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

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

Deadline for manuscript submissions: 28 February 2026 | Viewed by 23705

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Special Issue Editors

Prof. Dr. Ratna Babu Chinnam

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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 (11 papers)

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Research

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19 pages, 2239 KB

Open AccessArticle

Human-Centered Assessment of Product Complexity and Its Impact on Assembly Line Performances

by Amanda Aljinović Meštrović, Marina Crnjac Žižić, Nikola Gjeldum and Nikola Banduka

Machines 2025, 13(9), 855; https://doi.org/10.3390/machines13090855 - 16 Sep 2025

Viewed by 185

Abstract

Modern production systems face the challenges of increasing personalization of products, growing structural complexity, and the need for sustainability. In this context, it is necessary to include the human dimension in the optimization of production processes, especially in line with the principles of Industry 5.0 and the circular economy. In this paper, a complexity index is proposed that integrates the objective characteristics of the product and the subjectively perceived workload of the operator during assembly. The proposed index was used in the assembly line optimization process using linear programming to find a compromise solution between two often-conflicting objectives: maximizing output and minimizing complexity. In the analysis, two approaches to the initial balance of the assembly line were considered—by assembly time and by complexity of work elements—which were used as inputs to the optimization model. The results show that an approach that considers complexity from the operator’s point of view contributes to a more even load distribution but also can lead to higher overall performance. Such an approach confirms the importance of integrating the human factor into optimization processes and thus contributes to the creation of efficient, sustainable, and human-centric production systems of 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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34 pages, 951 KB

Open AccessArticle

The Digital Maturity of Small- and Medium-Sized Enterprises in the Saguenay-Lac-Saint-Jean Region

by Gautier George Yao Quenum, Stéfanie Vallée and Myriam Ertz

Machines 2025, 13(9), 835; https://doi.org/10.3390/machines13090835 - 9 Sep 2025

Viewed by 348

Abstract

This study examines the digital maturity of small- and medium-sized enterprises (SMEs) in the context of Industry 4.0. Despite growing awareness of the importance of digital transformation, many SMEs encounter structural and strategic challenges that impede their progress. Among their obstacles is the inadequacy of digital maturity models used to diagnose digital maturity levels in SMEs due to their typological, sectoral, geographical, and other specific characteristics. Using a constructivist and qualitative approach, we have developed a simplified, inclusive, and holistic assessment framework comprising six key dimensions (technology, culture, organization, people and human resources, strategic planning), associated with six progressive maturity levels. Our findings reveal that most SMEs studied in 2023 exhibit a beginner level of digital maturity. These enterprises are characterized by small-scale digital initiatives, often lacking a clear strategy, with limited or partial digitization of processes and heterogeneous technology adoption. The resulting self-assessment tool provides SMEs with practical guidance to launch, evaluate, and accelerate their digital transformation. This study contributes theoretically by proposing a practical digital maturity model and offering a tool to support SMEs and public policy. It highlights the need for tailored support, strategic alignment, and continuous training to unlock the full potential of Industry 4.0 in less urbanized and resource-constrained areas. 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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23 pages, 1783 KB

Open AccessArticle

Training for Industry 5.0: Evaluating Effectiveness and Mapping Emerging Competences

by Alexios Papacharalampopoulos, Olga Maria Karagianni, Matteo Fedeli, Philipp Lackner, Gintare Aleksandraviciene, Massimo Ippolito, Unai Elorza, Antonius Johannes Schröder and Panagiotis Stavropoulos

Machines 2025, 13(9), 825; https://doi.org/10.3390/machines13090825 - 7 Sep 2025

Viewed by 305

Abstract

As Industry 5.0 emerges as a human-centric evolution of industrial systems, this study investigates the effectiveness of training interventions in companies aimed at supporting the transition to Industry 5.0, emphasizing human-centric and resilient skill development. Drawing from multiple case studies involving engineers and operators, the research applies both meta-analysis and meta-regression to assess the added value of experiential learning approaches such as Teaching and Learning Factories. In addition, a novel methodology combining quantitative analyses with qualitative interpretation of emerging competences is presented. Principal Component Analysis and classification frameworks are employed to identify and organize key competence clusters along technological, organizational, and social dimensions. Special attention is given to the emergence of human-centered competences such as decision empowerment, which are shown to complement traditional operational capabilities. The findings confirm that experiential training interventions enhance both self-efficacy and adaptive operational readiness, while the use of fusion techniques enables the generalization of results across heterogeneous corporate settings. This work contributes to ongoing discourse on Industry 5.0 readiness by linking training design to strategic company incentives and highlights the role of structured evaluation in informing future policy and implementation pathways. 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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13 pages, 1994 KB

Open AccessCommunication

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 - 1 Sep 2025

Viewed by 469

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 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

Open AccessArticle

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 482

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. 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

Open AccessArticle

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 691

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 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

Open AccessArticle

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 1636

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 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

Open AccessArticle

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 1127

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 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

Open AccessArticle

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 4541

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 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

Open AccessArticle

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 4 | Viewed by 2321

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 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

Open AccessReview

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 10 | Viewed by 9722

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 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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