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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: closed (31 July 2025) | Viewed by 19655

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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (8 papers)

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Research

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13 pages, 1994 KB  
Communication
Injection Mold for Plastics Manufactured by Metal-FFF with Conformal Cooling Channels: A Proof-of-Concept Case
by José Enrique Solís, Juan Claver, Marta María Marín, Eva María Rubio and Amabel García-Domínguez
Machines 2025, 13(9), 784; https://doi.org/10.3390/machines13090784 (registering DOI) - 1 Sep 2025
Abstract
Injection molding is widely used for mass-producing plastic components, demanding precise thermal control to optimize cycle times and part quality. Traditional CNC-machined molds limit design flexibility and restrict advanced cooling features like conformal cooling channels (CCCs). Integrating CCCs improves cooling performance, reduces cycle [...] Read more.
Injection molding is widely used for mass-producing plastic components, demanding precise thermal control to optimize cycle times and part quality. Traditional CNC-machined molds limit design flexibility and restrict advanced cooling features like conformal cooling channels (CCCs). Integrating CCCs improves cooling performance, reduces cycle times, and offers more efficient, cost-effective designs. Additive manufacturing (AM), especially Metal-Fused Filament Fabrication (Metal-FFF), offers geometries unattainable by machining. While most mold research focuses on Laser Powder Bed Fusion (LPBF), the feasibility of Metal-FFF molds remains underexplored. This study presents the design, fabrication, and experimental evaluation of an injection mold produced via Metal-FFF with integrated CCCs. The process included computational design, resistance simulations, fabrication, debinding, sintering, and post-processing, followed by testing under injection molding conditions. Results show that Metal-FFF molds with CCCs boost cooling efficiency, cutting cycle times by about 30% compared to conventional molds, while offering greater design freedom and economic benefits. Nonetheless, issues such as porosity and shrinkage need further refinement to fully leverage this technology for industrial use. 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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12 pages, 2729 KB  
Article
Educational Robotics for Industry 4.0 and 5.0 with Wlkata Mirobot in Laboratory Process Modelling
by Miriam Pekarcikova, Peter Trebuna, Marek Kliment, Jana Kronova and Matus Matiscsak
Machines 2025, 13(9), 753; https://doi.org/10.3390/machines13090753 - 22 Aug 2025
Viewed by 334
Abstract
This study explores the integration of educational robotics into the development of digital competencies essential for Industry 4.0 and 5.0. These industrial paradigms are defined by automation, interconnected cyber-physical systems, value chain integration, and digitalisation. In this environment, digital skills become strategically vital. [...] Read more.
This study explores the integration of educational robotics into the development of digital competencies essential for Industry 4.0 and 5.0. These industrial paradigms are defined by automation, interconnected cyber-physical systems, value chain integration, and digitalisation. In this environment, digital skills become strategically vital. Didactic robotic platforms, such as the Wlkata Mirobot, offer students hands-on opportunities to develop these abilities in a practical and interdisciplinary context. When combined with technologies like digital twins, the Internet of Things, and simulation tools, educational robotics fosters both technical proficiency and adaptability to evolving industrial demands. The presented case study demonstrates the design, construction, and experimental setup of a functional laboratory mini-line using the Wlkata Mirobot. The focus is placed on layout design, robot programming, and simulation-based process optimization to reflect real industrial processes. This study also presents student feedback and performance indicators from repeated trials to illustrate the educational and operational potential of the solution. 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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19 pages, 2299 KB  
Article
A Supervised Machine Learning-Based Approach for Task Workload Prediction in Manufacturing: A Case Study Application
by Valentina De Simone, Valentina Di Pasquale, Joanna Calabrese, Salvatore Miranda and Raffaele Iannone
Machines 2025, 13(7), 602; https://doi.org/10.3390/machines13070602 - 12 Jul 2025
Viewed by 529
Abstract
Predicting workload for tasks in manufacturing is a complex challenge due to the numerous variables involved. In small- and medium-sized enterprises (SMEs), this process is often experience-based, leading to inaccurate predictions that significantly impact production planning, order management, and consequently the ability to [...] Read more.
Predicting workload for tasks in manufacturing is a complex challenge due to the numerous variables involved. In small- and medium-sized enterprises (SMEs), this process is often experience-based, leading to inaccurate predictions that significantly impact production planning, order management, and consequently the ability to meet customer deadlines. This paper presents an approach that leverages machine learning to enhance workload prediction with minimal data collection, making it particularly suitable for SMEs. A case study application using supervised machine learning models for regression, trained in an open-source data analytics, reporting, and integration platform (KNIME Analytics Platform), has been carried out. An Automated Machine Learning (AutoML) regression approach was employed to identify the most suitable model for task workload prediction based on minimising the Mean Absolute Error (MAE) scores. Specifically, the Regression Tree (RT) model demonstrated superior accuracy compared to more traditional simple averaging and manual predictions when modelling data for a single product type. When incorporating all available product data, despite a slight performance decrease, the XGBoost Tree Ensemble still outperformed the traditional approaches. These findings highlight the potential of machine learning to improve workload forecasting in manufacturing, offering a practical and easily implementable solution for SMEs. 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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15 pages, 4002 KB  
Article
Condition-Based Maintenance for Degradation-Aware Control Systems in Continuous Manufacturing
by Faisal Alsaedi and Sara Masoud
Machines 2025, 13(2), 141; https://doi.org/10.3390/machines13020141 - 12 Feb 2025
Viewed by 1419
Abstract
To enhance maintenance endeavors, it is imperative to gain a deep understanding of system degradation. In systems with degradation-aware control, observing degradation becomes particularly challenging. Even with sensors, such controllers continuously mitigate deviations to ensure the system operates within optimal limits. Here, we [...] Read more.
To enhance maintenance endeavors, it is imperative to gain a deep understanding of system degradation. In systems with degradation-aware control, observing degradation becomes particularly challenging. Even with sensors, such controllers continuously mitigate deviations to ensure the system operates within optimal limits. Here, we propose a framework explicitly tailored for degradation-aware control systems, built upon two main components: (1) degradation modeling to estimate and track hidden degradation over time and (2) a Long Short-Term Memory Autoencoder-Degradation Stage Detector (A-LSTMA-DSD) to define alarm and failure thresholds for enabling condition-based maintenance. In degradation modeling, the framework utilizes actuator measurements to model hidden degradation. Next, an A-LSTMA-DSD model is developed to flag anomalies, based on which alarm and failure thresholds are assigned. These dynamic thresholds are defined to ensure sufficient time for addressing maintenance requirements. Working with real data from a boiler unit in an oil refinery and focusing on steam leakages, our proposed framework successfully identified all failures and on average triggered alarm and failure thresholds 15 and 8 days in advance of failures, respectively. In addition to triggering these thresholds, our system outperforms baseline models, such as CNN, LSTM, ANN, ARIMA, and Facebook Profit, in identifying failures by 60% and 95%, respectively. 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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12 pages, 809 KB  
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
Cited by 1 | Viewed by 1054
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 KB  
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
Cited by 4 | Viewed by 4171
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 KB  
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
Cited by 3 | Viewed by 2201
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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Review

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35 pages, 3552 KB  
Review
A Review of the Industry 4.0 to 5.0 Transition: Exploring the Intersection, Challenges, and Opportunities of Technology and Human–Machine Collaboration
by Md Tariqul Islam, Kamelia Sepanloo, Seonho Woo, Seung Ho Woo and Young-Jun Son
Machines 2025, 13(4), 267; https://doi.org/10.3390/machines13040267 - 24 Mar 2025
Cited by 8 | Viewed by 8399
Abstract
The Industrial Revolution (IR) involves a centuries-long process of economic and societal transformation driven by industrial and technological innovation. From agrarian, craft-based societies to modern systems powered by Artificial Intelligence (AI), each IR has brought significant societal advancements yet raised concerns about future [...] Read more.
The Industrial Revolution (IR) involves a centuries-long process of economic and societal transformation driven by industrial and technological innovation. From agrarian, craft-based societies to modern systems powered by Artificial Intelligence (AI), each IR has brought significant societal advancements yet raised concerns about future implications. As we transition from the Fourth Industrial Revolution (IR4.0) to the emergent Fifth Industrial Revolution (IR5.0), similar questions arise regarding human employment, technological control, and adaptation. During all these shifts, a recurring theme emerges as we fear the unknown and bring a concern that machines may replace humans’ hard and soft skills. Therefore, comprehensive preparation, critical discussion, and future-thinking policies are necessary to successfully navigate any industrial revolution. While IR4.0 emphasized cyber-physical systems, IoT (Internet of Things), and AI-driven automation, IR5.0 aims to integrate these technologies, keeping human, emotion, intelligence, and ethics at the center. This paper critically examines this transition by highlighting the technological foundations, socioeconomic implications, challenges, and opportunities involved. We explore the role of AI, blockchain, edge computing, and immersive technologies in shaping IR5.0, along with workforce reskilling strategies to bridge the potential skills gap. Learning from historic patterns will enable us to navigate this era of change and mitigate any uncertainties in the future. 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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