Integration of Cutting-Edge Interoperability Approaches in Cyber-Physical Production Systems and Industry 4.0

Interoperability in smart manufacturing refers to how interconnected cyber-physical components exchange information and interact. This is still an exploratory topic, and despite the increasing number of applications, many challenges remain open. This chapter presents an integrative framework to understand common practices, concepts, and technologies used in trending research to achieve interoperability in production systems. The chapter starts with the question of what interoperability is and provides an alternative answer based on influential works in the field, followed by the presentation of important reference mod4els and their relation to smart manufacturing. It continues by discussing different types of interoperability, data formats, and common ontologies necessary for the integration of heterogeneous systems and the contribution of emerging technologies in achieving interoperability. This chapter ends with a discussion of a recent use case and final remarks

Implementation of a Production-Control System Using Integrated Automation ML and OPC UA

In the era of Industry 4.0, manufacturing systems must be well equipped with adaptable fabrication facilities and flexible production patterns to acquire 'plug-And-produce' capabilities for future production lines. However, the conventional engineering mode is so obsolete and inefficient that modern intelligent production requirements cannot be met any more. In this study, the architecture for an industrial process-control system is proposed using integrated AutomationML and OPC UA technologies. A lab-level experimental system was implemented and the system was executed to verify that the proposed architecture is reasonable and feasible in terms of actual performance

Indústria 4.0: desenvolvimento de sistemas de informação

Nos últimos anos, houve uma evolução clara nos fornecedores da indústria automotiva, a qual se deve às constantes exigências impostas pelas OEMs (Fabricantes de Equipamentos Originais). É exigido um nível de qualidade bastante elevado, com uma competitividade dos produtos cada vez maior, existindo também um claro aumento da produtividade neste setor. Assim sendo, existe a necessidade de fazer evoluir os processos com a utilização de tecnologia, acompanhada de uma constante monitorização de dados e informações. A evolução dos métodos de trabalho é então um processo chave nas empresas mais tradicionais, sendo esse progresso gradual, para que os trabalhadores possam ter a oportunidade de evoluir as suas capacidades, promovendo uma simbiose entre mudança tecnológica, conhecimento e mudança de mentalidade. Esta dissertação apresenta uma proposta de implementação de sistemas de controlo da produção segundo requisitos da 4ª Revolução Industrial, na empresa Ficocables, Lda. Estes sistemas recorrem a diferentes tecnologias segmentadas pelas necessidades de cada processo, sendo eles a nível de chão-de-fábrica ou processos não-produtivos. Este projeto tem como objetivo a automatização dos processos, tornando-os mais eficazes, eficientes e fiáveis. Com a implementação de sistemas tecnológicos, foi possível reduzir recursos e aumento o valor do produto, pela diminuição de custos alocados a processos não-produtivos.Over the last few years, there has been a clear trend in the automotive industry suppliers, which was due to the requirements imposed by OEMs (Original Equipment Manufacturers). A fairly high level of quality is required, with the competitiveness of products increasing, as well as a clear increased of productivity in this sector. Therefore, there is a need to enhance and evolve the process using technology accompanied by constant monitoring of data and information. The evolution of working methods is then a key process in the most traditional businesses, making gradual progress, so that workers can have the opportunity to develop their skills, promoting a symbiosis between technological change, knowledge and change of mentality. This dissertation presents a proposal for the implementation of production control systems following the requirements of the 4th Industrial Revolution, into the company Ficocables, Lda. These systems use different targeted technologies for the needs of each process, namely in the level of shop floor, as well as non-productive processes. This project aims at the automation of processes, making them more effective, efficient and reliable. With the implementation of technological systems it was possible to reduce resources and increase the value of the product, by reducing the costs allocated to nonproductive processes

Development of a supervisory internet of things (IoT) system for factories of the future

Big data is of great importance to stakeholders, including manufacturers, business partners, consumers, government. It leads to many benefits, including improving productivity and reducing the cost of products by using digitalised automation equipment and manufacturing information systems. Some other benefits include using social media to build the agile cooperation between suppliers and retailers, product designers and production engineers, timely tracking customers’ feedbacks, reducing environmental impacts by using Internet of Things (IoT) sensors to monitor energy consumption and noise level. However, manufacturing big data integration has been neglected. Many open-source big data software provides complicated capabilities to manage big data software for various data-driven applications for manufacturing. In this research, a manufacturing big data integration system, named as Data Control Module (DCM) has been designed and developed. The system can securely integrate data silos from various manufacturing systems and control the data for different manufacturing applications. Firstly, the architecture of manufacturing big data system has been proposed, including three parts: manufacturing data source, manufacturing big data ecosystem and manufacturing applications. Secondly, nine essential components have been identified in the big data ecosystem to build various manufacturing big data solutions. Thirdly, a conceptual framework is proposed based on the big data ecosystem for the aim of DCM. Moreover, the DCM has been designed and developed with the selected big data software to integrate all the three varieties of manufacturing data, including non-structured, semi-structured and structured. The DCM has been validated on three general manufacturing domains, including product design and development, production and business. The DCM cannot only be used for the legacy manufacturing software but may also be used in emerging areas such as digital twin and digital thread. The limitations of DCM have been analysed, and further research directions have also been discussed