrial managers are faced with strategic decisions that guide the businesses direction. Important decision-making problems may include but are not limited to intra- and intersectoral assembly factory collaboration, outsourcing of subassembly production or logistics activities, production network design, and the use or creation of assembly platforms coordinating Manufacturing-as-a-Service activities. Providing reliable and scientific insights into these decision-making domains may impact the entire assembly sector.

Once, a strategic direction is decided, tactical decisions need to be taken to facilitate this business direction. One well-known problem in this domain is the assembly line balancing problem. However, due to the increasingly volatile demand, static balancing is insufficient and new buffering or assembly system reconfiguration approaches are needed. Additionally, assembly lines require to be fed with assembly parts in an efficient manner. Hitherto, approaches combining multiple decisions or hedging against volatility are scarce. New approaches may also consider human factors such as ergonomics.

The operational level typically concerns short planning horizons of a few days or weeks. Therefore, scheduling of machines, worker shifts, transportation and processes efficiently is crucial in this phase. Controlling the adherence to schedules may be tackled by the use of digital twins such that actions can be initiated when necessary. To allow agile reactions, worker training needs to be scheduled while maintaining high productivity.

This open session invites original manuscripts that aim to improve the effectiveness and efficiency of assembly systems from a strategic, tactical, or operational perspective. To this end, optimization and simulation approaches as well as industrial case studies, economic analyses, analytical models, experiments, and other decision-making techniques may be utilized.

Code: s62p7


Self-healing and Self-organizing Techniques for Sustainable Manufacturing Systems

Proposed by Zbigniew Banaszak, Grzegorz Bocewicz, Arkadiusz Gola, Marcos de Sales Guerra Tsuzuki, Marcin Witczak, Marek B. Zaremba (Poland)

Keywords: Self-organizing systems; Self optimization and configuration; Production planning

Code: k2e4m


The increasing adoption of methods from computational intelligence, biologically inspired computing, and optimization in engineering disciplines such as mechatronics, production or logistics enables the development of a new class of autonomous systems characterized by self-optimization, self-coordination, self-configuration, self-healing, self-diagnosis, self-repair, and so on. Technical systems possessing such features are distinguished by the endogenous adaption of objectives as reaction to changing influences and the resulting autonomous adjustment of parameters or structure and consequently of the system‘s behavior. Techniques implemented in them together with the opportunities brought by Industry 4.0 technologies provide an entirely new attitude to deploy smart and sustainable collaborative networks to face challenges and issues imposed by Sustainable Manufacturing Systems.

This special session focuses on the role of technology-enhanced life cycle management on final products and manufacturing assets to support sustainability strategies in manufacturing operations. Its goal is then to promote new ideas, principles and to generate new collaborations between scientists and engineers to present research results-oriented on advanced management methodologies, digital technologies and new management concepts for lifecycle risks, performance and costs.

Code: k2e4m 

The Future of Human Work in Smart Manufacturing Environments

Proposed by Chiara Cimini, David Romero, Sergio Cavalieri, Johan Stahre, Mirco Moencks (Italy)

Keywords: Human interaction; Human factors; Collaborative manufacturing

Code: f48s1


Automation, digitalisation and robotics have ushered a new industrial age in which machines and computers can substitute, complement and augment human workers in an increasingly wider range of activities, physical and cognitive, paving the way to the concepts of Operator 4.0 and Logistics Operator 4.0. Operators of the Future will be immersed in intelligent environments, with the possibility to share and receive realtime information from smart objects and will be involved in new collaboration mechanisms and social interactions with robots and artificial intelligent systems. Re-thinking manufacturing from a human-centred perspective allow to use and adapt digital, smart technologies to enhance the unique and irreplaceable capabilities of humans, who will continue to play a main role in the shopfloor. In Smart Manufacturing, the available amount of information will not be manageable for a normal operator, just because of the variety, quantity and intensity. New methods will be required to help Operators 4.0 in managing increasing cognitive workloads. Moreover, the COVID-19 pandemic called for urgent investigation about new practices of industrial smart working, allowing social distancing and space/time flexibility. Alongside the development of new technologies, studies in the human-related aspects must be carried out, both at theoretical level, highlighting the interdependences between technologies and human capabilities, and at practical level, providing industrial companies with effective tools to drive their workforce towards human-centred smart manufacturing environments.

This special session calls for high‐quality contributions investigating the main research challenges, reviews, case studies and applications related to the following topics (but not limited to):

•Multidisciplinary Approaches in Human-centred Smart Technologies Development

•Human-centred Development of Assisting/Augmenting Technologies

•Human Factors Affecting Implementations of Smart Manufacturing Technologies

•New Skills/Competencies for the Operator/Workforce 4.0

•Industrial Smart Working Theory and Practice

•Human-AI Interactions/Collaboration in Cognitive Tasks

•Human-Machine/Robot Interactions/Collaboration

Code: f48s1