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Digital Manufacturing and Design Training Network
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Integration of cutting-edge interoperability approaches in cyber-physical production systems and industry 4.0
Publication . Estrada-Jimenez, Luis Alberto; Pulikottil, Terrin; Hien, Nguyen Ngoc; Torayev, Agajan; Rehman, Hamood Ur; Mo, Fan; Hojjati, Sanaz Nikghadam; Barata, José; CTS - Centro de Tecnologia e Sistemas; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
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 models 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.
Immune system inspired smart maintenance framework
Publication . Pulikottil, Terrin; Martínez-Arellano, Giovanna; Barata, José; DEE - Departamento de Engenharia Electrotécnica e de Computadores; CTS - Centro de Tecnologia e Sistemas; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; Springer Science Business Media
As the manufacturing industry is moving towards the fourth industrial revolution, there is an increasing need for smart maintenance systems that could provide manufacturers with a competitive advantage by predicting failures. Despite various efforts by researchers, there are still challenges for these systems to work reliably in the industry such as lack of adaptability, resilience, reaction to disturbances, and future-proofing. Bio-inspired frameworks like artificial immune systems provide an alternative approach to satisfying these challenges. But existing immune-based frameworks focus only on adaptive immunity characteristics and ignore innate immunity which is important for quick detection and faster response. There is a need for a holistic view of the immune system in developing an adaptive & resilient maintenance framework. This paper presents a holistic view of the human immune system with a focus on the intelligence & response mechanism of both innate & adaptive immunity. Inspired by this holistic view and considering the emerging computer technologies — Internet of Things, Edge & Cloud Computing, Multi-Agent System, Ontology, Big Data, Digital Twin, Machine Learning, and Augmented Reality — we present a smart maintenance framework. The proposed framework is used for tool condition monitoring to demonstrate its implementation.
Big Data Life Cycle in Shop-Floor-Trends and Challenges
Publication . Pulikottil, Terrin; Estrada-Jimenez, Luis A.; Abadia, José Joaquín Peralta; Carrera-Rivera, Angela; Torayev, Agajan; Rehman, Hamood Ur; Mo, Fan; Nikghadam-Hojjati, Sanaz; Barata, José; DEE - Departamento de Engenharia Electrotécnica e de Computadores; CTS - Centro de Tecnologia e Sistemas; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; DEE2010-C2 Robótica e Manufactura Integrada por Computador; Institute of Electrical and Electronics Engineers (IEEE)
Big data is defined as a large set of data that could be structured or unstructured. In manufacturing shop-floor, big data incorporates data collected at every stage of the production process. This includes data from machines, connecting devices, and even manufacturing operators. The large size of the data available on the manufacturing shop-floor presents a need for the establishment of tools and techniques along with associated best practices to leverage the advantage of data-driven performance improvement and optimization. There also exists a need for a better understanding of the approaches and techniques at various stages of the data life cycle. In the work carried out, the data life-cycle in shop-floor is studied with a focus on each of the components -Data sources, collection, transmission, storage, processing, and visualization. A narrative literature review driven by two research questions is provided to study trends and challenges in the field. The selection of papers is supported by an analysis of n-grams. Those are used to comprehensively characterize the main technological and methodological aspects and as starting point to discuss potential future research directions. A detailed review of the current trends in different data life cycle stages is provided. In the end, the discussion of the existing challenges is also presented.
Self-Organization in Smart Manufacturing
Publication . Estrada-Jimenez, Luis A.; Pulikottil, Terrin; Nikghadam-Hojjati, Sanaz; Barata, José; CTS - Centro de Tecnologia e Sistemas; DEE - Departamento de Engenharia Electrotécnica e de Computadores; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; Institute of Electrical and Electronics Engineers (IEEE)
The concept of smart manufacturing has attracted huge attention in the last years as an answer to the increasing complexity, heterogeneity, and dynamism of manufacturing ecosystems. This vision embraces the notion of autonomous and self-organized elements, capable of self-management and self-decision-making under a context-aware and intelligent infrastructure. While dealing with dynamic and uncertain environments, these solutions are also contributing to generating social impact and introducing sustainability into the industrial equation thanks to the development of task-specific resources that can be easily adapted, re-used, and shared. A lot of research under the context of self-organization in smart manufacturing has been produced in the last decade considering different methodologies and developed under different contexts. Most of these works are still in the conceptual or experimental stage and have been developed under different application scenarios. Thus, it is necessary to evaluate their design principles and potentiate their results. The objective of this paper is threefold. First, to introduce the main ideas behind self-organization in smart manufacturing. Then, through a systematic literature review, describe the current status in terms of technological and implementation details, mechanisms used, and some of the potential future research directions. Finally, the presentation of an outlook that summarizes the main results of this work and their interrelation to facilitate the development of self-organized manufacturing solutions. By providing a holistic overview of the field, we expect that this work can be used by academics and practitioners as a guide to generate awareness of possible requirements, industrial challenges, and opportunities that future self-organizing solutions can have towards a smart manufacturing transition.
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Entidade financiadora
European Commission
Programa de financiamento
H2020
Número da atribuição
814078