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Bispo LGM, Amaral FG. The impact of Industry 4.0 on occupational health and safety: A systematic literature review. JOURNAL OF SAFETY RESEARCH 2024; 90:254-271. [PMID: 39251284 DOI: 10.1016/j.jsr.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 02/25/2024] [Accepted: 04/24/2024] [Indexed: 09/11/2024]
Abstract
INTRODUCTION Industry 4.0 has brought new paradigms to businesses based on high levels of automation and interconnectivity and the use of technologies. This new context has an impact on the work environment and workers. Nevertheless, these impacts are still inconclusive and controversial, requiring new investigative perspectives. This study aimed to investigate the requirements sought, the risk factors identified, and the adverse effects on workers caused by the characteristics of I4.0. METHOD The methodology was based on a systematic literature review utilizing the PRISMA protocol, and 30 articles were found eligible. A descriptive and bibliometric analysis of these studies was performed. RESULTS The results identified the main topics that emerged and have implications for workers' Occupational Health and Safety (OHS) and divided them into categories. The requirements are related mainly to cognitive, organizational, and technological demands. The most significant risk factors generated were associated with the psychosocial ones, but organizational, technological, and occupational factors were also identified. The adverse effects cited were categorized as psychic, cognitive, physical, and organizational; stress was the most cited effect. An explanatory theoretical model of interaction was proposed to represent the pathway of causal relations between the requirements and risk factors for the effects caused by I4.0. CONCLUSIONS AND PRACTICAL APPLICATIONS This review has found just how complex the relationships between the principles of Industry 4.0 are (e.g., requirements, risk factors, and effects) and the human factors. It also suggests a pathway for how these relationships occur, bridging the gap left by the limited studies focused on connecting these topics. These results can help organizational managers understand the impacts of I4.0 on workers' safety and health.
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Affiliation(s)
- Lucas Gomes Miranda Bispo
- Production and Transportation Engineering Department, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.
| | - Fernando Gonçalves Amaral
- Production and Transportation Engineering Department, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
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Liu S, Li P, Wang J, Liu P. Toward industry 5.0: Challenges and enablers of intelligent manufacturing technology implementation under the perspective of sustainability. Heliyon 2024; 10:e35162. [PMID: 39157342 PMCID: PMC11328039 DOI: 10.1016/j.heliyon.2024.e35162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 07/23/2024] [Accepted: 07/24/2024] [Indexed: 08/20/2024] Open
Abstract
The advancement of intelligent manufacturing technology in the era of Industry 5.0 has propelled the intelligence and automation of manufacturing production, while also exerting a significant impact on sustainable development of the manufacturing industry. However, the challenges and enablers faced by the transformation of intelligent manufacturing technology in the context of sustainable development of Industry 5.0 are still unclear. Based on literature review and expert opinions, this study uses the Likert scale to determine the challenges and enablers of the implementation of intelligent manufacturing technology in social, environmental and economic sustainability. The fuzzy-DEMETAL and AISM are used to analyze the logical relationship and hierarchical relationship between the above factors, and the MICMAC matrix is used to determine the key influencing factors. The research conclusions show that the most important challenges affecting the implementation of intelligent manufacturing technology are cost and funding, and the most important enabler is social benefits and public service improved. This research will provide insights for industry practitioners and decision makers in the management and decision-making process of implementing the transformation and upgrading of manufacturing intelligent manufacturing, thereby enhancing the sustainability of manufacturing development.
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Affiliation(s)
- Shiyan Liu
- School of Management, Zhengzhou University, Zhengzhou 450001, China
| | - Pengyue Li
- School of Management, Zhengzhou University, Zhengzhou 450001, China
| | - Jinfeng Wang
- China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China
| | - Peng Liu
- School of Management, Zhengzhou University, Zhengzhou 450001, China
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Yang T, Razzaq L, Fayaz H, Qazi A. Redefining fan manufacturing: Unveiling industry 5.0's human-centric evolution and digital twin revolution. Heliyon 2024; 10:e33551. [PMID: 39050440 PMCID: PMC11268175 DOI: 10.1016/j.heliyon.2024.e33551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 05/22/2024] [Accepted: 06/24/2024] [Indexed: 07/27/2024] Open
Abstract
Industry 5.0 has the capacity to surpass the technology -oriented efficiency of Industry 4.0 and advance sustainable development objectives such as prioritizing human needs, ensuring socio-environmental sustainability, and enhancing resilience. Digital twins and simulation technologies improve manufacturing, evaluate products and operations, and predict any potential adverse consequences. With digital twin technology, everything that exists in the physical world will eventually be duplicated in the digital realm. Within the context of Industry 5.0, this study aims to investigate the impact of digital twin technology on the fan manufacturing sector. The proposal for implementing the enabling industry 5.0 application was presented to the chief engineers of eight distinct fan manufacturers. Out of these, five responded positively and their feedback was subsequently followed up on. Three different avenues such as production, supply chain, and testing transparency were proposed for industry 5.0 implementation. The exploration of testing transparency is being undertaken based on a consensual decision. The Web of Things standard that enables digital twin generation in industry 4.0 is implemented to enable the testing transparency. This data was linked with the internal digital twin of fan motor created using ANSYS. This digital twin can predict the lifespan of the motor by analyzing the temperature of the motor housing surface. Toward sustainability and resilience with Industry 4.0 and Industry 5.0 may provide insights into this alignment.
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Affiliation(s)
- Taoer Yang
- School of Law, Xiamen University, Fujian, China
| | - Luqman Razzaq
- Department of Mechanical Engineering Technology, University of Gujrat, Gujrat, 50700, Pakistan
| | - H. Fayaz
- Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
| | - Atika Qazi
- Centre for Lifelong Learning, University Brunei Darussalam, Brunei Darussalam
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Hermawati S, Correa R, Mohan M, Lawson G, Houghton R. Defining human-centricity in Industry 5.0 and assessing the readiness of ergonomics/human factors communities in UK. ERGONOMICS 2024:1-20. [PMID: 38685828 DOI: 10.1080/00140139.2024.2343947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 04/10/2024] [Indexed: 05/02/2024]
Abstract
There is a lack of a clear and consistent definition of human-centricity in Industry 5.0. This study identified the definition of human-centricity in Industry 5.0 through a systematic literature review and used it to assess the readiness of Ergonomics/Human Factors communities in the UK. The assessment of the communities readiness was conducted by reviewing UK accredited courses and events of three professional bodies; and interviewing practitioners (n = 8). Eleven themes were identified as elements of human-centricity from the thematic analysis of 30 publications. Gaps that had to be addressed to better equip UK practitioners to support the realisation of human-centricity in Industry 5.0 were also identified.
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Affiliation(s)
- Setia Hermawati
- Human Factors Research Group, Mechanical, Materials and Manufacturing Engineering Department, Faculty of Engineering, The University of Nottingham, Nottingham, UK
| | - Rhea Correa
- Human Factors Research Group, Mechanical, Materials and Manufacturing Engineering Department, Faculty of Engineering, The University of Nottingham, Nottingham, UK
| | - Mrinal Mohan
- Human Factors Research Group, Mechanical, Materials and Manufacturing Engineering Department, Faculty of Engineering, The University of Nottingham, Nottingham, UK
| | - Glyn Lawson
- Human Factors Research Group, Mechanical, Materials and Manufacturing Engineering Department, Faculty of Engineering, The University of Nottingham, Nottingham, UK
| | - Robert Houghton
- Human Factors Research Group, Mechanical, Materials and Manufacturing Engineering Department, Faculty of Engineering, The University of Nottingham, Nottingham, UK
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Abril-Jiménez P, Carvajal-Flores D, Buhid E, Cabrera-Umpierrez MF. Enhancing worker-centred digitalisation in industrial environments: A KPI evaluation methodology. Heliyon 2024; 10:e26638. [PMID: 38434084 PMCID: PMC10906181 DOI: 10.1016/j.heliyon.2024.e26638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 02/06/2024] [Accepted: 02/16/2024] [Indexed: 03/05/2024] Open
Abstract
Recently, the European Commission announced Industry 5.0 as a strategic initiative toward a value-driven industrial transformation. This new paradigm coexists with previous Industry 4.0 revolution that has guided the efforts towards technology driven industrial digitalisation in the past ten years. As part of this Industry 4.0 strategies, numerous KPI-driven evaluation methods were proposed to cover the multiple pillars of smart industry assessment. However, they do not incorporate human workers and actors in a systematic way as drivers for digitalisation processes, as the new Industry 5.0 paradigm argues. This paper addresses this gap by proposing an evaluation methodology that incorporates multiple human actors in the digitalisation process. The final objective of this methodology is to evaluate the direct and indirect benefits of the technology-driven transformation process to achieve the goals of human workers and other human stakeholders. To this end, our methodology provides the basis for proposing assessment tools and instruments for technological and infrastructure integration, process optimisation, new functionalities and human factors benefits, and four core indicators that have been applied to a real case comparing the digitalisation processes of three different companies.
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Affiliation(s)
- Patricia Abril-Jiménez
- Life Supporting Technologies Research Group, Universidad Politécnica de Madrid, Avda Complutense 30, 28040, Madrid, Spain
| | - Diego Carvajal-Flores
- Life Supporting Technologies Research Group, Universidad Politécnica de Madrid, Avda Complutense 30, 28040, Madrid, Spain
| | - Eduardo Buhid
- Life Supporting Technologies Research Group, Universidad Politécnica de Madrid, Avda Complutense 30, 28040, Madrid, Spain
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Postelnicu CC, Boboc RG. Extended reality in the automotive sector: A bibliometric analysis of publications from 2012 to 2022. Heliyon 2024; 10:e24960. [PMID: 38312558 PMCID: PMC10835006 DOI: 10.1016/j.heliyon.2024.e24960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 01/05/2024] [Accepted: 01/17/2024] [Indexed: 02/06/2024] Open
Abstract
The present study aims to present a bibliometric analysis of publications related to "Extended Reality" (XR) in the automotive sector. XR is revolutionizing the industry in all fields, and the automotive is one of the sectors that has had much to gain from this technology and its components (Virtual Reality, Augmented Reality, Mixed Reality). Articles on XR in the automotive field that were published from 2012 to 2022 were retrieved from the Scopus database. Extracted items were analysed in terms of the document type, document language, year of publication, country, authors, affiliations, sources, citations, keywords, and research domains. The open-source tool VOSviewer was used to visualize trends in research on XR applied to automotive. The analyses of 1584 documents revealed that the total number of publications has continually increased over the last 11 years. The country producing most of the articles in this field was Germany, followed by the United States and China. The most productive journal is Transportation Research Part F: Traffic Psychology and Behaviour and the institution that issued most of the articles is Technical University of Munich. From the analysis of author keywords, the prominent research areas currently involving the use of XR technologies in automotive can be highlighted: virtual prototyping, design, manufacturing, sales, training, driver or pedestrian behaviour analysis, and ergonomics. More recently, terms like artificial intelligence and autonomous vehicles have started to be used more frequently in studies in the field. The current study reveals an expanding corpus of literature on XR-based applications for the automotive sector using bibliometric methods. Researchers and stakeholders can use this study as a useful reference to comprehend the big picture and the state-of-the-art in this area.
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Affiliation(s)
- Cristian-Cezar Postelnicu
- Department of Automotive and Transport Engineering, Transilvania University of Brașov, 29 Eroilor Blvd., 500036 Brasov, Romania
| | - Răzvan Gabriel Boboc
- Department of Automotive and Transport Engineering, Transilvania University of Brașov, 29 Eroilor Blvd., 500036 Brasov, Romania
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Henriksen A, Blond L. Executive-centered AI? Designing predictive systems for the public sector. SOCIAL STUDIES OF SCIENCE 2023; 53:738-760. [PMID: 37154115 DOI: 10.1177/03063127231163756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Recent policies and research articles call for turning AI into a form of IA ('intelligence augmentation'), by envisioning systems that center on and enhance humans. Based on a field study at an AI company, this article studies how AI is performed as developers enact two predictive systems along with stakeholders in public sector accounting and public sector healthcare. Inspired by STS theories about values in design, we analyze our empirical data focusing especially on how objectives, structured performances, and divisions of labor are built into the two systems and at whose expense. Our findings reveal that the development of the two AI systems is informed by politically motivated managerial interests in cost-efficiency. This results in AI systems that are (1) designed as managerial tools meant to enable efficiency improvements and cost reductions, and (2) enforced on professionals on the 'shop floor' in a top-down manner. Based on our findings and a discussion drawing on literature on the original visions of human-centered systems design from the 1960s, we argue that turning AI into IA seems dubious, and ask what human-centered AI really means and whether it remains an ideal not easily realizable in practice. More work should be done to rethink human-machine relationships in the age of big data and AI, in this way making the call for ethical and responsible AI more genuine and trustworthy.
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Affiliation(s)
| | - Lasse Blond
- Danish Technological Institute, Aarhus, Denmark
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Dhayal KS, Giri AK, Kumar A, Samadhiya A, Agrawal S, Agrawal R. Can green finance facilitate Industry 5.0 transition to achieve sustainability? A systematic review with future research directions. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:102158-102180. [PMID: 37695480 DOI: 10.1007/s11356-023-29539-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/23/2023] [Indexed: 09/12/2023]
Abstract
Most of the world's rising carbon emission results from industrial activities. Previous industrial revolutions did not put much thought into safeguarding the natural world. Governments worldwide have been continuously implementing regulations and policies for the mitigation of climate change to promote sustainable development. To achieve decarbonization, the climate change discussion is merged with Industry 5.0 (I5.0) where green finance (GF) plays a crucial role. This technological metamorphosis of transition from Industry 4.0 (I4.0) to I5.0 will affect humans and their society. I5.0 forms a symbiotic relationship with different aspects of Society 5.0 (S5.0) such as social (human‒machine centricity), ecological (zero emissions), and technological (green innovations). Thus, the I5.0 transition prioritizes greening the economy in pursuit of achieving S5.0. Through a systematic review of 196 articles, this research study concisely summarizes the rapidly expanding body of information. The research domain gave six major themes: Green Innovations (GI), Green Manufacturing Practices (GMP), Circular Economy (CE), Green Supply Chain Management (GSCM), Emerging Economies, and Net Zero Economy (NZE). Finally, a framework has been provided that illustrates the supporting role of GF for the I5.0 transition eventually followed by S5.0. This study provides an overview of these themes with their propositions and future research directions. The present study addresses the knowledge gap by providing valuable contributions to the burgeoning research domain of I5.0 and GF. Moreover, it aims to garner the attention of different stakeholders to integrate these two concepts of research to attain the goal of sustainable development.
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Affiliation(s)
- Karambir Singh Dhayal
- Department of Economics and Finance, Birla Institute of Technology and Science (BITS), Pilani, Rajasthan, India.
| | - Arun Kumar Giri
- Department of Economics and Finance, Birla Institute of Technology and Science (BITS), Pilani, Rajasthan, India
| | - Anil Kumar
- Guildhall School of Business and Law, London Metropolitan University, London, UK
| | - Ashutosh Samadhiya
- Operations and Supply Chain Management, Jindal Global Business School, OP Jindal Global University, Sonipat, Haryana, India
| | - Shruti Agrawal
- Department of Humanities and Social Sciences, Malaviya National Institute of Technology, Jaipur, Rajasthan, India
| | - Rohit Agrawal
- Operations Management and Quantitative Techniques, Indian Institute of Management (IIM), Bodhgaya, Bihar, India
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Do JS, Kareem AB, Hur JW. LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS). SENSORS (BASEL, SWITZERLAND) 2023; 23:1009. [PMID: 36679806 PMCID: PMC9866563 DOI: 10.3390/s23021009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 06/17/2023]
Abstract
Industry 5.0, also known as the "smart factory", is an evolution of manufacturing technology that utilizes advanced data analytics and machine learning techniques to optimize production processes. One key aspect of Industry 5.0 is using vibration data to monitor and detect anomalies in machinery and equipment. In the case of a vertical carousel storage and retrieval system (VCSRS), vibration data can be collected and analyzed to identify potential issues with the system's operation. A correlation coefficient model was used to detect anomalies accurately in the vertical carousel system to ascertain the optimal sensor placement position. This model utilized the Fisher information matrix (FIM) and effective independence (EFI) methods to optimize the sensor placement for maximum accuracy and reliability. An LSTM-autoencoder (long short-term memory) model was used for training and testing further to enhance the accuracy of the anomaly detection process. This machine-learning technique allowed for detecting patterns and trends in the vibration data that may not have been evident using traditional methods. The combination of the correlation coefficient model and the LSTM-autoencoder resulted in an accuracy rate of 97.70% for detecting anomalies in the vertical carousel system.
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Is Industry 5.0 a Human-Centred Approach? A Systematic Review. Processes (Basel) 2023. [DOI: 10.3390/pr11010193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Industry 5.0 presents itself as a strategy that puts the human factor at the centre of production, where the well-being of the worker is prioritized, as well as more sustainable and resilient production systems. For human centricity, it is necessary to empower human beings and, respectively, industrial operators, to improve their individual skills and competences in collaboration or cooperation with digital technologies. This research’s main purpose and distinguishing point are to determine whether Industry 5.0 is truly human-oriented and how human centricity can be created with Industry 5.0 technologies. For that, this systematic literature review article analyses and clarifies the concepts and ideologies of Industry 5.0 and its respective technologies (Artificial Intelligence, Robotics, Human-robot collaboration, Digitalization), as well as the strategies of human centricity, with the aim of achieving sustainable and resilient systems, especially for the worker.
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Tegtmeier P, Weber C, Sommer S, Tisch A, Wischniewski S. Criteria and Guidelines for Human-Centered Work Design in a Digitally Transformed World of Work: Findings from a Formal Consensus Process. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192315506. [PMID: 36497580 PMCID: PMC9740184 DOI: 10.3390/ijerph192315506] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 06/09/2023]
Abstract
With the increasing digital transformation, work tasks are changing-in some cases, significantly. Our study addresses the question of whether the established criteria for work design are still sufficient or if they should get updated and additional criteria become necessary in the context of digitalization. In a multistage consensus process involving interdisciplinary groups of experts, we have identified specific criteria for the humane design of work in a world increasingly permeated by digitalized work tools. Starting with an expert workshop using a combined nominal group/focus group technique, followed by a real-time Delphi study, a content analysis and a five-stage peer comment process, we detected 13 criteria and 38 design guidelines for human-centered work in digital transformation. Mapping these with established criteria, it became apparent that some established criteria have experienced a new dynamic because of the digital transformation. For other criteria, a need for digitization-sensitive design is discernible. In addition, criteria have emerged whose necessity is rooted in the digital transformation. A diffusion and stronger interconnection of the various levels of the work system in connection with the digital transformation of work is apparent.
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Affiliation(s)
- Patricia Tegtmeier
- Federal Institute for Occupational Safety and Health, Friedrich-Henkel-Weg 1-25, 44149 Dortmund, Germany
| | - Corinna Weber
- CWeber-Coaching, Wasserstraße 26, 46284 Dorsten, Germany
| | - Sabine Sommer
- Federal Institute for Occupational Safety and Health, Nöldnerstraße 40-42, 10317 Berlin, Germany
| | - Anita Tisch
- Federal Institute for Occupational Safety and Health, Friedrich-Henkel-Weg 1-25, 44149 Dortmund, Germany
| | - Sascha Wischniewski
- Federal Institute for Occupational Safety and Health, Friedrich-Henkel-Weg 1-25, 44149 Dortmund, Germany
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Jeyaraman M, Nallakumarasamy A, Jeyaraman N. Industry 5.0 in Orthopaedics. Indian J Orthop 2022; 56:1694-1702. [PMID: 36187596 PMCID: PMC9485301 DOI: 10.1007/s43465-022-00712-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 07/28/2022] [Indexed: 02/04/2023]
Abstract
Background Industrial revolutions play a major role in the development of technologies in various fields. Currently, the world is marching towards softwarization and digitalization. There is an emerging need for conversion of Industry 4.0 to Industry 5.0 for technological development and implementation of the same in the digital era. In health care, digitalization emerged in Industry 4.0 revolution. To enhance patient care and quality of life, Industry 5.0 plays a major role in providing patient-centric care and customization and personalization of products. The integration of human intelligence with artificial intelligence provides a precise diagnosis and enhances the recovery and functional outcome of the patients. Materials and methods In this manuscript, the domains and limitations of Industry 5.0 and further research on Industry 6.0 were elaborated on to bring out technologies in better health care. Results Industry 5.0 lessens the work of medical professionals and integrates software-based diagnosis and management. It provides cost-effective manufacturing solutions with limited resources compared to Industry 4.0. Industry 5.0 focuses on SMART and additive manufacturing of implants, and the development of bio-scaffolds, prosthetics, and instruments. In this manuscript, the domains and limitations of Industry 5.0 and further research on Industry 6.0 were elaborated on to bring out technologies in better health care. Conclusion 'The personalization and customization of products' are the hallmarks of this evolving Industry 5.0 revolution. The major uplifts in various domains of industry 5.0 such as advanced automation, digitalization, collaborative robots, and personalization bring this an inevitable mechano-scientific technological revolution in this current medical era.
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Affiliation(s)
- Madhan Jeyaraman
- Department of Orthopaedics, Faculty of Medicine, Sri Lalithambigai Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai, Tamil Nadu 600095 India
- South Texas Orthopaedic Research Institute (STORI Inc.), Laredo, TX 78045 USA
| | - Arulkumar Nallakumarasamy
- Department of Orthopaedics, All India Institute of Medical Sciences, Bhubaneswar, Odisha 751019 India
| | - Naveen Jeyaraman
- Department of Orthopaedics, Atlas Hospitals, Tiruchirappalli, Tamil Nadu 620002 India
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Prediction of Temperature and Carbon Concentration in Oxygen Steelmaking by Machine Learning: A Comparative Study. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The basic oxygen steelmaking process (BOS) faces the issue of the absence of information about the melt temperature and the carbon concentration in the melt. Although deterministic models for predicting steelmaking process variables are being developed in metallurgical research, machine-learning models can model the nonlinearities of process variables and provide a good estimate of the target process variables. In this paper, five machine learning methods were applied to predict the temperature and carbon concentration in the melt at the endpoint of BOS. Multivariate adaptive regression splines (MARS), support-vector regression (SVR), neural network (NN), k-nearest neighbors (k-NN), and random forest (RF) methods were compared. Machine modeling was based on static and dynamic observations from many melts. In predicting from dynamic melting data, a method of pairing static and dynamic data to create a training set was proposed. In addition, this approach has been found to predict the dynamic behavior of temperature and carbon during melting. The results showed that the piecewise-cubic MARS model achieved the best prediction performance for temperature in testing on static and dynamic data. On the other hand, carbon predictions by machine models trained on joined static and dynamic data were more powerful. In the case of predictions from dynamic data, the best results were obtained by the k-NN-based model, i.e., carbon, and the piecewise-linear MARS model in the case of temperature. In contrast, the neural network recorded the lowest prediction performance in more tests.
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