The data-oriented paradigm has proven to be fundamental for the technological transformation process that characterizes Industry 4.0 (I4.0) so that big data and analytics is considered a technological pillar of this process. The goal of I4.0 is the implementation of the so-called Smart Factory, characterized by Intelligent Manufacturing Systems (IMS) that overcome traditional manufacturing systems in terms of efficiency, flexibility, level of integration, digitalization, and intelligence. The literature reports a series of system architecture proposals for IMS, which are primarily data driven. Many of these proposals treat data storage solutions as mere entities that support the architecture’s functionalities. However, choosing which logical data model to use can significantly affect the performance of the IMS. This work identifies the advantages and disadvantages of relational (SQL) and non-relational (NoSQL) data models for I4.0, considering the nature of the data in this process. The characterization of data in the context of I4.0 is based on the five dimensions of big data and a standardized format for representing information of assets in the virtual world, the Asset Administration Shell. This work allows identifying appropriate transactional properties and logical data models according to the volume, variety, velocity, veracity, and value of the data. In this way, it is possible to describe the suitability of relational and NoSQL databases for different scenarios within I4.0.
One of the foundations for Industry 4.0 is the integration of various industrial elements (i.e. sensors, machines, and services) so that these devices can decide in a relatively autonomous way the level of integration which will be adopted. Thus, it is important to understand how the communication Machine to Machine is effectively realized and how these data can be explored and used to enhance the manufacturing process. The exchange of information between machines in the industrial process represents a potential to acquire and analyze a mass of data characterized as "big data", which can be perceived as an opportunity to discuss the paradigms of the industrial systems. Therefore, the purpose of this research is to identify the requirements for the Machine to Machine communication and the use of this data/information for more complexes analyzes using big data and analytics techniques. The KAOS methodology was utilized to model these requirements.
As mudanças constantes e as incertezas do mercado discutidas no contexto da Indústria 4.0 exigem sistemas de controle produtivos mais flexíveis e modulares permitindo mudança rápida na cadeia produtiva descentralizada. Entretanto, os sistemas de controle tradicionais (legados) tornam-se rígidos, concebidos por meio de arquiteturas hierárquicas específicas, que não respondem mais de forma eficiente quando comparado ao que pode ser realizado diante do paradigma baseado em I4.0. Além disso, para que empresas possam responder prontamente a essas mudanças, os sistemas produtivos distribuídos devem estar integrados utilizando diferentes tecnologias. Para integrar esses ambientes heterogêneos, aproximando os sistemas de fabricação real ao plano de fabricação de produtos, novos modelos de arquiteturas são introduzidos na indústria habilitados pelo crescente avanço tecnológico de Tecnologia da Informação e Comunicação (TIC), Internet das Coisas (IoT) e arquiteturas orientada a serviços (SOA), as quais através de metodologias de Controle inteligente como Sistemas Multi-Agentes (MAS) habilitam os Sistemas de produção cyber físicos (CPPS). Essa abordagem de interoperabilidade vêm sendo discutida na Indústria 4.0 através da representação de seus ativos (AAS) que descreve uma nova padronização capaz de contribuir para a migração global de diferentes sistemas industriais. Neste contexto, este trabalho apresenta uma proposta para o desenvolvimento de novas soluções de controle baseadas em I4.0 para sistemas legados de manufatura. Para tal, este trabalho propõe a comunicação e integração de sistemas legados de manufatura com o uso destes novos conceitos, sendo a proposta aplicada em um estudo de caso real.
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