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Applied Sciences
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The Future of Manufacturing and Industry 4.0
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The Future of Manufacturing and Industry 4.0

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 3097

Special Issue Editors

Dr. Jaskó Szilárd

E-Mail Website
Guest Editor
Department of Applied Informatics, University of Pannonia, H-8800 Nagykanizsa, Hungary
Interests: adaptive intelligent systems; sensor networks; Industry 4.0; Industry 5.0
Dr. Tamás Ruppert

E-Mail Website
Guest Editor
Department of Process Engineering, University of Pannonia, H-8200 Veszprém, Hungary
Interests: process mining algorithms; discrete-event simulators; Industry 4.0; Operator 4.0; Industry 5.0
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industry 4.0 (I4.0)—also recognized as the fourth industrial revolution or smart manufacturing—is characterized by the integration of intelligent digital technologies into manufacturing and industrial processes. This concept applied (and applies) a range of technologies from the Internet of Things (IoT) to artificial intelligence (AI) for enable the possibilities of real-time decision-making and enhanced productivity. Various crises and forecasts have highlighted the need to review certain elements of the I4.0 directive.

The next generation of I4.0, Industry 5.0 (I5.0), has defined resilience, sustainability and human-centricity as three pillars into which I4.0 should be developed. Recent global crises, such as the COVID-19 pandemic, have highlighted that our previous understanding of industrial production and its logistics assumes an optimal world. Unfortunately, our world is not ideal and, therefore, we cannot look at it as such; we must make our industrial biospheres resilient against adversity. There is growing evidence that our current growth-oriented economic approach is misguided and could lead to a major world-scale collapse if we do not make our environment, including our industrial processes, sustainable. Even though our whole social system is supposed to serve society, we sometimes forget about the human being in the process of optimising our systems. Human-centred thinking needs to become central again in these processes. The current version of I5.0 may not address all the future problems and development potential of I4.0. Therefore, it is important to examine the future of I4.0 from many angles to shape the development of I5.0 and, thus, humanity.

In light of this vision for the future, there are many aspects of future manufacturing processes that may need to be changed or fine-tuned. For this Special Issue, we welcome all research and work that presents the future of I4.0 as a whole or in a specific segment, its risks, concrete developments, its impact on society, its anomalies, and any specific work related to this topic.

Dr. Jaskó Szilárd
Dr. Tamás Ruppert
Guest Editors

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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • the future of manufacturing
  • the future of industry
  • new technologies in Industry 4.0
  • circular economy in industry, human-centred solutions
  • sustainable manufacturing

Published Papers (5 papers)

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Research

25 pages, 1173 KiB  
Article
Preparedness for Data-Driven Business Model Innovation: A Knowledge Framework for Incumbent Manufacturers
by Shailesh Tripathi, Nadine Bachmann, Manuel Brunner and Herbert Jodlbauer
Appl. Sci. 2024, 14(8), 3454; https://doi.org/10.3390/app14083454 - 19 Apr 2024
Viewed by 304
Abstract
This study investigates data-driven business model innovation (DDBMI) for incumbent manufacturers, underscoring its importance in various strategic and managerial contexts. Employing topic modeling, the study identifies nine key topics of DDBMI. Through qualitative thematic synthesis, these topics are further refined, interpreted, and categorized [...] Read more.
This study investigates data-driven business model innovation (DDBMI) for incumbent manufacturers, underscoring its importance in various strategic and managerial contexts. Employing topic modeling, the study identifies nine key topics of DDBMI. Through qualitative thematic synthesis, these topics are further refined, interpreted, and categorized into three levels: Enablers, value creators, and outcomes. This categorization aims to assess incumbent manufacturers’ preparedness for DDBMI. Additionally, a knowledge framework is developed based on the identified nine key topics of DDBMI to aid incumbent manufacturers in enhancing their understanding of DDBMI, thereby facilitating the practical application and interpretation of data-driven approaches to business model innovation. Full article
(This article belongs to the Special Issue The Future of Manufacturing and Industry 4.0)
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27 pages, 3870 KiB  
Article
Knowledge Graph-Based Framework to Support Human-Centered Collaborative Manufacturing in Industry 5.0
by László Nagy, János Abonyi and Tamás Ruppert
Appl. Sci. 2024, 14(8), 3398; https://doi.org/10.3390/app14083398 - 17 Apr 2024
Viewed by 422
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue The Future of Manufacturing and Industry 4.0)
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13 pages, 3361 KiB  
Article
Performance Evaluation of Reconfiguration Policy in Reconfigurable Manufacturing Systems including Multi-Spindle Machines: An Assessment by Simulation
by Paolo Renna
Appl. Sci. 2024, 14(7), 2778; https://doi.org/10.3390/app14072778 - 26 Mar 2024
Viewed by 248
Abstract
Reconfigurable manufacturing systems (RMSs) are extensively studied and employed to address demand uncertainties. RMS machines are designed to be modular and adaptable to changing requirements. A recent innovation is the introduction of multi-spindle reconfigurable machines (MRMTs). This study evaluates the impact of MRMTs’ [...] Read more.
Reconfigurable manufacturing systems (RMSs) are extensively studied and employed to address demand uncertainties. RMS machines are designed to be modular and adaptable to changing requirements. A recent innovation is the introduction of multi-spindle reconfigurable machines (MRMTs). This study evaluates the impact of MRMTs’ introduction into an RMS, considering factors such as the number of MRMT machines and reconfiguration policies. A simulation model incorporating failures, process time variability, and part inter-arrival supports the analysis. The numerical results aid decision makers in determining the optimal RMS configuration with MRMTs. The simulation outcomes indicate that a balanced number of multi-spindle machines can significantly enhance performance compared with an unbalanced distribution. Full article
(This article belongs to the Special Issue The Future of Manufacturing and Industry 4.0)
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19 pages, 8110 KiB  
Article
Prediction of Short-Shot Defects in Injection Molding by Transfer Learning
by Zhe-Wei Zhou, Hui-Ya Yang, Bei-Xiu Xu, Yu-Hung Ting, Shia-Chung Chen and Wen-Ren Jong
Appl. Sci. 2023, 13(23), 12868; https://doi.org/10.3390/app132312868 - 30 Nov 2023
Viewed by 621
Abstract
For a long time, the traditional injection molding industry has faced challenges in improving production efficiency and product quality. With advancements in Computer-Aided Engineering (CAE) technology, many factors that could lead to product defects have been eliminated, reducing the costs associated with trial [...] Read more.
For a long time, the traditional injection molding industry has faced challenges in improving production efficiency and product quality. With advancements in Computer-Aided Engineering (CAE) technology, many factors that could lead to product defects have been eliminated, reducing the costs associated with trial runs during the manufacturing process. However, despite the progress made in CAE simulation results, there still exists a slight deviation from actual conditions. Therefore, relying solely on CAE simulations cannot entirely prevent product defects, and businesses still need to implement real-time quality checks during the production process. In this study, we developed a Back Propagation Neural Network (BPNN) model to predict the occurrence of short-shots defects in the injection molding process using various process states as inputs. We developed a Back Propagation Neural Network (BPNN) model that takes injection molding process states as input to predict the occurrence of short-shot defects during the injection molding process. Additionally, we investigated the effectiveness of two different transfer learning methods. The first method involved training the neural network model using CAE simulation data for products with length–thickness ratios (LT) of 60 and then applying transfer learning with real process data. The second method trained the neural network model using real process data for products with LT60 and then applied transfer learning with real process data from products with LT100. From the results, we have inferred that transfer learning, as compared to conventional neural network training methods, can prevent overfitting with the same amount of training data. The short-shot prediction models trained using transfer learning achieved accuracies of 90.2% and 94.4% on the validation datasets of products with LT60 and LT100, respectively. Through integration with the injection molding machine, this enables production personnel to determine whether a product will experience a short-shot before the mold is opened, thereby increasing troubleshooting time. Full article
(This article belongs to the Special Issue The Future of Manufacturing and Industry 4.0)
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19 pages, 2591 KiB  
Article
Enhancing Industrial Design Competitiveness: Research and Application of a Machine Tool Industrial Design Decision-Making Method Based on Product Family Architecture and Systematic Evaluation
by Zhiwei Yao, Xumin Wu, Yu Wu and Xintong Wen
Appl. Sci. 2023, 13(21), 11831; https://doi.org/10.3390/app132111831 - 29 Oct 2023
Viewed by 1083
Abstract
This study employs an innovative industrial design decision-making approach based on the notion of product family architecture and structural equation modeling. The aim is to enhance the product competitiveness of machine tool enterprises, which is crucial for their development. Key domains within machine [...] Read more.
This study employs an innovative industrial design decision-making approach based on the notion of product family architecture and structural equation modeling. The aim is to enhance the product competitiveness of machine tool enterprises, which is crucial for their development. Key domains within machine tool industrial design were systematically analyzed, leading to the creation of a design evaluation criteria system. Through quantitative data, the importance order of indicators at all levels was determined to enhance the overall competitiveness of product industrial design. First, teachers of relevant majors and professionals from machine tool companies completed a Likert scale questionnaire to validate the PFA-SEM hypothesis model. Next, a subjective–objective integrated weighting method was introduced to guide the ranking of multiple design elements. The feasibility of this method was confirmed by applying the Entropy-Based Multi-Objective Decision-Making Method (Technique for Order Preference by Similarity to Ideal Solution). These approaches helped achieve industrial design improvements in Heatking induction vertical hardening machines. This research aids in optimizing machine tool design, guiding design iterations, and serving as a reference for the development and optimization of other machine tool design solutions. Full article
(This article belongs to the Special Issue The Future of Manufacturing and Industry 4.0)
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