Evaluation of Cognitive Architectures for Cyber-Physical Production Systems

Cyber-physical production systems (CPPS) integrate physical and computational resources due to increasingly available sensors and processing power. This enables the usage of data, to create additional benefit, such as condition monitoring or optimization. These capabilities can lead to cognition, such that the system is able to adapt independently to changing circumstances by learning from additional sensors information. Developing a reference architecture for the design of CPPS and standardization of machines and software interfaces is crucial to enable compatibility of data usage between different machine models and vendors. This paper analysis existing reference architecture regarding their cognitive abilities, based on requirements that are derived from three different use cases. The results from the evaluation of the reference architectures, which include two instances that stem from the field of cognitive science, reveal a gap in the applicability of the architectures regarding the generalizability and the level of abstraction. While reference architectures from the field of automation are suitable to address use case specific requirements, and do not address the general requirements, especially w.r.t. adaptability, the examples from the field of cognitive science are well usable to reach a high level of adaption and cognition. It is desirable to merge advantages of both classes of architectures to address challenges in the field of CPPS in Industrie 4.0

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

Advancing Smart Manufacturing in Europe: Experiences from Two Decades of Research and Innovation Projects

In the past two decades, a large amount of attention has been devoted to the introduction of smart manufacturing concepts and technologies into industrial practice. In Europe, these efforts have been supported by European research and innovation programs, bringing together research and application parties. In this paper, we provide an overview of a series of four content-wise connected projects on the European scale that are aimed at advancing smart manufacturing, with a focus on connecting processes on smart factory shop floors to manufacturing equipment on the one hand and enterprise-level business processes on the other hand. These projects cover several tens of application cases across Europe. We present our experiences in the form of a single, informal longitudinal case study, highlighting both the major advances and the current limitations of developments. To organize these experiences, we place them in the context of the well-known RAMI4.0 reference framework for Industry 4.0 (covering the ISA-95 standard). Then, we analyze the experiences, both the positive ones and those including problems, and draw our learnings from these. In doing so, we do not present novel technological developments in this paper—these are presented in the papers we refer to—but concentrate on the main issues we have observed to guide future developments in research efforts and industrial innovation in the smart industry domain

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

A Knowledge Graph Based Integration Approach for Industry 4.0

The fourth industrial revolution, Industry 4.0 (I40) aims at creating smart factories employing among others Cyber-Physical Systems (CPS), Internet of Things (IoT) and Artificial Intelligence (AI). Realizing smart factories according to the I40 vision requires intelligent human-to-machine and machine-to-machine communication. To achieve this communication, CPS along with their data need to be described and interoperability conflicts arising from various representations need to be resolved. For establishing interoperability, industry communities have created standards and standardization frameworks. Standards describe main properties of entities, systems, and processes, as well as interactions among them. Standardization frameworks classify, align, and integrate industrial standards according to their purposes and features. Despite being published by official international organizations, different standards may contain divergent definitions for similar entities. Further, when utilizing the same standard for the design of a CPS, different views can generate interoperability conflicts. Albeit expressive, standardization frameworks may represent divergent categorizations of the same standard to some extent, interoperability conflicts need to be resolved to support effective and efficient communication in smart factories. To achieve interoperability, data need to be semantically integrated and existing conflicts conciliated. This problem has been extensively studied in the literature. Obtained results can be applied to general integration problems. However, current approaches fail to consider specific interoperability conflicts that occur between entities in I40 scenarios. In this thesis, we tackle the problem of semantic data integration in I40 scenarios. A knowledge graphbased approach allowing for the integration of entities in I40 while considering their semantics is presented. To achieve this integration, there are challenges to be addressed on different conceptual levels. Firstly, defining mappings between standards and standardization frameworks; secondly, representing knowledge of entities in I40 scenarios described by standards; thirdly, integrating perspectives of CPS design while solving semantic heterogeneity issues; and finally, determining real industry applications for the presented approach. We first devise a knowledge-driven approach allowing for the integration of standards and standardization frameworks into an Industry 4.0 knowledge graph (I40KG). The standards ontology is used for representing the main properties of standards and standardization frameworks, as well as relationships among them. The I40KG permits to integrate standards and standardization frameworks while solving specific semantic heterogeneity conflicts in the domain. Further, we semantically describe standards in knowledge graphs. To this end, standards of core importance for I40 scenarios are considered, i.e., the Reference Architectural Model for I40 (RAMI4.0), AutomationML, and the Supply Chain Operation Reference Model (SCOR). In addition, different perspectives of entities describing CPS are integrated into the knowledge graphs. To evaluate the proposed methods, we rely on empirical evaluations as well as on the development of concrete use cases. The attained results provide evidence that a knowledge graph approach enables the effective data integration of entities in I40 scenarios while solving semantic interoperability conflicts, thus empowering the communication in smart factories

Adaptive and Dynamic Feedback Loops between Production System and Production Network based on the Asset Administration Shell

In production networks, production must run efficiently across company boundaries. Companies must be able to react quickly as a single unit. Two trends are influencing this situation: On the one hand, the progressing servitization leads to the increased offering of digital services in the field of manufacturing. From the literature, it is known that digital services let manufacturers, suppliers, and industrial customers interact more closely and frequently in a production network. On the other hand, the concept of the digital twin is trending. It promises the real-time prognosis and control of production systems. Although the concept of the digital twin itself can be vague there are some technologies trying to implement the digital twin of production. The asset administration shell (AAS) is an example of such a technology that draws growing attention. Picking up the initial situation these two trends could be used to create a feedback loop between the production system and network and thus improve the overall efficiency in production networks. Based on this idea, the paper first presents an approach to model systematically a possibility for a feedback loop orienting to the business model concept. Second, a reference architecture is derived from the RAMI 4.0 standard. The specified reference architecture is the basis for the specific implementation. Third, a procedure is developed to implement a specific architecture. For implementing an architecture, the usage of the asset administration shell is assumed. Finally, the approach is validated in a use case from the high precision weight industry

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

Asset Administration Shell as an interoperable enabler of Industry 4.0 software architectures: a case study

In recent years, the discipline of Digital Transformation in manufacturing companies turned out to be a hot topic of research debate, which allowed the design and introduction of new technologies and tools able to exploit the potential of the data produced by the shop floor assets. This increased interest in data generation and management has however highlighted a crucial issue about the lack of standardised models and structures to share these data and ensure interoperability. Among the several concepts proposed by the recent initiatives devoted to solving or mitigating this issue, Asset Administration Shell (AAS) is increasing in popularity, given its potential in providing standardised and modular information about the assets and events represented. This paper deals with a demonstration of the easiness of integration of AAS in pre-existing software architecture, allowing higher flexibility and a better understanding of the ongoing processes: a production line has been indeed entirely represented with modular AAS metamodels and it has been used to feed a Digital Model representing the line configuration. The use case proposed proves the effectiveness of the obtained solution when used for virtual commissioning operations