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Machines
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Smart Manufacturing and Beyond: Bridging Innovation in Industry 4.0 and...
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Smart Manufacturing and Beyond: Bridging Innovation in Industry 4.0 and 5.0

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3006

Special Issue Editors

Prof. Dr. Ratna Babu Chinnam

E-Mail Website
Guest Editor
Chair, Department of Industrial & Systems Engineering, Wayne State University, 4815 Fourth Street, Detroit, MI 48201, USA
Interests: autonomous diagnostics; prognostics; Industry 4.0; smart engineering systems; supply chain management; sustainability
Special Issues, Collections and Topics in MDPI journals
Dr. Sara Masoud

E-Mail Website
Guest Editor
Assistant Professor, Department of Industrial & Systems Engineering, Wayne State University, 4815 4th Street, Detroit, MI 48202, USA
Interests: human robot collaboration; extended reality (including virtual reality, augmented reality, and mixed reality); physics-based simulation; digital twins

Special Issue Information

Dear Colleagues,

Industry 4.0 has ushered in a transformative era in manufacturing, integrating cutting-edge technologies to enhance efficiency, productivity and competitiveness. As we stand on the cusp of Industry 5.0, emphasizing collaborative synergy between humans and machines, there is a unique opportunity to explore and advance the realms of both Industry 4.0 and 5.0 in smart manufacturing.

Industry 4.0 signifies a revolutionary shift in the manufacturing landscape, integrating state-of-the-art technologies to amplify efficiency, productivity and competitiveness. The significance of Industry 4.0 lies in its capacity to overhaul conventional manufacturing processes by incorporating advanced digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), big data analytics, extended reality, additive manufacturing, digital twinning and robotics. These innovations establish seamless connectivity and communication among machines, systems and humans, cultivating a more agile and responsive production environment. Moreover, with the emergence of Industry 5.0, there is an additional layer of transformation. Industry 5.0 builds upon the foundation laid by its predecessor, emphasizing collaboration between humans and machines. It envisions a future where technology not only enhances efficiency, but also fosters a harmonious coexistence between human ingenuity and technological advancements. Together, Industry 4.0 and 5.0 enable the scalable customization of products, addressing the dynamic demands of a rapidly evolving market. Embracing these industrial revolutions not only elevates operational efficiency, but also strategically positions industries to flourish in an increasingly interconnected and digitized global economy.

This Special Issue aims to publish works that delve into recent advancements in science and technology within the dynamic landscape of smart manufacturing. Topics of interest include, but are not limited to:

  • Additive and hybrid manufacturing;
  • Intelligent automation;
  • Manufacturing ergonomics;
  • Smart factories;
  • Digital engineering;
  • Sustainable manufacturing;
  • Collaborative robots in manufacturing;
  • Knowledge management;
  • Cyber–physical systems;
  • Manufacturing engineering;
  • Equipment design;
  • Advanced inspection and measurement;
  • Digital twinning;
  • Immersive manufacturing;
  • Autonomous production;
  • Big Data analytics in Industry 4.0/5.0;
  • Blockchain for manufacturing;
  • Cloud computing in Industry 4.0/5.0;
  • Cyber security in Industry 4.0/5.0;
  • Human–machine interaction;
  • Industrial Internet of Things (IIoT);
  • Maintenance in in Industry 4.0/5.0;
  • Quality management in Industry 4.0/5.0;
  • Collaborative robotics in Industry 4.0/5.0;
  • Simulation in Industry 4.0/5.0;
  • Smart operators in Industry 4.0/5.0.

Prof. Dr. Ratna Babu Chinnam
Dr. Sara Masoud
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. 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
  • Industry 4.0
  • smart manufacturing
  • advanced manufacturing
  • cognitive manufacturing systems

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Published Papers (3 papers)

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Research

12 pages, 809 KiB  
Article
I3oT (Industrializable Industrial Internet of Things) Tool for Continuous Improvement in Production Line Efficiency by Means of Sub-Bottleneck Detection Method
by Javier Llopis, Antonio Lacasa, Nicolás Montés and Eduardo Garcia
Machines 2024, 12(11), 760; https://doi.org/10.3390/machines12110760 - 29 Oct 2024
Viewed by 498
Abstract
The present paper shows how to develop an I3oT (Industrializable Industrial Internet of Things) tool for continuous improvement in production line efficiency by means of the sub-bottleneck detection method. There is a large amount of scientific literature related to the detection of bottlenecks [...] Read more.
The present paper shows how to develop an I3oT (Industrializable Industrial Internet of Things) tool for continuous improvement in production line efficiency by means of the sub-bottleneck detection method. There is a large amount of scientific literature related to the detection of bottlenecks in production lines. However, there is no scientific literature that develops tools to improve production lines based on the bottlenecks that go beyond rebalancing tasks. This article explores the concept of a sub-bottleneck. In order to detect sub-bottlenecks in a massive way, the use of one of the I3oT (Industrializable Industrial Internet of Things) tools developed in our previous work, the mini-terms, is proposed. These mini-terms use the existing sensors for the normal operation of the production lines to measure the sub-cycle times and use them to predict the deterioration of the machine components found in the production lines. The sub-bottleneck algorithms proposed are used in two real twin lines at the Ford manufacturing plant in Almussafes (Valencia), the (3LH) and (3RH), to show how the lines can be continuously improved by means of sub-bottleneck detection. Full article
(This article belongs to the Special Issue Smart Manufacturing and Beyond: Bridging Innovation in Industry 4.0 and 5.0)
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25 pages, 3004 KiB  
Article
Solving Flexible Job-Shop Scheduling Problem with Heterogeneous Graph Neural Network Based on Relation and Deep Reinforcement Learning
by Hengliang Tang and Jinda Dong
Machines 2024, 12(8), 584; https://doi.org/10.3390/machines12080584 - 22 Aug 2024
Viewed by 1075
Abstract
Driven by the rise of intelligent manufacturing and Industry 4.0, the manufacturing industry faces significant challenges in adapting to flexible and efficient production methods. This study presents an innovative approach to solving the Flexible Job-Shop Scheduling Problem (FJSP) by integrating Heterogeneous Graph Neural [...] Read more.
Driven by the rise of intelligent manufacturing and Industry 4.0, the manufacturing industry faces significant challenges in adapting to flexible and efficient production methods. This study presents an innovative approach to solving the Flexible Job-Shop Scheduling Problem (FJSP) by integrating Heterogeneous Graph Neural Networks based on Relation (HGNNR) with Deep Reinforcement Learning (DRL). The proposed framework models the complex relationships in FJSP using heterogeneous graphs, where operations and machines are represented as nodes, with directed and undirected arcs indicating dependencies and compatibilities. The HGNNR framework comprises four key components: relation-specific subgraph decomposition, data preprocessing, feature extraction through graph convolution, and cross-relation feature fusion using a multi-head attention mechanism. For decision-making, we employ the Proximal Policy Optimization (PPO) algorithm, which iteratively updates policies to maximize cumulative rewards through continuous interaction with the environment. Experimental results on four public benchmark datasets demonstrate that our proposed method outperforms four state-of-the-art DRL-based techniques and three common rule-based heuristic algorithms, achieving superior scheduling efficiency and generalization capabilities. This framework offers a robust and scalable solution for complex industrial scheduling problems, enhancing production efficiency and adaptability. Full article
(This article belongs to the Special Issue Smart Manufacturing and Beyond: Bridging Innovation in Industry 4.0 and 5.0)
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22 pages, 11268 KiB  
Article
Artificial Intelligence Enabling Denoising in Passive Electronic Filtering Circuits for Industry 5.0 Machines
by Alessandro Massaro
Machines 2024, 12(8), 551; https://doi.org/10.3390/machines12080551 - 13 Aug 2024
Viewed by 905
Abstract
The paper proposes an innovative model able to predict the output signals of resistance and capacitance (RC) low-pass filters for machine-controlled systems. Specifically, the work is focused on the analysis of the parametric responses in the time- and frequency-domain of the filter output [...] Read more.
The paper proposes an innovative model able to predict the output signals of resistance and capacitance (RC) low-pass filters for machine-controlled systems. Specifically, the work is focused on the analysis of the parametric responses in the time- and frequency-domain of the filter output signals, by considering a white generic noise superimposed onto an input sinusoidal signal. The goal is to predict the filter output using a black-box model to support the denoising process by means of a double-stage RC filter. Artificial neural networks (ANNs) and random forest (RF) algorithms are compared to predict the output of noisy signals. The work is concluded by defining guidelines to correct the voltage output by knowing the predictions and by adding further RC elements correcting the distorted signals. The model is suitable for the implementation of Industry 5.0 Digital Twin (DT) networks applied to manufacturing processes. Full article
(This article belongs to the Special Issue Smart Manufacturing and Beyond: Bridging Innovation in Industry 4.0 and 5.0)
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