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Machines
Special Issues
Artificial Intelligence and Robotics in Manufacturing and Automation
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Artificial Intelligence and Robotics in Manufacturing and Automation

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 404

Special Issue Editor

Prof. Dr. Daniel Roozbahani

E-Mail Website
Guest Editor
ARTEMIS Laboratory, Department of Robotics Engineering, Widener University, Chester, PA 19013, USA
Interests: artificial intelligence (AI) and robotics

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) and robotics are revolutionizing the manufacturing and automation sectors, driving efficiency, precision, and adaptability. With the increasing integration of smart technologies, industries are witnessing a transformation in production processes, predictive maintenance, quality control, and supply chain management. AI-powered systems enhance decision making, optimize production lines, and reduce downtime, while robotics enable automation with unprecedented speed and flexibility.

This Special Issue aims to explore the latest advancements, challenges, and applications of AI and robotics in manufacturing and automation. We invite original research and review articles covering topics such as AI-driven predictive analytics; collaborative robots (cobots); machine learning for process optimization, autonomous systems and human–robot interaction; swarm robotics—multi-robot coordination for adaptive and decentralized manufacturing systems; AI for quality control and defect detection—machine vision and deep learning for automatic inspection and anomaly detection; and energy-efficient AI for sustainable manufacturing—developing AI-driven strategies to reduce energy consumption. Contributions highlighting real-world case studies, emerging trends, and innovative solutions are highly encouraged.

By bringing together insights from academia and industry, this Special Issue seeks to foster knowledge exchange and provide a comprehensive understanding of how AI and robotics are shaping the future of smart manufacturing. We welcome researchers, engineers, and practitioners to submit their contributions and join the discourse on intelligent automation.

Prof. Dr. Daniel Roozbahani
Guest Editor

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

  • artificial intelligence in manufacturing
  • industrial robotics
  • smart automation
  • machine learning for production optimization
  • collaborative robots (cobots)
  • predictive maintenance with AI
  • human–robot interaction
  • autonomous systems in industry
  • AI-powered defect detection
  • swarm intelligence for robotics
  • AI for supply chain optimization

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Published Papers (1 paper)

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24 pages, 1967 KiB  
Review
A Review of 3D Shape Descriptors for Evaluating Fidelity Metrics in Digital Twin
by Md Tarique Hasan Khan, Soonhung Han, Tahir Abbas Jauhar and Chiho Noh
Machines 2025, 13(9), 750; https://doi.org/10.3390/machines13090750 - 22 Aug 2025
Viewed by 111
Abstract
Digital Twin (DTw) technology is a cornerstone of Industry 4.0, enabling real-time monitoring, predictive maintenance, and performance optimization across diverse industries. A key requirement for effective DTw implementation is high geometric fidelity—ensuring the digital model accurately represents the physical counterpart. Fidelity metrics provide [...] Read more.
Digital Twin (DTw) technology is a cornerstone of Industry 4.0, enabling real-time monitoring, predictive maintenance, and performance optimization across diverse industries. A key requirement for effective DTw implementation is high geometric fidelity—ensuring the digital model accurately represents the physical counterpart. Fidelity metrics provide a quantitative means to assess this alignment in terms of geometry, behavior, and performance. Among these, 3D shape descriptors play a central role in evaluating geometric fidelity, offering computational tools to measure shape similarity between physical and digital entities. This paper presents a comprehensive review of 3D shape descriptor methods and their applicability to geometric fidelity assessment in DTw systems. We introduce a structured taxonomy encompassing classical, structural, texture-based, and deep learning-based descriptors, and evaluate each in terms of transformation invariance, robustness to noise, computational efficiency, and suitability for various DTw applications. Building upon this analysis, we propose a conceptual fidelity metric that maps descriptor properties to the specific fidelity requirements of different application domains. This metric serves as a foundational framework for shape-based fidelity evaluation and supports the selection of appropriate descriptors based on system needs. Importantly, this work aligns with and contributes to the emerging ISO 30138 standardization initiative by offering a descriptor-driven approach to fidelity assessment. Through this integration of taxonomy, metric design, and standardization insight, this paper provides a roadmap for more consistent, scalable, and interoperable fidelity measurement in digital twin environments—particularly those demanding high precision and reliability. Full article
(This article belongs to the Special Issue Artificial Intelligence and Robotics in Manufacturing and Automation)
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