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Intelligent Automation and Robotics for Future Factories: Vision Systems, Artificial Intelligence, and Machine Engineering

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

Deadline for manuscript submissions: 20 August 2026 | Viewed by 1616

Editors

Dr. Filipe Pereira

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. Miguel Oliveira

E-Mail Website
Guest Editor
Polytechnic School of Design, Management and Production Technologies Aveiro-Norte, University of Aveiro, Estrada do Cercal 449, 3720-509 Oliveira de Azeméis, Portugal
Interests: software engineering; AI; 3D modelling and programming; mobile development; distributed systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on practical and applied research in intelligent automation and robotics for future factories, with particular emphasis on machine vision, artificial intelligence, digitalization, and advanced machine engineering for real industrial environments. We welcome contributions on the design, integration, and deployment of robotic and automated systems; AI-driven inspection and quality control; perception and sensing technologies; edge/industrial IoT and data architectures; digital twins and simulation for manufacturing; human–robot collaboration; and methods that improve productivity, flexibility, safety, and sustainability in industrial machines and manufacturing lines. Both methodological and application-oriented works are encouraged, including validation in laboratory pilots or real factory case studies.

Dr. Filipe Pereira
Dr. Miguel Oliveira
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • intelligent automation
  • industrial robotics
  • machine vision
  • artificial intelligence
  • deep learning
  • quality inspection
  • edge computing
  • industrial IoT (IIoT)
  • digital twins
  • human–robot collaboration

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Published Papers (2 papers)

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Research

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35 pages, 5649 KB  
Article
Cross-Dataset Benchmarking of Deep Learning Models for Surface Defect Classification in Metal Parts
by Fábio Mendes da Silva, João Manuel R. S. Tavares, António Mendes Lopes and Antonio Ramos Silva
Appl. Sci. 2026, 16(6), 3022; https://doi.org/10.3390/app16063022 - 20 Mar 2026
Cited by 1 | Viewed by 851
Abstract
Accurate surface defect classification is critical for industrial quality control. Although Deep Learning achieves strong results on individual datasets, most prior studies benchmark only a narrow set of models under inconsistent pipelines, limiting comparability and industrial relevance. This work introduces the first systematic [...] Read more.
Accurate surface defect classification is critical for industrial quality control. Although Deep Learning achieves strong results on individual datasets, most prior studies benchmark only a narrow set of models under inconsistent pipelines, limiting comparability and industrial relevance. This work introduces the first systematic benchmark of ten architectures—CNNs (CNN, ResNet18/50), lightweight models (MobileNetV2, SuperSimpleNet, GhostNet, EfficientNetV2), Vision Transformers (Swin Transformer), a hybrid CNN–Transformer (CoAtNet), and a one-stage detector (YOLOv12)—across five public defect datasets (NEU-DET, X-SDD, KolektorSDD2, DAGM, MTDD) under a unified pipeline. Results show that Swin Transformer and CoAtNet achieve the best performance (mean F1-scores 90.8% and 85.5%), while EfficientNetV2 underperformed (41.9%), underscoring the need for domain-specific benchmarks. Lightweight models such as MobileNetV2, GhostNet, and SuperSimpleNet deliver competitive accuracy at much lower cost, offering practical solutions for edge deployment. By bridging the gap between academic benchmarks and manufacturing requirements, this study provides actionable guidance for selecting defect detection models in automated inspection. Full article
(This article belongs to the Special Issue Intelligent Automation and Robotics for Future Factories: Vision Systems, Artificial Intelligence, and Machine Engineering)
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Review

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30 pages, 4467 KB  
Review
Interoperability in Industrial Robotics: A Literature Review and Conceptual Path Toward a Universal Robot Protocol
by Vasco Fonseca, Ramiro Barbosa and Filipe Pereira
Appl. Sci. 2026, 16(11), 5217; https://doi.org/10.3390/app16115217 - 22 May 2026
Viewed by 399
Abstract
This work presents a literature review on interoperability in industrial robotics. The analysis of 45 selected studies reveals that existing approaches remain fragmented across communication, control abstraction, and semantic integration layers. The review synthesizes key developments in programming paradigms, communication technologies, and interoperability [...] Read more.
This work presents a literature review on interoperability in industrial robotics. The analysis of 45 selected studies reveals that existing approaches remain fragmented across communication, control abstraction, and semantic integration layers. The review synthesizes key developments in programming paradigms, communication technologies, and interoperability solutions in heterogeneous industrial environments. Based on the identified gaps, a conceptual interoperability framework, referred to as the Universal Robot Protocol (URP), is derived to support unified integration across system layers. URP is not proposed as an implemented protocol, but as a research-driven conceptual direction intended to integrate existing technologies within a coherent interoperability architecture. This contribution aims to support future research and the industrial adoption of interoperable robotic systems in Industry 4.0 and Industry 5.0 environments. Full article
(This article belongs to the Special Issue Intelligent Automation and Robotics for Future Factories: Vision Systems, Artificial Intelligence, and Machine Engineering)
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