Ontological Approach to Support the Horizontal and Vertical Information Integration in Smart Manufacturing Systems: An Experimental Case in a Long-Life Packaging Factory

Pereira, Ramon Martinez; Szejka, Anderson Luis; Canciglieri, Osíris

doi:10.3389/fmtec.2022.854155

Cited by 6 publications

(4 citation statements)

References 53 publications

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“…The indicators are grouped into categories relevant to industry 5.0 research, including physical ergonomics (safety, physical workload, job design), cognitive ergonomics (mental workload, awareness), performance (efficiency, effectiveness), and user experience satisfaction/hedonomics (emotional responses, acceptance, attitudes, trust) [15]. In the field of ontology engineering, especially within the Industry 4.0 framework, a structured series of methodical procedures is adhered to [43]. Initially, a comprehensive analysis of requirements is performed to grasp the diverse points of view of stakeholders and establish the goals, extent, and requirements of the ontologies [44].…”

Section: State-of-the-art-knowledge Gap and Motivationmentioning

confidence: 99%

Knowledge Graph-Based Framework to Support Human-Centered Collaborative Manufacturing in Industry 5.0

Nagy,

Abonyi,

Ruppert

2024

Applied Sciences

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The importance of highly monitored and analyzed processes, linked by information systems such as knowledge graphs, is growing. In addition, the integration of operators has become urgent due to their high costs and from a social point of view. An appropriate framework for implementing the Industry 5.0 approach requires effective data exchange in a highly complex manufacturing network to utilize resources and information. Furthermore, the continuous development of collaboration between human and machine actors is fundamental for industrial cyber-physical systems, as the workforce is one of the most agile and flexible manufacturing resources. This paper introduces the human-centric knowledge graph framework by adapting ontologies and standards to model the operator-related factors such as monitoring movements, working conditions, or collaborating with robots. It also presents graph-based data querying, visualization, and analysis through an industrial case study. The main contribution of this work is a knowledge graph-based framework that focuses on the work performed by the operator, including the evaluation of movements, collaboration with machines, ergonomics, and other conditions. In addition, the use of the framework is demonstrated in a complex use case based on an assembly line, with examples of resource allocation and comprehensive support in terms of the collaboration aspect between shop-floor workers.

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Section: State-of-the-art-knowledge Gap and Motivationmentioning

confidence: 99%

Knowledge Graph-Based Framework to Support Human-Centered Collaborative Manufacturing in Industry 5.0

Nagy,

Abonyi,

Ruppert

2024

Applied Sciences

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“…Therefore, DS is applied for planning and management of injection molding manufacturing, in such a way that for CPS systems where manufacturing is realized, it generates a knowledge base of data and meta data, based on which DS is built for manufacturing scheduling, capacity planning, realization of work orders and capacity management. The next example [152], refers to grinding and digitalization of manufacturing in SMEs. The open source MES model was developed using IoT, intelligent planning, DT and cloud computing.…”

Section: Refmentioning

confidence: 99%

On the Way to the Development of Smart Manufacturing Model in Practice

Djoric,

Jordanovic,

Lalic

et al. 2024

Preprint

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Digital technologies have been present in manufacturing and industry for a long time, and today they represent the basis for the application of the Industry 4.0 model in practice. The transformation marked by the Industry 4.0 model is based on the application of a large number of models of communication technologies, solutions for connecting entities (machines, parts, ...) in the factory within Internet of Things (IoT), as well as the possibility of big data analysis (BDA) throught the artificial intelligence tools (AI/ML). These technologies provide great opportunities to create more flexible and profitable manufacturing processes based on data, and using the Internet. As more and more factories and their equipment are IoT enabled, the volume of data will only grow, hence the application of digital technologies is inevitable. To truly pave the way forward to Industry 4.0 and beyond, manufacturing must evolve into cognitive manufacturing, which is the base model of smart manufacturing. This paper provides an overview of the development of the concept of digital manufacturing, from different aspects, with examples of application in real manufacturing. The goal is to create a good practice of digital manufacturing with the elements of Industry 4.0, as a basis for the development and application of the smart manufacturing (SM) model.

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“…The transformation was the semantic grouping created to show that Industry 4.0 develops over time. In this, it is a transition (Kazancoglu et al, 2021), step (Grabowska et al, 2022a), movement (Pereira et al, 2022), and change (Nwankwo, 2022), always with the sense of the dynamics of transformation (Sivanuja & Sandanayake, 2022), in which the current stage is just the germination of the future. This transformation may happen slowly, but it gains strength and speed until it becomes a revolution (Lemos et al, 2022;Rodrigues, 2021;Turel & Elife, 2019).…”

Section: Industry 40: Main Approachesmentioning

confidence: 99%

“…Machines have started to replace man in the most strenuous and dangerous tasks, intensifying and perfecting the transformation of raw materials into goods. The attributes included in this group were transformation (Wikström & Gedda, 2022), a fundamental element of every contemporary industry (Sivanuja & Sandanayake, 2022;Pereira et al, 2022;Kazancoglu et al, 2021;Kazancoglu et al, 2021), to mark the innovative character of the fourth industrial revolution, operationalized with intelligent machines (Abubakar et al, 2022;Almatani, 2021;Gonçalves et al, 2022;Mohammed & Trzcieliński, 2021;Sivanuja & Sandanayake, 2022;Sarı et al, 2020) and which, for this reason, also generate innovative (Câmara, 2020;Gonçalves et al, 2022;Grabowska et al, 2022b;Mohammed & Trzcieliński, 2021) and sustainable (Grabowska et al, 2022a) products. The significant assets (Boltz, 2022) of these new organizations are knowledge, which structures the new industrial practices (Nwankwo, 2022;Cruzara et al, 2021), present in each of their production lines (Bataglini, 2021).…”

Section: Attributes Of Industry 40mentioning

confidence: 99%

What is Industry 4.0: a Review of the Literature Using the Conceptual Bibliographic Method

De Oliveira,

Craveiro,

Bezerra

et al. 2023

J. Professional Business Review

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Purpose: Scientific production has tried to understand the various evolutionary intricacies of Industry 4.0, the so-called fourth industrial revolution. As discoveries increase and deepen, there must also be an effort to understand the frontiers of scientific knowledge, its most noticeable and studied aspects, and those attributes that have only now been perceived. It is, therefore, necessary to periodically take stock of what science knows about the phenomenon to understand where it is headed and which paths have already been taken. Therefore, the need to take inventory of the current state of knowledge about Industry 4.0 was the theoretical justification for this study. Theoretical framework: The theoretical architecture of this study consisted of the transformations that the industrial revolutionary stages caused in production systems, firstly, and then for the entire society. It involves aspects from various areas of scientific knowledge to understand the different perceptions contained in the scientific literature on the phenomenon of Industry 4.0. Design/Methodology/Approach: The conceptual bibliographic method was used to generate answers to three guiding questions: What are the main approaches to Industry 4.0? What are its main attributes? What is its irradiation logic? All conceptual definitions in scientific studies published in English and Portuguese available in the Google Scholar database from 2019 to 2020 were analyzed, with a total of 19 studies. The semantic analysis technique was used to identify the approaches and attributes, which were then organized by semantic proximity, constituting the semantic groupings on which the results were generated and interpreted. Findings: The results showed the existence of eight groups of approaches (procedures, evolution, time, grouping, transformation, layer, organization, and model) and twelve groups of attributes (industrialization, transformation technologies, information technologies, integrated system, processes, society, quality, knowledge, planning, market, value chain, and automation). These discoveries made it possible to understand that Industry 4.0 has a driving force: knowledge, which spreads similarly in waves until it reaches the entire society and is not limited only to the interior of factories and economic organizations. Research, Practical, and Social Implications: Two implications of these findings stand out. The first is the dynamic nature of implementing Industry 4.0 in organizations, whether economic or not, which is done more quickly if knowledge and technologies are available or more slowly when expertise and technologies are fragile or non-existent. The second is teleological since the purpose of Industry 4.0 and all industrial revolutions is to continually improve production processes, even though these benefits often generate unwanted externalities, such as environmental impacts. This means that the progress of knowledge and its developments cannot be stopped but can be directed towards other purposes. Originality/Value: The main contribution of this study to science is to point out the current stage of the evolution of Industry 4.0 about previous revolutions. Three other contributions are also significant: identifying the main approaches to the phenomenon, its most prominent characteristics, and its irradiation dynamics.

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Ontological Approach to Support the Horizontal and Vertical Information Integration in Smart Manufacturing Systems: An Experimental Case in a Long-Life Packaging Factory

Cited by 6 publications

References 53 publications

Knowledge Graph-Based Framework to Support Human-Centered Collaborative Manufacturing in Industry 5.0

Knowledge Graph-Based Framework to Support Human-Centered Collaborative Manufacturing in Industry 5.0

On the Way to the Development of Smart Manufacturing Model in Practice

What is Industry 4.0: a Review of the Literature Using the Conceptual Bibliographic Method

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