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The fourth industrial revolution shapes today's private and industrial environments, implying increased digitalization, connectivity, and artificial intelligence. Wearable devices support digital communication by displaying information and monitoring health-related aspects by measuring vital signs. Even though various wearables for measuring vital signs are already used in private life, they have not yet found their way into the production environment. This could be due to poor data quality or a lack of acceptance among employees regarding the usability of wearables during work activities. This paper aims to evaluate the accuracy and usability of selected wearable devices in manual assembly. Therefore, two user studies were conducted in a rebuilt production environment. The first study focuses on the data accuracy of the heart rate measurement of different wearables during manual assembly. In the second study, the usability of the selected wearables is evaluated with the thinking-aloud method during a manual assembly task and questionnaires. [ABSTRACT FROM AUTHOR]

In the third industrial revolution (IR), mechatronics engineering has emerged, which is the merger of mechanical and electronics engineering along with information technology to an extent. In the current industrial revolution (fourth), the amalgamation of mechanical with Artificial intelligence (AI) has been used to simulate human behaviour in machines. In the earlier decades, the application of AI in mechanical engineering has expedited and the increased volume of related research papers is a proof of this fact. The broad domain of mechanical engineering has a lot of potential for machine learning applications in order to detect errors, minimise rejection, improve product quality, optimise a system, and so on, as it has not been intensely explored although. This article’s goal is to give a structured evaluation of works that employ vision systems and to attract researchers to analyse its potential integration with machine learning (ML) techniques from the industry 4.0 viewpoint. Current trends and research gaps in the area of manufacturing in collaboration with ML are also discussed at appropriate places. A general procedure to carry out research in the field of mechanical engineering is also presented. The results of various studies highlighted in this paper confirm that the implementation of AI in ME has provided an optimum solution to many mechanical problems that improved product quality and minimised the associated cost. Finally, the authors have led a direction to empower researchers in order to explore various areas in mechanical engineering from Industry 4.0 and AI perspective and enable possible topics for future research. The article concludes by providing challenges that may be faced in the coming future about the use of machine learning in mechanical engineering problems. [ABSTRACT FROM AUTHOR]

In this paper are investigated the re-engineering approaches for the optimization of pasta production processes. A preliminary study of technologies and information systems architectures to be applied to the entire pasta supply chain has been carried out. In the first part of the paper is presented an overview concerning Industry 4.0 enabling technologies, besides, in the second part are discussed the engineered processes improving production quality. Concerning simulation process, is designed by means of modelling workflows the wheat storage process, by simulating the automatism of silos controlled and managed by volume sensors. Finally, following Industry 5.0 facilities have been applied image vision and artificial intelligence methodologies suitable for auto-adaptive quality check of pasta. The paper discusses different models about production processes, efficiency, risks, costs and benefits. [ABSTRACT FROM AUTHOR]

Today's global supply chain (SC) is grappling with unprecedented challenges, which rendered conventional SC resilience (SCR) inadequate. In the wake of the Industry 4.0 (I4.0) age, investment in digital SCR (DSCR) relying on I4.0 technologies can potentially enhance organizations abilities to detect, avoid, respond to, and recover from disruptions promptly and efficiently. Literature most focuses on traditional SCR, while DSCR remains incipient. From this, this paper conducts a comprehensive literature review on the SCR, I4.0, and investment (INV) interplays to identify potential research gaps and avenues. It is revealed the integration of SCR-I4.0-INV as a complex and multifaceted process that requires holistic approaches. Some research gaps include the need for empirical studies on DSCR impacts, the role of organizational culture in supporting digital transformation, and investment and resilience trade-offs. This research provides insights for decision-makers and policymakers seeking to develop strategies for promoting resilient SCs in the digital transformation era. [ABSTRACT FROM AUTHOR]

The implementation of artificial intelligence and 4.0 technologies helps business leaders make better decisions to reduce costs, improve team performance, and increase productivity. It also enhances the quality of work life of workers, allows the optimization of the information obtained from the equipment in real time, in this way even unscheduled maintenance can be avoided. A systematic review of the literature regarding the applications and benefits that artificial intelligence has given to companies is presented, the information was obtained from databases such as Ebsco, Scopus, MDPI, ScienceDirect. Analyses, concepts, and contributions that have benefited the understanding of the need to embrace new technologies 4.0 and artificial intelligence are presented, for the benefit of companies and workers. [ABSTRACT FROM AUTHOR]