Materials characterisation and software tools as key enablers in Industry 5.0 and wider acceptance of new methods and products

Konstantopoulos, Georgios; Charitidis, Costas A.; Bañares, Miguel A.; Portela, Raquel; Zangenberg, Nikolaj; Capria, Ennio; Sebastiani, Marco; Goldbeck, Gerhard; Koumoulos, Elias; Boskovic, Bojan; Diplas, Spyros; Konchakova, Natalia; Todorov, Ilian; Dykeman, Donna; Avataneo, Silvia Giovanna; Falzetti, Marco; Diz, Enrique Lozano; Reinosa, Julian J.; Burov, Ekaterina; Škrelić, Dejan; Jeliazkova, Nina; Barton, Bastian; Masotti, Giovanni; Tamarozzi, Tommaso; Alcón, Iván Moya

doi:10.1016/j.mtcomm.2023.106607

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…They mainly approached the medicine domain, looking for ML tools to predict lung cancer disease [53,54]. The last two groups contain two authors for each group, with a very small impact: discovering technologies and operations in Industry 4.0, proposing a framework for logistic management, and material characterization using Industry 5.0 [55][56][57][58]. The full list of authors is listed in Figure 9.…”

Section: Authors' Explanationmentioning

confidence: 99%

Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

Domenteanu,

Cibu,

Delcea

2024

Sustainability

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Over the past years, machine learning and big data analysis have emerged, starting as a scientific and fictional domain, very interesting but difficult to test, and becoming one of the most powerful tools that is part of Industry 5.0 and has a significant impact on sustainable, resilient manufacturing. This has garnered increasing attention within scholarly circles due to its applicability in various domains. The scope of the article is to perform an exhaustive bibliometric analysis of existing papers that belong to machine learning and big data, pointing out the capability from a scientific point of view, explaining the usability of applications, and identifying which is the actual in a continually changing domain. In this context, the present paper aims to discuss the research landscape associated with the use of machine learning and big data analysis in Industry 5.0 in terms of themes, authors, citations, preferred journals, research networks, and collaborations. The initial part of the analysis focuses on the latest trends and how researchers lend a helping hand to change preconceptions about machine learning. The annual growth rate is 123.69%, which is considerable for such a short period, and it requires a comprehensive analysis to check the boom of articles in this domain. Further, the exploration investigates affiliated academic institutions, influential publications, journals, key contributors, and most delineative authors. To accomplish this, a dataset has been created containing researchers’ papers extracted from the ISI Web of Science database using keywords associated with machine learning and big data, starting in 2016 and ending in 2023. The paper incorporates graphs, which describe the most relevant authors, academic institutions, annual publications, country collaborations, and the most used words. The paper ends with a review of the globally most cited documents, describing the importance of machine learning and big data in Industry 5.0.

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Section: Authors' Explanationmentioning

confidence: 99%

Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

Domenteanu,

Cibu,

Delcea

2024

Sustainability

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Industry 5.0 and sustainability: An overview of emerging trends and challenges for a green future

Rame,

Purwanto,

Sudarno

2024

Innovation and Green Development

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IoT-Driven Innovations: A Case Study Experiment and Implications for Industry 5.0

Blinova,

Singh,

Kaur

et al. 2024

BIO Web Conf.

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This paper uses a thorough case study experiment to examine the real-world applications of IoT-driven innovations within the context of Industry 5.0. The factory floor has a temperature of 32.5°C, a warehouse humidity of 58%, and a safe pressure level of 102.3 kPa on the manufacturing line, according to an analysis of IoT sensor data. A 5.7% decrease in energy use was made possible by the data-driven strategy, as shown by the office's CO2 levels falling to 450 parts per million. The case study participants, who had a varied range of skills, were instrumental in the implementation of IoT, and the well-organized schedule guaranteed a smooth deployment. Key Industry 5.0 indicators, such as +2% in production efficiency, -5.7% in energy usage, -29% in quality control flaws, and +33.3% in inventory turnover, show significant gains. Key metrics evaluation, data-driven methodology, case study, Industry 5.0, IoT-driven innovations, and revolutionary potential are highlighted by these results.

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Materials characterisation and software tools as key enablers in Industry 5.0 and wider acceptance of new methods and products

Cited by 4 publications

References 28 publications

Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

Industry 5.0 and sustainability: An overview of emerging trends and challenges for a green future

IoT-Driven Innovations: A Case Study Experiment and Implications for Industry 5.0

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