DIN Spec 91345 RAMI 4.0 compliant data pipelining: An approach to support data understanding and data acquisition in smart manufacturing environments

Today, data scientists in the manufacturing domain are confronted with a set of challenges associated to data acquisition as well as data processing including the extraction of valuable in-formation to support both, the work of the manufacturing equipment as well as the manufacturing processes behind it. One essential aspect related to data acquisition is the pipelining, including various commu-nication standards, protocols and technologies to save and transfer heterogenous data. These circumstances make it hard to understand, find, access and extract data from the sources depend-ing on use cases and applications. In order to support this data pipelining process, this thesis proposes the use of the semantic model. The selected semantic model should be able to describe smart manufacturing assets them-selves as well as to access their data along their life-cycle. As a matter of fact, there are many research contributions in smart manufacturing, which already came out with reference architectures or standards for semantic-based meta data descrip-tion or asset classification. This research builds upon these outcomes and introduces a novel se-mantic model-based data pipelining approach using as a basis the Reference Architecture Model for Industry 4.0 (RAMI 4.0).Hoje em dia, os cientistas de dados no domínio da manufatura são confrontados com várias normas, protocolos e tecnologias de comunicação para gravar, processar e transferir vários tipos de dados. Estas circunstâncias tornam difícil compreender, encontrar, aceder e extrair dados necessários para aplicações dependentes de casos de utilização, desde os equipamentos aos respectivos processos de manufatura. Um aspecto essencial poderia ser um processo de canalisação de dados incluindo vários normas de comunicação, protocolos e tecnologias para gravar e transferir dados. Uma solução para suporte deste processo, proposto por esta tese, é a aplicação de um modelo semântico que descreva os próprios recursos de manufactura inteligente e o acesso aos seus dados ao longo do seu ciclo de vida. Muitas das contribuições de investigação em manufatura inteligente já produziram arquitecturas de referência como a RAMI 4.0 ou normas para a descrição semântica de meta dados ou classificação de recursos. Esta investigação baseia-se nestas fontes externas e introduz um novo modelo semântico baseado no Modelo de Arquitectura de Referência para Indústria 4.0 (RAMI 4.0), em conformidade com a abordagem de canalisação de dados no domínio da produção inteligente como caso exemplar de utilização para permitir uma fácil exploração, compreensão, descoberta, selecção e extracção de dados

A DIN Spec 91345 RAMI 4.0 Compliant Data Pipelining Model: An Approach to Support Data Understanding and Data Acquisition in Smart Manufacturing Environments

Today, data scientists in the manufacturing domain are confronted with various communication standards, protocols and technologies to save and transfer various kinds of data. These circumstances makes it hard to understand, find, access and extract data needed for use case depended applications. One solution could be a data pipelining approach enforced by a semantic model which describes smart manufacturing assets itself and the access to their data along their life-cycle. Many research contributions in smart manufacturing already came out with with reference architectures like the RAMI 4.0 or standards for meta data description or asset classification. Our research builds upon these outcomes and introduces a semantic model based DIN Spec 91345 (RAMI 4.0) compliant data pipelining approach with the smart manufacturing domain as exemplary use case. This paper has a focus on the developed semantic model used to enable an easy data exploration, finding, access and extraction of data, compatible with various used communication standards, protocols and technologies used to save and transfer data.publishersversionpublishe

Semantic Asset Administration Shells in Industry 4.0: A Survey

The Asset Administration Shell (AAS) is a fundamental concept in the Reference Architecture Model for Industry 4.0 (RAMI 4.0), that provides a virtual and digital representation of all information and functions of a physical asset in a manufacturing environment. Recently, Semantic AASs have emerged that add knowledge representation formalisms to enhance the digital representation of physical assets. In this paper, we provide a comprehensive survey of the scientific contributions to Semantic AASs that model the Information and Communication Layer within RAMI 4.0, and summarise and demonstrate their structure, communication, functionalities, and use cases. We also highlight the challenges of future development of Semantic AASs

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

How to Design Scheduling Solutions for Smart Manufacturing Environments Using RAMI 4.0?

Publisher Copyright: © 2013 IEEE. This work was supported in part by the European Union (EU) Project Advanced Manufacturing Solutions Tightly Aligned With Business Needs (AVANGARD), and in part by the European Union's Horizon 2020 Research and Innovation Program under Grant 869986.The scheduling applied to manufacturing represents a huge opportunity for companies to stand out in a world of fast and big changes. Having a reliable scheduling system will allow factories to deal with the significant demand for highly customized products. Although manufacturing scheduling has been deeply studied for decades, there is still a gap between academia and industry, namely because the lack of flexibility and homogeneity among scheduling solutions, which makes them very use case-oriented. Furthermore, the absence of standardization is also making it difficult to implement smart scheduling solutions in industrial scenarios. Thus, this work presents a set of requirements and design principles based on axiomatic design concept, to make the first steps to standardize the designing and development of manufacturing scheduling solutions in the context of Industry 4.0. At the end, is presented a scheduling generic framework targeting smart manufacturing and evaluated in a practical use case.publishersversionpublishe

A conceptual framework for smart production planning and control in Industry 4.0

[EN] This article aims to introduce the challenge (i.e., integration of new collaborative models and tools) posed by the automation and collaboration of industrial processes in Industry 4.0 (I4.0) smart factories. Small- and medium-sized enterprises (SMEs) are particularly confronted with new technological and organisational changes, but a conceptual framework for production planning and control (PPC) systems in the I4.0 context is lacking. The main contributions of this article are to: (i) identify the functions making up traditional PPC and smart production planning and control in I4.0 (SPPC 4.0); (ii) analyse the impact of I4.0 technologies on PPC systems; (iii) propose a conceptual framework that provides the systematic structuring of how a PPC system operates in the I4.0 context, dubbed SPPC 4.0. Thus SPPC 4.0 is proposed by adopting the axes of the RAMI 4.0 reference architecture model, which compiles and contains the main concepts of PPC systems and I4.0. It also provides the technical description, organisation and understanding of each aspect, which can provide a guide for academic research and industrial practitioners to transform PPC systems towards I4.0 implementations. Finally, theoretical implications and research gaps are provided.The research leading to these results received funding from the European Union H2020 Program with grant agreements No. 958205 "Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)" and No. 825631 "Zero-Defect Manufacturing Platform (ZDMP)"; the "Industrial Production and Logistics Optimization in Industry 4.0" (i4OPT) (Ref. PROMETEO/2021/065) project granted by the Valencian Regional Government; and the PAI-12-21 open-access support from the Universitat Politecnica de Valencia.Cañas, H.; Mula, J.; Campuzano-Bolarín, F.; Poler, R. (2022). A conceptual framework for smart production planning and control in Industry 4.0. Computers & Industrial Engineering. 173:1-12. https://doi.org/10.1016/j.cie.2022.10865911217

Reference Network and Localization Architecture for Smart Manufacturing based on 5G

5G promises to shift Industry 4.0 to the next level by allowing flexible production. However, many communication standards are used throughout a production site, which will stay so in the foreseeable future. Furthermore, localization of assets will be equally valuable in order to get to a higher level of automation. This paper proposes a reference architecture for a convergent localization and communication network for smart manufacturing that combines 5G with other existing technologies and focuses on high-mix low-volume application, in particular at small and medium-sized enterprises. The architecture is derived from a set of functional requirements, and we describe different views on this architecture to show how the requirements can be fulfilled. It connects private and public mobile networks with local networking technologies to achieve a flexible setup addressing many industrial use cases.Comment: 10 pages; submitted to 6th International Conference on System-Integrated Intelligence. Intelligent, flexible and connected systems in products and production, 7-9 September Genova, Ital