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Applications of Artificial Intelligence in Industry 4.0/5.0: Innovations, Challenges, and Future Directions

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 January 2026) | Viewed by 29466

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Special Issue Editors

Dr. Filipe Pereira

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1. Faculdade de Engenharia, Universidade do Porto (FEUP), Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
2. Instituto Superior de Engenharia do Porto (ISEP), Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015 Porto, Portugal
3. Institute of Science and Innovation in Mechanical and Industrial Engineering (INEGI), Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
4. Research Center in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
Interests: automation and industrial control; Industry 4.0; mechatronics; artificial intelligence; industrial, mobile and colaborative robots and industrial network protocols and advanced communication networks
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Prof. Dr. Carlos Felgueiras

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CIETI, ISEP, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015 Porto, Portugal
Interests: energy quality; energy and buildings; energy and environmental indicators; renewable energy systems; sustainable energy systems; engineering education
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Prof. Dr. Vítor Carvalho

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1. The Applied Artificial Intelligence Laboratory (2Ai) of the School of Technology (EST), Polytechnic Institute of Cávado and Ave (IPCA), 4750-810 Barcelos, Portugal
2. Algoritmi R&D Centre, Minho University, 4710-057 Braga, Portugal
Interests: sensors; data acquisition; serious games; education; machine learning
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Dr. Pedro M. B. Torres

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1. Polytechnic Institute of Castelo Branco, Av. Pedro Álvares Cabral No 12, 6000-084 Castelo Branco, Portugal
2. SYSTEC—Research Center for Systems and Technologies, ARISE—Advanced Production and Intelligent Systems Associated Laboratory, 4200-465 Porto, Portugal
Interests: electronics; instrumentation; automation; control; robotics; cyber-physical systems; computer vision; image processing and machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of artificial intelligence (AI) in industrial environments is revolutionizing production, automation, and process management, surpassing the limits of Industry 4.0 and paving the way for Industry 5.0. This Special Issue invites original research, case studies, and review articles that present AI applications in diverse industrial areas. Topics of interest include AI-driven predictive maintenance, quality control, autonomous optimization, vision-based inspection, mobile robotics and smart navigation in industrial environments, the development of smart sensors for industrial AI, automation and control based on neural networks and deep learning, and finally AI for energy management and industrial energy efficiency. Additionally, we encourage applications with an focusing on integrating AI into cyber–physical systems, digital twins, collaborative and autonomous robotics, and industrial internet of things (IIoT) frameworks, highlighting how these advances drive smart, data-driven manufacturing.

Dr. Filipe Pereira
Prof. Dr. Carlos Felgueiras
Prof. Dr. Vítor Carvalho
Dr. Pedro M. B. Torres
Guest Editors

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Keywords

industrial artificial intelligence
Industry 4.0
Industry 5.0
predictive maintenance
quality control
cyber–physical systems
collaborative robotics
digital twins
IIoT
smart factories

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Published Papers (10 papers)

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Research

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28 pages, 11621 KB

Open AccessArticle

AI-Driven Innovation in Manufacturing Digitalization: Real-Time Predictive Models

by Amir M. Horr, Sofija Milicic and David Blacher

Appl. Sci. 2025, 15(24), 13225; https://doi.org/10.3390/app152413225 - 17 Dec 2025

Viewed by 1462

Abstract

The digital transformation of manufacturing is accelerating through the integration of artificial intelligence (AI), particularly via real-time predictive models. These models enable manufacturers to transition from reactive to proactive strategies, intelligent optimization and decision-making. Within the frameworks of Industry 4.0 and Industry 5.0, which emphasize technologies such as cyber-physical systems, cloud computing, and human-centric innovation, AI-driven data models are pivotal for achieving smart, adaptive, and sustainable production systems. This paper investigates the impact of AI-based predictive modeling on manufacturing digitalization and its future potential. It examines how these models contribute to advanced frameworks such as online process advisory systems, digital shadows, and digital twins, while addressing their limitations and implementation challenges. Furthermore, the study reviews current practices in real-time data modeling across manufacturing processes—including direct-chill casting—supported by real-world case studies. These examples illustrate both the practical benefits and technical hurdles of deploying AI in dynamic industrial environments. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Industry 4.0/5.0: Innovations, Challenges, and Future Directions)

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37 pages, 12674 KB

Open AccessArticle

Efficient Neural Modeling of Wind Power Density for National-Scale Energy Planning: Toward Sustainable AI Applications in Industry 5.0

by Mario Molina-Almaraz, Luis Octavio Solís-Sánchez, Luis E. Bañuelos-García, Celina L. Castañeda-Miranda, Héctor A. Guerrero-Osuna and Eduardo García-Sánchez

Appl. Sci. 2025, 15(24), 13000; https://doi.org/10.3390/app152413000 - 10 Dec 2025

Cited by 1 | Viewed by 794

Abstract

This study presents an efficient and reproducible framework for estimating wind power density (WPD) across Mexico using a Dense Neural Network (DNN) trained exclusively on ERA5 and ERA5-Land reanalysis data. The model is designed as a computationally efficient surrogate that reproduces the statistical behavior of the ERA5 benchmark while enabling national-scale WPD mapping and short-term projections at minimal computational cost. Meteorological variables—including wind components at 10 m and 100 m, surface temperature, pressure, and terrain elevation—were harmonized on a 0.25° grid for the 1971–2024 period. A chronological dataset split (70-20-10%) was applied to realistically evaluate forecasting capability. The optimized DNN architecture (512-256-128 neurons) achieved high predictive performance (R² ≈ 0.91, RMSE ≈ 6.2 W/m²) and accurately reproduced spatial patterns and seasonal variability, particularly in high-resource regions such as Oaxaca and Baja California. Compared with deeper neural architectures, the proposed model reduced training time by more than 60% and energy consumption by approximately 40%, supporting principles of sustainable computing and Industry 5.0. The resulting WPD fields, delivered in interoperable NetCDF formats, can be directly integrated into decision-support tools for wind-farm planning, smart-grid management, and long-term renewable-energy strategies in data-scarce environments. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Industry 4.0/5.0: Innovations, Challenges, and Future Directions)

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17 pages, 1252 KB

Open AccessArticle

Optimization of an Automated Substrate Irrigation System Using the SAC Reinforcement Learning Agent

by Žydrūnas Kavaliauskas, Giedrius Blažiūnas and Igor Šajev

Appl. Sci. 2025, 15(23), 12715; https://doi.org/10.3390/app152312715 - 1 Dec 2025

Viewed by 801

Abstract

This study presents the optimization of an automated mushroom substrate irrigation system by integrating a Soft Actor-Critic (SAC) reinforcement learning agent with a recursive LSTM prediction model. The system, based on a Siemens S7-1200 PLC, CS650 dielectric sensors, and an Ethernet-based data architecture, provides real-time control of humidity, temperature, and electrical conductivity. Experimental data analysis shows that the SAC agent increases the episodic reward from 20–32 to 90–100 units over 200 episodes, stably maintaining the substrate moisture in the range of 61–65%. The LSTM model achieved a Validation Loss of 0.016–0.022, accurately predicting the hydro-physical parameters. Compared to traditional PID controllers, the SAC-based system reduces humidity deviations by 35–40%, reduces the risk of overwatering and drying out, and increases mycelium colonization. The results confirm that the developed cyber-bioprocess platform increases the stability of the mushroom cultivation process, water use efficiency, and product quality and shows potential for industrial application, which must be validated in larger-scale trials. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Industry 4.0/5.0: Innovations, Challenges, and Future Directions)

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21 pages, 824 KB

Open AccessArticle

Biases in AI-Supported Industry 4.0 Research: A Systematic Review, Taxonomy, and Mitigation Strategies

by Javier Arévalo-Royo, Francisco-Javier Flor-Montalvo, Juan-Ignacio Latorre-Biel, Emilio Jiménez-Macías, Eduardo Martínez-Cámara and Julio Blanco-Fernández

Appl. Sci. 2025, 15(20), 10913; https://doi.org/10.3390/app152010913 - 11 Oct 2025

Viewed by 3095

Abstract

Industrial engineering research has been reshaped by the integration of artificial intelligence (AI) within the framework of Industry 4.0, characterized by the interplay between cyber-physical systems (CPS), advanced automation, and the Industrial Internet of Things (IIoT). While this integration opens new opportunities, it also introduces biases that undermine the reliability and robustness of scientific and industrial outcomes. This article presents a systematic literature review (SLR), supported by natural language processing techniques, aimed at identifying and classifying biases in AI-driven research within industrial contexts. Based on this meta-research approach, a taxonomy is proposed that maps biases across the stages of the scientific method as well as the operational layers of intelligent production systems. Statistical analysis confirms that biases are unevenly distributed, with a higher incidence in hypothesis formulation and results dissemination. The study also identifies emergent AI-related biases specific to industrial applications such as predictive maintenance, quality control, and digital twin management. Practical implications include stronger reliability in predictive analytics for manufacturers, improved accuracy in monitoring and rescue operations through transparent AI pipelines, and enhanced reproducibility for researchers across stages. Mitigation strategies are then discussed to safeguard research integrity and support trustworthy, bias-aware decision-making in Industry 4.0. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Industry 4.0/5.0: Innovations, Challenges, and Future Directions)

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20 pages, 2979 KB

Open AccessArticle

Computer Vision-Enabled Construction Waste Sorting: A Sensitivity Analysis

by Xinru Liu, Zeinab Farshadfar and Siavash H. Khajavi

Appl. Sci. 2025, 15(19), 10550; https://doi.org/10.3390/app151910550 - 29 Sep 2025

Cited by 5 | Viewed by 4128

Abstract

This paper presents a comprehensive sensitivity analysis of the pioneering real-world deployment of computer vision-enabled construction waste sorting in Finland, implemented by a leading provider of robotic recycling solutions. Building upon and extending the findings of prior field research, the study analyzes an industry flagship case to examine the financial feasibility of computer vision-enabled robotic sorting compared to conventional sorting. The sensitivity analysis covers cost parameters related to labor, wages, personnel training, machinery (including AI software, hardware, and associated components), and maintenance operations, as well as capital expenses. We further expand the existing cost model by integrating the net present value (NPV) of investments. The results indicate that the computer vision-enabled automated system (CVAS) achieves cost competitiveness over conventional sorting (CS) under conditions of higher labor-related costs, such as increased headcount, wages, and training expenses. For instance, when annual wages exceed EUR 20,980, CVAS becomes more cost-effective. Conversely, CS retains cost advantages in scenarios dominated by higher machinery and maintenance costs or extremely elevated discount rates. For example, when the average machinery cost surpasses EUR 512,000 per unit, CS demonstrates greater economic viability. The novelty of this work arises from the use of a pioneering real-world case study and the improvements offered to a comprehensive comparative cost model for CVAS and CS, and furthermore from clarification of the impact of key cost variables on solution (CVAS or CS) selection. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Industry 4.0/5.0: Innovations, Challenges, and Future Directions)

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20 pages, 2752 KB

Open AccessArticle

Development and Optimization of an Automated Industrial Wastewater Treatment System Using PLC and LSTM Neural Network

by Žydrūnas Kavaliauskas, Giedrius Blažiūnas, Igor Šajev, Aleksandras Iljinas and Dovilė Gimžauskaitė

Appl. Sci. 2025, 15(16), 8990; https://doi.org/10.3390/app15168990 - 14 Aug 2025

Cited by 4 | Viewed by 3725

Abstract

This study presents an automated industrial wastewater treatment system based on Siemens programmable logic controller (PLC) that optimizes reagent dosing, aeration, sedimentation, and sludge separation. The system uses accurate pH sensors, dosing pumps, solenoid valves, and a human–machine interface (HMI), and real-time monitoring is provided by a Teltonika TRB255 communication module (<45 sec. response time). As a result, the treatment cycle time was reduced by 31%, reagent consumption by 30%, and operator intervention was reduced from 95 to less than 15 min per day, achieving a pollutant removal efficiency of 89%. A two-layer LSTM architecture developed on the PyTorch platform predicts pH (6.7–7.7), temperature (12–20 °C), and reagent consumption (~9.8 kg/cycle). The model was trained with 240 h of data (64 neurons, learning rate 0.001). The validation loss remained stable, indicating reliable learning. The study confirms that AI-based automation provides greater process stability, meets environmental standards, and promotes sustainable resource use. The scientific novelty of this study is the application of an advanced long short-term memory (LSTM) model to predict wastewater treatment process parameters, allowing for accurate prediction of pH, temperature, flow, and reagent consumption, etc. This provides an opportunity to optimize the process and reduce costs, while ensuring high treatment efficiency and stability. Although there are several publications on the application of artificial intelligence models in the field of industrial wastewater treatment, this is a relatively new field, and there are little data in the scientific literature. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Industry 4.0/5.0: Innovations, Challenges, and Future Directions)

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Review

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17 pages, 910 KB

Open AccessReview

A Framework for Integrating Robotic Process Automation with Artificial Intelligence Applied to Industry 5.0

by Leonel Patrício, Leonilde Varela, Zilda Silveira, Carlos Felgueiras and Filipe Pereira

Appl. Sci. 2025, 15(13), 7402; https://doi.org/10.3390/app15137402 - 1 Jul 2025

Cited by 12 | Viewed by 7712

Abstract

The transition to Industry 5.0 highlights the growing integration of Robotic Process Automation (RPA) and Artificial Intelligence (AI) in industrial ecosystems. However, adoption remains fragmented, lacking standardized frameworks to align intelligent automation with human-centric principles. While RPA improves operational efficiency and AI enhances cognitive decision-making, challenges such as organizational resistance, interoperability, and ethical governance hinder scalable and sustainable implementation. The envisioned scenario involves seamless RPA-AI integration, fostering human–machine collaboration, operational resilience, and sustainability. Expected outcomes include (1) hyperautomation for efficiency gains, (2) agile, data-driven decision-making, (3) sustainable resource optimization, and (4) an upskilled workforce focusing on innovation. This study proposes a structured five-stage framework for RPA-AI deployment in Industry 5.0, combining automation, cognitive enhancement, and human–machine symbiosis. A systematic literature review (PICO method) identifies gaps and supports the framework’s design, validated through operational, human-impact, and sustainability metrics. Incorporating ethical governance and continuous upskilling, the model ensures technological advancement aligns with societal and environmental values. Results demonstrate its potential as a roadmap for responsible digital transformation, balancing efficiency with human-centricity. Future research should focus on empirical validation and sector-specific adaptations. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Industry 4.0/5.0: Innovations, Challenges, and Future Directions)

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22 pages, 4096 KB

Open AccessReview

AI, Optimization, and Human Values: Mapping the Intellectual Landscape of Industry 4.0 to 5.0

by Albérico Travassos Rosário and Ricardo Jorge Gomes Raimundo

Appl. Sci. 2025, 15(13), 7264; https://doi.org/10.3390/app15137264 - 27 Jun 2025

Cited by 7 | Viewed by 1941

Abstract

This study conducts a systematic bibliometric literature review to explore the conceptual and technological transition from Industry 4.0 to Industry 5.0, focusing on the roles of artificial intelligence (AI), optimization, and human values. Applying the PRISMA 2020 protocol, the analysis includes 53 peer-reviewed sources from the Scopus database, emphasizing the integration of advanced technologies such as cyber–physical systems, the Internet of Things, collaborative robotics, and explainable AI. While Industry 4.0 is marked by intelligent automation and digital connectivity, Industry 5.0 introduces a human-centric paradigm emphasizing sustainability, resilience, and co-creation. The findings underscore the significance of human–machine collaboration, process personalization, AI education, and ethical governance as foundational pillars of this new industrial era. This review highlights the emerging role of enabling technologies that reconcile technical performance with social and environmental values, promoting a more inclusive and sustainable model for industrial development. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Industry 4.0/5.0: Innovations, Challenges, and Future Directions)

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Other

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31 pages, 2332 KB

Open AccessSystematic Review

A Systematic Review and Taxonomy of Machine Learning Methods for Process Optimization and Control in Laser Welding

by Jan Voets, Hasan Tercan, Tobias Meisen and Cemal Esen

Appl. Sci. 2026, 16(3), 1568; https://doi.org/10.3390/app16031568 - 4 Feb 2026

Viewed by 1174

Abstract

Laser welding is widely used in complex manufacturing processes and valued for its reliability, flexibility, and high energy density. However, achieving the desired weld quality requires the detection and, ideally, the prevention of defects. Besides other methods, machine learning (ML) has been integrated into laser welding with the primary goal of process optimization and quality improvement, for example, by enabling process adaptation before or during welding to reduce defects. This survey systematically reviews publications from 2015 to 2025 that integrate machine learning and deep learning methods into laser welding optimization or adaptation processes. An extensive analysis identifies which parts of the process and for what purposes ML methods are researched and implemented and how they are evaluated, as well as the sensors, lasers, and materials involved. Furthermore, the findings are analyzed and organized into taxonomies that define overarching meta-categories into which existing approaches can be classified and contextualized. The results reveal that various ML approaches are applied for tasks, such as surrogate modeling, process planning, direct control, and virtual sensing and monitoring. Although many different control parameters and optimization targets are considered, laser power and welding speed dominate as the most frequently adjusted parameters, while penetration depth and weld geometry-related properties are the most common optimization targets. Finally, the survey identifies major challenges, including the lack of benchmarking datasets, standardized evaluation protocols, and interpretable models. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Industry 4.0/5.0: Innovations, Challenges, and Future Directions)

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32 pages, 8971 KB

Open AccessSystematic Review

Systematic Review of Reinforcement Learning in Process Industries: A Contextual and Taxonomic Approach

by Marco Antonio Paz Ramos and Axel Busboom

Appl. Sci. 2025, 15(24), 12904; https://doi.org/10.3390/app152412904 - 7 Dec 2025

Cited by 3 | Viewed by 2778

Abstract

The process industry (PI) plays a vital role in the global economy and faces mounting pressure to enhance sustainability, operational agility, and resource efficiency amid tightening regulatory and market demands. Although artificial intelligence (AI) has been explored in this domain for decades, its adoption in industrial practice remains limited. Recently, machine learning (ML) has gained momentum, particularly when integrated with core PI systems such as process control, instrumentation, quality management, and enterprise platforms. Among ML techniques, reinforcement learning (RL) has emerged as a promising approach to tackle complex operational challenges. In contrast to conventional data-driven methods that focus on prediction or classification, RL directly addresses sequential decision making under uncertainty, a defining characteristic of dynamic process operations. Given RL’s growing relevance, this study conducts a systematic literature review to evaluate its current applications in the PI, assess methodological developments, and identify barriers to broader industrial adoption. The review follows the PRISMA methodology, a structured framework for identifying, screening, and selecting relevant publications. This approach ensures alignment with a clearly defined research question and minimizes bias, focusing on studies that demonstrate meaningful industrial applications of RL. The findings reveal that RL is transitioning from a theoretical construct to a practical tool, particularly in the chemical sector and for tasks such as process control and scheduling. Methodological maturity is improving, with algorithm selection increasingly tailored to problem-specific requirements and a trend toward hybrid models that integrate RL with established control strategies. However, most implementations remain confined to simulated environments, underscoring the need for real-world deployment, safety assurances, and improved interpretability. Overall, RL exhibits the potential to serve as a foundational component of next-generation smart manufacturing systems. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Industry 4.0/5.0: Innovations, Challenges, and Future Directions)

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