Multi-agent systems to implement industry 4.0 components

The fast-changing market conditions, the increased global competition and the rapid technological developments demand flexible, adaptable and reconfigurable manufacturing systems based on Cyber-Physical Systems (CPS). Aligned with CPS, the adoption of production system architectures is suitable to reduce complexity and achieve interoperability in the industrial applications. In this context, the Reference Architecture Model for Industry 4.0 (RAMI4.0) provides the guidelines to develop Industry 4.0 (I4.0) compliant solutions, considering the existing industrial standards. The so-called I4.0 components implement this model in practice, combining the physical asset with its digital representation, named Asset Administration Shell (AAS). This paper explores the use of Multi-Agent Systems (MAS) to implement the AAS functionalities, taking advantage of their inherits characteristics, e.g., autonomy, intelligence, decentralization and reconfigurability. In this context, the mapping between AAS functionalities and MAS characteristics is provided, as well as the challenges for this implementation. The applicability is illustrated by digitalizing an inspection cell comprising an UR3 robot and several console products by using MAS technology.info:eu-repo/semantics/publishedVersio

Collective intelligence in self-organized industrial cyber-physical systems

Cyber-physical systems (CPS) play an important role in the implementation of new Industry 4.0 solutions, acting as the backbone infrastructure to host distributed intelligence capabilities and promote the collective intelligence that emerges from the interactions among individuals. This collective intelligence concept provides an alternative way to design complex systems with several benefits, such as modularity, flexibility, robustness, and reconfigurability to condition changes, but it also presents several challenges to be managed (e.g., non-linearity, self-organization, and myopia). With this in mind, this paper discusses the factors that characterize collective intelligence, particularly that associated with industrial CPS, analyzing the enabling concepts, technologies, and application sectors, and providing an illustrative example of its application in an automotive assembly line. The main contribution of the paper focuses on a comprehensive review and analysis of the main aspects, challenges, and research opportunities to be considered for implementing collective intelligence in industrial CPS. The identified challenges are clustered according to five different categories, namely decentralization, emergency, intelligent machines and products, infrastructures and methods, and human integration and ethics. Although the research indicates some potential benefits of using collective intelligence to achieve the desired levels of autonomy and dynamic adaptation of industrial CPS, such approaches are still in the early stages, with perspectives to increase in the coming years. Based on that, they need to be further developed considering some main aspects, for example, related to balancing the distribution of intelligence by the vertical and horizontal dimensions and controlling the nervousness in self-organized systems.info:eu-repo/semantics/publishedVersio

Towards the digitization using asset administration shells

Industry 4.0 (I4.0) is promoting the digitization of traditional manufacturing systems towards flexible, reconfigurable and intelligent factories based on Cyber-Physical Systems (CPS). In this context, the Reference Architecture Model Industrie 4.0 (RAMI4.0) provides guidelines to develop I4.0 compliant solutions based on industrial standards. As the main RAMI4.0 specification, the Asset Administration Shell (AAS) is a standard digital representation of an industrial asset that plays a pivotal role in enabling interoperable communication among I4.0 components across the value chain. This paper provides an analysis of the current state-of-the-art of implementing AAS, discussing, amongst others, the key enabling technologies used to implement the AAS and the alignment of the research works found in the literature with the I4.0 components criteria.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020. The author Lucas Sakurada thanks the FCT - Fundação para a Ciência e Tecnologia, Portugal, for the PhD Grant DFA/BD/9234/2020.info:eu-repo/semantics/publishedVersio

Engineering a multi-agent systems approach for realizing collaborative asset administration shells

In recent years, there has been a high demand for flexible and reconfigurable processes to meet the fast-changing market conditions. In this context, Industry 4.0 is promoting the digitization of traditional production systems towards intelligent factories with highly automated and rapidly adaptable capabilities based on Industrial Cyber-Physical Systems (ICPS). This digitization process is currently being leveraged by the so-called Asset Administration Shell (AAS), a standardized digital representation of an asset that provides the uniform access to the asset information. Additionally, the AASs offer the digital basis for future autonomous systems, where intelligent AASs may perform collaborative functions to enhance industrial processes. However, currently such solutions are still at an early stage of maturity. In this sense, this paper explores the adoption of a Multi-agent Systems (MAS) approach to provide the required intelligent and collaborative aspects for the traditional AAS. The proposed MAS-based AAS solution was applied in an industrial automation case study to analyze the feasibility of MAS to perform intelligent and collaborative functions in the AAS context.info:eu-repo/semantics/publishedVersio

Digitization of industrial environments through an industry 4.0 compliant approach

About a decade after the introduction of Industry 4.0 (I4.0) as a paradigm oriented towards the digitization of industrial environments, centered on the concept of industrial Cyber-physical Systems (CPS) to enable the development of intelligent and distributed industrial systems, many companies around the world are still not immersed in this digital transformation era. This transition is not straightforward and requires the aligned with the novel technologies, architectures and standards to migrate entire traditional systems into I4.0 systems. In this context, this paper presents an approach to perform the digitization of non-I4.0 components/systems into I4.0 through an approach based on the Asset Administration Shell (AAS), which is a standardized digital representation of an asset. This approach enables to hold the asset information throughout its lifecycle, provides a standard communication interface with the asset, and is based on a set of modules that are combined with the AAS to provide novel functionalities for the asset, e.g., monitoring, diagnosis and optimization. Moreover, this approach adopts Multi-agent Systems (MAS) to provide mainly autonomy and collaborative capabilities to the system. The agents are able to get information from the AASs, making intelligent decisions and perform distributed tasks following interaction strategies, e.g., collaboration, negotiation and self-organization. The feasibility of the proposed approach was tested by digitizing a small-scale production system comprising several assets.The authors are grateful to the Foundation for Science and Technology (FCT), Portugal, for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). The author Lucas Sakurada thanks the FCT for the PhD Grant 2020.09234.BD.info:eu-repo/semantics/publishedVersio

Agent-based asset administration shell approach for digitizing industrial assets

Modern manufacturing systems are facing new challenges related to the fast-changing market conditions, increased global competition and rapid technological developments, imposing strong requirements in terms of flexibility, robustness and reconfigurability. In this context, the Industry 4.0 (I4.0) paradigm relies on digitizing industrial assets to fulfil these requirements. The implementation of this digitization process is being promoted by the so-called Asset Administration Shell (AAS), a digital representation of an asset that complies with standardization and interoperability strategies. At this moment, a significant part of the AAS developments is more focused on the information management of the asset along its lifecycle and not concerned with aspects of intelligence and collaboration, which are fundamental aspects to develop I4.0 compliant solutions. In this sense, this paper presents an agent-based AAS approach for enhancing the digitization process of assets, considering agents to embed distributed intelligence and collaborative functions, service orientation to support interoperability, and holonic principles to provide the system organization. The proposed agent-based AAS was implemented in an industrial automation system aiming to analyze its applicability.info:eu-repo/semantics/publishedVersio

Multi-agent system for monitoring temperature in sensing surfaces including hard and soft sensors

In the digital transformation era, the collection of data assumes a crucial relevance. In some applications, the use of real sensors to measure the target parameters is constrained by technical or economical limitations. In such situations, it is required to use alternative techniques based on soft sensors that acquire data by estimating the measurement of a variable through the correlation of the data acquired by the neighbouring sensors. However, the co-existence of real and soft sensors requires a computational infra-structure that integrates these heterogeneous data sources and supports the synchronisation of the monitoring system based on the inputs of different measurement nodes. Multi-agent systems provide this distributed infra-structure for the data collection, ensuring modularity, scalability and reconfigurability capabilities. This paper introduces a multi-agent system approach to create a modular and scalable sensing system, based on a diversity of real and soft sensors, to support the monitoring of temperature in thin-film sensing surfaces. The proposed approach was experimentally tested in a plastic injection process, presenting promising results in terms of accuracy and response time, and allowing to obtain more sampling points through the use of computational techniques to complement the real data.The work reported in this paper was supported by ONSURF - Mobilizar Competências Tecnológicas em Engenharia de Superfícies, Projeto nº POCI-01-0247-FEDER-024521.info:eu-repo/semantics/publishedVersio

A methodology for integrating asset administration shells and multi-agent systems

Industry 4.0 (I4.0) is promoting the digitization of industrial environments towards intelligent and distributed industrial automation systems based on Cyber-physical Systems (CPS). Currently, this digitization process is being leveraged by the Asset Administration Shell (AAS), which digitally describes an asset in a standardized and semantically unambiguous form throughout its lifecycle. However, more robust solutions based on autonomous AASs endowed with collaborative and intelligent capabilities, also called proactive AASs, are still in the early stages. In this context, Multi-agent Systems (MAS) are a key enabler to provide the required autonomy, intelligence and collaborative capabilities for the AASs. With this in mind, this paper presents a methodology positioned with respect to the Reference Architecture Model Industrie 4.0 (RAMI4.0) layers, which provides guidelines for integrating AASs and MAS, aiming to support the development of proactive AASs. The applicability of the proposed methodology was tested through the integration of AASs and MAS for a smallscale CPS demonstrator.The authors are grateful to the Foundation for Science and Technology (FCT), Portugal, for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). The author Lucas Sakurada thanks the FCT for the PhD Grant 2020.09234.BD.info:eu-repo/semantics/publishedVersio

Learning emergent digital technologies: the experience in the internet of things course unit

Industry 4.0 is re-shaping the way companies and individuals operate, but it is also introducing strong demands in education processes to train professionals with adequate competencies in emergent digital technologies, e.g., Internet of Things (IoT), Artificial Intelligence and collaborative robotics. In the last decade, innovative educational methods are being applied, e.g., problem-based learning and project-based learning, to move the traditional education approach into a more student-centric process where the student has a more active role. Recent studies point out that the combination of such educational methods is beneficial, each one selected according to the particularities of the learning subject and objective. Having this in mind, this paper describes the application of a learning methodology that combines different educational methods, namely face-toface, problem-based learning and project-based learning, in a teaching course unit focusing on IoT technologies. The achieved results show an increase of the student’s assessment performance, motivation and satisfaction, and the opportunity to consolidate their acquired knowledge with hands-on practice. This approach also stimulates the acquisition of soft skills, mainly teamwork, communication, creativity and critical thinking.The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio

Co-design process for upskilling the workforce in the factories of the future

The digital transformation that the world is facing has a strong impact in the professional occupations and job profiles in the factories of the future context, requiring the need of upskilling and re-qualification of the workforce. Taking this into account, an Industrial Collaborative Educational Design (ICoED) is presented comprising three stages and eight steps, and considering a democratic and collaborative participation of the different stakeholders, namely the managers, educators and learners, each one providing its own perspective on the design of the training programme. In this co-design process, the analysis of the skills’ gap is a crucial task to prepare the initial stage of the process, particularly identifying the needs in terms of soft and hard skills. The proposed ICoED process was applied to solve an upskilling problem of an industrial metal stamping company, with the participants performing three workshops to execute the eight steps, reaching a training programme with five modules, each one settled with proper activities, resources and infrastructures.This work is part of the FIT4FoF project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement n. 820701.info:eu-repo/semantics/publishedVersio