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Special Issue Editor

Dr. Ambarish Kulkarni

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Guest Editor

School of Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
Interests: mobility; Industry 4.0; metaverse; artificial engineering; virtual and augmented reality

Special Issue Information

Dear Colleagues,

Recent growth in digital practices has made traditional mechanical engineering practices redundant, and thus encouraged industries to embrace digital technologies, including metaverse, digital design, deep learning, etc. This insurgency led to the development of Industry 5.0, where the digital technologies are augmented with human collaboration, creating a hybrid system deriving the best of both worlds. The digital revolution brought about by Industry 5.0 has paved the way to extreme automation, enabling efficient data transfer in conjunction with Internet of Things (IOT), artificial intelligence (AI), virtual reality and big data. However, challenges such as increasing complexities and unprecedented circumstances emphasize the need for human–machine collaborative systems for obtaining accuracy and speed in digital implementation.

As the digital transformation due to Industry 4.0 has made a massive change in the business models of sectors such as manufacturing and construction, this Special Issue takes it forward and explores the multidisciplinary applications of Industry 5.0. This Special Issue provides an opportunity for academicians/industrialists to submit papers that provide insights into Industry 5.0 and digital practices in interdisciplinary engineering applications. We welcome papers that demonstrate new developments in Industry 5.0 with applications to digital design, human–machine integration, and big data in all engineering fields (e.g., mechanical, robotic, mechatronic).

Research topics for this Special Issue include, but are not limited to:

Digital design and manufacturing;
Industrial Internet of Things (IIOT);
Big data;
Augmented reality (AR);
Virtual reality (VR);
Industry 5.0;
Quantum Artificial Intelligence;
Machine learning and deep learning;
Human–computer interface (HCI).

Dr. Ambarish Kulkarni
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Industry 5.0
digital practices
mixed reality
smart manufacturing
cognitive twins
metaverse
virtual prototyping
cyber security

Published Papers (3 papers)

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Research

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19 pages, 2897 KiB

Open AccessEditor’s ChoiceArticle

Cloud-Empowered Data-Centric Paradigm for Smart Manufacturing

by Sourabh Dani, Akhlaqur Rahman, Jiong Jin and Ambarish Kulkarni

Machines 2023, 11(4), 451; https://doi.org/10.3390/machines11040451 - 3 Apr 2023

Cited by 3 | Viewed by 1386

Abstract

In the manufacturing industry, there are claims about a novel system or paradigm to overcome current data interpretation challenges. Anecdotally, these studies have not been completely practical in real-world applications (e.g., data analytics). This article focuses on smart manufacturing (SM), proposed to address the inconsistencies within manufacturing that are often caused by reasons such as: (i) data realization using a general algorithm, (ii) no accurate methods to overcome the actual inconsistencies using anomaly detection modules, or (iii) real-time availability of insights of the data to change or adapt to the new challenges. A real-world case study on mattress protector manufacturing is used to prove the methods of data mining with the deployment of the isolation forest (IF)-based machine learning (ML) algorithm on a cloud scenario to address the inconsistencies stated above. The novel outcome of these studies was establishing efficient methods to enable efficient data analysis. Full article

(This article belongs to the Special Issue Industry 5.0 and Digital Practices in Multidisciplinary Applications)

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22 pages, 3384 KiB

Open AccessArticle

Cognitive Implementation of Metaverse Embedded Learning and Training Framework for Drivers in Rolling Stock

by Andrew Danylec, Krutika Shahabadkar, Hussein Dia and Ambarish Kulkarni

Machines 2022, 10(10), 926; https://doi.org/10.3390/machines10100926 - 12 Oct 2022

Cited by 7 | Viewed by 2345

Abstract

Public safety is prime concern in rail industry and driver training on hazard perception is crucial. Additionally, a new driver’s skill set determines the productivity and quality of existing driver training methods. Apprentice train drivers are required to complete massive hours under supervision of experienced drivers to attain the required skill sets causing productivity issues. Traditional driver training is paper based, and assessments are individually evaluated without any scientific rigor, resulting in quality challenges. This paper proposes a Metaverse embedded learning and training framework for drivers in rolling stock. The framework includes driver vision analysis by eye tracking and pupil dilation focusing on enhancing the productivity and quality of driver training and hazard detection for drivers in rolling stock. Metaverse embedded training and learning enhances experiential learning with unique benefits. In this paper, a metaverse-based training framework is proposed for train drivers to enhance productivity, quality, and safety aspects through case studies including: (i) driver sightline studies and (ii) vision analysis. The studies developed quantifying driver hazard perceptions and related comprehension rates based on eye tracking and vision studies. In conclusion, the overall savings on cost and time are 95% effective using Metaverse-based training method compared to traditional methods. Stakeholders need to supervise on driver tasks, knowledge retention, damage control due to the occurrence of hazards. The framework substantially reduced hazards to 50% with saving up to 3696 man-hours. The assessment was completely automated to provide real time assessment thus providing 93% more positive results compared to traditional methods. Full article

(This article belongs to the Special Issue Industry 5.0 and Digital Practices in Multidisciplinary Applications)

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Other

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11 pages, 461 KiB

Open AccessViewpoint

Towards a New Mindset for Interaction Design—Understanding Prerequisites for Successful Human–Machine Cooperation Using the Example of Food Production

by Rica Pohl and Lukas Oehm

Machines 2022, 10(12), 1182; https://doi.org/10.3390/machines10121182 - 7 Dec 2022

Cited by 2 | Viewed by 1122

Abstract

Advances in technology and digitalization offer huge potential for improving the performance of production systems. However, human limitations often function as an obstacle in realizing these improvements. This article argues that the discrepancy between potential and reality is caused by design features of technologies, also called ironies of automation. To this, the psychological mechanisms that cause these ironies are illustrated by the example of operators of a chocolate wrapping machine, and their effect is explained by basic theories of engineering psychology. This article concludes that engineers need to understand these basic theories and interdisciplinary teamwork is necessary to improve the design of digital technologies and enable harmonious, high-performance human–machine cooperation. Full article

(This article belongs to the Special Issue Industry 5.0 and Digital Practices in Multidisciplinary Applications)

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