Developing Function Blocks for Collecting Data and Integrating Legacy Systems in Manufacturing and Logistics

Manufacturing enterprises have been increasingly technology-driven during recent decades. Industry 4.0 promotes smart manufacturing and intelligent systems which can seamlessly communicate with each other and enable decentralized decision-making by monitoring the factory-floor process. This calls for the Information Communication Technologies (ICT) infrastructure to be effectively incorporated with the industries. Industry 4.0 presents the concept of “smart factory” in which Cyber-Physical-Systems (CPS) fuses the physical systems with the Internet of Things (IoT), enabling higher levels of interoperability and Information transparency. However, manufacturing enterprises in the recent past have characterized their efficiency by how prominently and adequately they adopt and utilize their IT solutions and how feasible those solutions are to integrate with their legacy systems. Enterprise Integration, in particular, has become more challenging owing to the highly dynamic manufacturing environment. System integration has become an indispensable field to be addressed , especially when the industry adopts connected enterprise paradigm. Connected enterprise systems enable industries to leverage their technologies to collect, analyze and refine their data to help them make better business decisions. In a recent trend, IT systems in manufacturing are majorly driven towards the cloud and collaborative solutions as a result of the exponential growth of internet technologies and their ability to adapt to rapid changes in the market. Collaborative frameworks are widely preferred by the enterprises as they enable better communication, increases productivity and improve business execution. They are critical for a business to function with agility in this fast pacing and changing world. One such platform is provided by the Cloud Collaborative Manufacturing Networks (C2NET) project that optimizes the supply network of manufacturing and logistics assets. This thesis research proposes an approach to integrate heterogeneous legacy systems by showcasing an implementation which favors robust data collection. This implementation is made possible by adopting Production Logistics and Sustainability Cockpit (PLANTCockpit) Open Source solution, which functions as a viable interface for real-time data collection and data-logistics thus enhancing the process optimization of the manufacturing enterprise. PLANTCockpit OS is a modular solution which enables to build and deploy flexible loosely coupled entities known as Function Blocks (FBs) that facilitate seamless legacy system integration and robust information exchange between the systems. This thesis also fulfills the C2NET project requirement to define the possibility of effective integration of PLANTCockpit OS in the C2NET reference architecture

Medição de desempenho de equipamentos legados por meio de sua conexão à nuvem

Orientador: Prof. Dr. Fernando DeschampsCoorientadores: Prof. Dr. Pablo Devid Valle, Prof(a). Dr(a). Silvana P. DetroDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Manufatura. Defesa : Curitiba, 13/12/2021Inclui referências: p. 85-98Resumo: A internet Industrial das coisas (IIoT) vem criando um mundo de conhecimento compartilhado, conectado, ligados as inovações e essas transformações digitais vem possibilitando conexão dos sistemas de manufaturas à nuvem. O resultado desta conexão é a geração de informações por meio de dados de máquinas, para contribuir com as tomadas de decisões na gestão de manufatura e estratégias industriais. Um desafio, para as pequenas e médias indústrias manufatureiras, que apresentam dificuldades em utilizar dados de máquinas para o gerenciamento das operações, devido à baixa adoção de tecnologias por compor um sistema legado de manufatura. Neste contexto, a pesquisa foca como objetivo na medição de desempenho de equipamentos legados por meio de sua conexão à nuvem. A pesquisa foi abordada pelo método Design Science Research (DSR), a qual concentra-se no desenvolvimento e na avaliação da aplicação do artefato (sistema de medição) por meio de base tecnológica. Com o objetivo explícito para projetar alternativas de soluções de problemas, tanto no desenvolvimento, quanto na aplicação. Na aplicação da Arquitetura em um sistema real de manufatura, a pesquisa apresentou resultados satisfatórios através da acuracidade obtida de 86% na medição realizada. No que tange, a medição de desempenho do equipamento, o índice medido alcançou um OEE de 27% de eficiência, o que é considerado baixo perante a média de empresas com manufatura de classe mundial. Conclui-se, que os resultados possibilitaram a verificação da confiabilidade das informações geradas pela arquitetura de aplicação e a medição do desempenho do equipamento por meio de sua aplicabilidade em um cenário real de manufatura com foco na gestão de manufatura.Abstract: The Industrial Internet of Things (IIoT) has been creating a world of shared, connected knowledge, linked to innovations, and these digital transformations have enabled the connection of manufacturing systems to the cloud. The result of this connection is the generation of information through machine data, to assist managers in their decision-making in the management of industrial operations and strategies. A challenge for small and medium manufacturing industries, which have difficulties in using machine data to manage operations, due to the low adoption of technologies for composing a legacy manufacturing system. In this context, the research focuses as an objective on measuring the performance of legacy equipment through its connection to the cloud. The research was applied through Design Science Research (DSR), which focuses on the development and evaluation of the application of the artifact (measurement system) through a technology basis, with the explicit objective of designing alternative problem solutions, both in development and application. In the application of architecture in a real manufacturing system, the research showed satisfactory results through the obtained accuracy of 86% in the measurement performed. Regarding the equipment performance measurement, the measured index reached an OEE of 27% efficiency, which is considered low compared to the average of companies with world-class manufacturing. It is concluded that the results made it possible to verify the reliability of the information generated by the application architecture and to measure the performance of the equipment through its applicability in a real manufacturing scenario with a focus on manufacturing management