365 research outputs found
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
Knowledge Management in the Fourth Industrial Revolution: Mapping the Literature and Scoping Future Avenues
Due to increased competitive pressure, modern organizations tend to rely on knowledge and its exploitation to sustain a long-term advantage. This calls for a precise understanding of knowledge management (KM) processes and, specifically, how knowledge is created, shared/transferred, acquired, stored/retrieved, and applied throughout an organizational system. However, since the beginning of the new millennium, such KM processes have been deeply affected and molded by the advent of the fourth industrial revolution, also called Industry 4.0, which involves the interconnectedness of machines and their ability to learn and share data autonomously. For this reason, the present study investigates the intellectual structure and trends of KM in Industry 4.0. Bibliometric analysis and a systematic literature review are conducted on a total of 90 relevant articles. The results reveal 6 clusters of keywords, subsequently explored via a systematic literature review to identify potential stream of this emergent field and future research avenues capable of producing meaningful advances in managerial knowledge of Industry 4.0 and its consequences
Supporting adaptiveness of cyber-physical processes through action-based formalisms
Cyber Physical Processes (CPPs) refer to a new generation of business processes enacted in many application environments (e.g., emergency management, smart manufacturing, etc.), in which the presence of Internet-of-Things devices and embedded ICT systems (e.g., smartphones, sensors, actuators) strongly influences the coordination of the real-world entities (e.g., humans, robots, etc.) inhabitating such environments. A Process Management System (PMS) employed for executing CPPs is required to automatically adapt its running processes to anomalous situations and exogenous events by minimising any human intervention. In this paper, we tackle this issue by introducing an approach and an adaptive Cognitive PMS, called SmartPM, which combines process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on three well-established action-based formalisms developed for reasoning about actions in Artificial Intelligence (AI), including the situation calculus, IndiGolog and automated planning. Interestingly, the use of SmartPM does not require any expertise of the internal working of the AI tools involved in the system
Cyber-physical production systems: Roots, expectations and R&D challenges
One of the most significant directions in the development of computer science and information and communication technologies is represented by Cyber-Physical Systems (CPSs) which are systems of collaborating computational entities which are in intensive connection with the surrounding physical world and its on-going processes, providing and using, at the same time, data-Accessing and data-processing services available on the internet. Cyber-Physical Production Systems (CPPSs), relying on the newest and foreseeable further developments of computer science, information and communication technologies on the one hand, and of manufacturing science and technology, on the other, may lead to the 4th Industrial Revolution, frequently noted as Industry 4.0. The key-note will underline that there are significant roots generally -And particularly in the CIRP community -which point towards CPPSs. Expectations and the related new R&D challenges will be outlined. © 2014 Elsevier B.V. This is an open access article under the CC BY-NC-ND license
A Semantic Interoperability Model Based on the IEEE 1451 Family of Standards Applied to the Industry 4.0
The Internet of Things (IoT) has been growing recently. It is a concept for connecting
billions of smart devices through the Internet in different scenarios. One area being
developed inside the IoT in industrial automation, which covers Machine-to-Machine (M2M) and industrial communications with an automatic process, emerging the
Industrial Internet of Things (IIoT) concept. Inside the IIoT is developing the concept of
Industry 4.0 (I4.0). That represents the fourth industrial revolution and addresses the
use of Internet technologies to improve the production efficiency of intelligent services
in smart factories. I4.0 is composed of a combination of objects from the physical world and the digital world that offers dedicated functionality and flexibility inside and outside of an I4.0 network.
The I4.0 is composed mainly of Cyber-Physical Systems (CPS). The CPS is the integration
of the physical world and its digital world, i.e., the Digital Twin (DT). It is responsible for realising the intelligent cross-link application, which operates in a self-organised and
decentralised manner, used by smart factories for value creation. An area where the CPS
can be implemented in manufacturing production is developing the Cyber-Physical Production System (CPPS) concept. CPPS is the implementation of Industry 4.0 and CPS in manufacturing and production, crossing all levels of production between the
autonomous and cooperative elements and sub-systems. It is responsible for connecting
the virtual space with the physical world, allowing the smart factories to be more intelligent, resulting in better and smart production conditions, increasing productivity,
production efficiency, and product quality. The big issue is connecting smart devices with
different standards and protocols. About 40% of the benefits of the IoT cannot be
achieved without interoperability. This thesis is focused on promoting the
interoperability of smart devices (sensors and actuators) inside the IIoT under the I4.0 context.
The IEEE 1451 is a family of standards developed to manage transducers. This standard reaches the syntactic level of interoperability inside Industry 4.0. However, Industry 4.0
requires a semantic level of communication not to exchange data ambiguously. A new
semantic layer is proposed in this thesis allowing the IEEE 1451 standard to be a complete framework for communication inside the Industry 4.0 to provide an interoperable network interface with users and applications to collect and share the data from the industry field.A Internet das Coisas tem vindo a crescer recentemente. É um conceito que permite
conectar bilhões de dispositivos inteligentes através da Internet em diferentes cenários.
Uma área que está sendo desenvolvida dentro da Internet das Coisas é a automação
industrial, que abrange a comunicação máquina com máquina no processo industrial de
forma automática. Essa interligação, representa o conceito da Internet das Coisas
Industrial. Dentro da Internet das Coisas Industrial está a desenvolver o conceito de
Indústria 4.0 (I4.0). Isso representa a quarta revolução industrial que aborda o uso de
tecnologias utilizadas na Internet para melhorar a eficiência da produção de serviços em
fábricas inteligentes. A Indústria 4.0 é composta por uma combinação de objetos do
mundo físico e do mundo da digital que oferece funcionalidade dedicada e flexibilidade
dentro e fora de uma rede da Indústria 4.0.
O I4.0 é composto principalmente por Sistemas Ciberfísicos. Os Sistemas Ciberfísicos
permitem a integração do mundo físico com seu representante no mundo digital, por
meio do Gémeo Digital. Sistemas Ciberfísicos são responsáveis por realizar a aplicação
inteligente da ligação cruzada, que opera de forma auto-organizada e descentralizada,
utilizada por fábricas inteligentes para criação de valor. Uma área em que o Sistema
Ciberfísicos pode ser implementado na produção manufatureira, isso representa o
desenvolvimento do conceito Sistemas de Produção Ciberfísicos. Esse sistema é a
implementação da Indústria 4.0 e Sistema Ciberfísicos na fabricação e produção. A
cruzar todos os níveis desde a produção entre os elementos e subsistemas autónomos e
cooperativos. Ele é responsável por conectar o espaço virtual com o mundo físico,
permitindo que as fábricas inteligentes sejam mais inteligentes, resultando em condições
de produção melhores e inteligentes, aumentando a produtividade, a eficiência da
produção e a qualidade do produto. A grande questão é como conectar dispositivos
inteligentes com diferentes normas e protocolos. Cerca de 40% dos benefícios da Internet
das Coisas não podem ser alcançados sem interoperabilidade. Esta tese está focada em
promover a interoperabilidade de dispositivos inteligentes (sensores e atuadores) dentro
da Internet das Coisas Industrial no contexto da Indústria 4.0.
O IEEE 1451 é uma família de normas desenvolvidos para gerenciar transdutores. Esta
norma alcança o nível sintático de interoperabilidade dentro de uma indústria 4.0. No
entanto, a Indústria 4.0 requer um nível semântico de comunicação para não haver a
trocar dados de forma ambígua. Uma nova camada semântica é proposta nesta tese
permitindo que a família de normas IEEE 1451 seja um framework completo para
comunicação dentro da Indústria 4.0. Permitindo fornecer uma interface de rede
interoperável com utilizadores e aplicações para recolher e compartilhar os dados dentro
de um ambiente industrial.This thesis was developed at the Measurement and Instrumentation Laboratory (IML)
in the University of Beira Interior and supported by the portuguese project INDTECH
4.0 – Novas tecnologias para fabricação, que tem como objetivo geral a conceção e
desenvolvimento de tecnologias inovadoras no contexto da Indústria 4.0/Factories of the Future (FoF), under the number POCI-01-0247-FEDER-026653
Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor
The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities
Towards a Service-Oriented Architecture for Production Planning and Control: A Comprehensive Review and Novel Approach
The trends of shorter product lifecycles, customized products, and volatile market environments require manufacturers to reconfigure their production increasingly frequent to maintain competitiveness and customer satisfaction. More frequent reconfigurations, however, are linked to increased efforts in production planning and control (PPC). This poses a challenge for manufacturers, especially in regard of demographic change and shortage of qualified labour, since many tasks in PPC are performed manually by domain experts. Following the paradigm of software-defined manufacturing, this paper targets to enable a higher degree of automation and interoperability in PPC by applying the concepts of service-oriented architecture. As a result, production planners are empowered to orchestrate tasks in PPC without consideration of underlying implementation details. At first, it is investigated how tasks in PPC can be represented as services with the aim of encapsulation and reusability. Secondly, a software architecture based on asset administration shells is presented that allows connection to production data sources and enables integration and usage of such PPC services. In this sense, an approach for mapping asset administrations shells to OpenAPI Specifications is proposed for interoperable and semantic integration of existing services and legacy systems. Lastly, challenges and potential solutions for data integration are discussed considering the present heterogeneity of data sources in manufacturing
Integration of Domain Expert-Centric Ontology Design into the CRISP-DM for Cyber-Physical Production Systems
In the age of Industry 4.0 and Cyber-Physical Production Systems (CPPSs) vast
amounts of potentially valuable data are being generated. Methods from Machine
Learning (ML) and Data Mining (DM) have proven to be promising in extracting
complex and hidden patterns from the data collected. The knowledge obtained can
in turn be used to improve tasks like diagnostics or maintenance planning.
However, such data-driven projects, usually performed with the Cross-Industry
Standard Process for Data Mining (CRISP-DM), often fail due to the
disproportionate amount of time needed for understanding and preparing the
data. The application of domain-specific ontologies has demonstrated its
advantageousness in a wide variety of Industry 4.0 application scenarios
regarding the aforementioned challenges. However, workflows and artifacts from
ontology design for CPPSs have not yet been systematically integrated into the
CRISP-DM. Accordingly, this contribution intends to present an integrated
approach so that data scientists are able to more quickly and reliably gain
insights into the CPPS. The result is exemplarily applied to an anomaly
detection use case
IDARTS – Towards intelligent data analysis and real-time supervision for industry 4.0
The manufacturing industry represents a data rich environment, in which larger and larger volumes of data are constantly being generated by its processes. However, only a relatively small portion of it is actually taken advantage of by manufacturers. As such, the proposed Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework presents the guidelines for the implementation of scalable, flexible and pluggable data analysis and real-time supervision systems for manufacturing environments. IDARTS is aligned with the current Industry 4.0 trend, being aimed at allowing manufacturers to translate their data into a business advantage through the integration of a Cyber-Physical System at the edge with cloud computing. It combines distributed data acquisition, machine learning and run-time reasoning to assist in fields such as predictive maintenance and quality control, reducing the impact of disruptive events in production.info:eu-repo/semantics/publishedVersio
- …