Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

Journal Browser

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

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: 20 January 2026 | Viewed by 5008

Share This Special Issue

Special Issue Editors

Dr. Filipe Pereira

E-Mail Website
Guest Editor

1. INEGI–Institute of Science and Innovation in Mechanical and Industrial Engineering, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
2. MEtRICs Research Center, School of Engineering, University of Minho, Campus of Azurém, 4800-058 Guimarães, 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
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Carlos Felgueiras

E-Mail Website
Guest Editor

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
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Vítor Carvalho

E-Mail Website
Guest Editor

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
Special Issues, Collections and Topics in MDPI journals

Dr. Pedro M. B. Torres

E-Mail Website1 Website2
Guest Editor

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

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

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

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

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 148

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)

► Show Figures

Figure 1

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

Viewed by 470

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)

► Show Figures

Figure 1

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

Viewed by 824

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)

► Show Figures

Figure 1

Review

Jump to: Research

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 4 | Viewed by 1696

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)

► Show Figures

Figure 1

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 1 | Viewed by 836

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)

► Show Figures

Journal Menu

Journal Browser

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

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (5 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI