Emerging Trends in Robotics and Automation for Advanced Manufacturing

A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 1052

Special Issue Editors

Materials Engineering Division, Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA 94550, USA
Interests: additive manufacturing; automation and robotics; self-driving laboratories and autonomous research systems; data analytics and machine learning; computational materials science
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Guest Editor
Materials Engineering Division, Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA 94550, USA
Interests: machine learning and data analytics; automation and robotics; self-driving laboratories and autonomous research systems; additive manufacturing and sustainable manufacturing; experimental and computational mechanics; energy and climate sciences

Special Issue Information

Dear Colleagues, 

We are inviting you to submit your research for this Special Issue of the Journal of Manufacturing and Materials Processing published by MDPI on “Emerging Trends in Robotics and Automation for Advanced Manufacturing”. The fields of robotics and automation are driving a profound transformation in advanced manufacturing, enabling unprecedented levels of precision, adaptability, and operational efficiency. As industries pivot toward intelligent, data-driven, and autonomous production environments, innovations in robotic systems, machine learning, and cyber–physical integration are reshaping conventional manufacturing paradigms. 

This Special Issue aims at showcasing high-impact research on the convergence of robotics, automation, and artificial intelligence (AI) within the context of next-generation manufacturing. We welcome original contributions—both theoretical and applied—that advance the design, control, deployment, and integration of robotic and automated technologies.  

Topics of interest include, but are not limited to, the following: 

  1. Advanced robotic or automated systems in manufacturing environments;
  2. AI-driven robotics and automation for process optimization; 
  3. AI and machine learning for advanced robotic control;
  4. Integration between digital and automated manufacturing ecosystems; 
  5. Smart manufacturing systems and industrial IoT; 
  6. Robotics for additive, hybrid, and subtractive manufacturing processes; 
  7. Collaborative robots (cobots) and human–robot interaction; 
  8. Advanced robotics and automation for scientific discovery. 

Dr. Ziheng Wu
Dr. Aldair E. Gongora
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Manufacturing and Materials Processing 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 1800 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

  • self-driving laboratory
  • robotics and automation
  • advance manufacturing
  • human–robot interaction
  • robotics for scientific discovery
  • machine learning

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Published Papers (1 paper)

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Research

32 pages, 9104 KB  
Article
Validation and Generalization of Key Building Blocks for Cyber-Physical Systems in Manufacturing: Insights from Automotive Inspection and Assembly Use Cases
by Michael Gfoellner, Christoph Kribernegg, Stefan Koerner, Martin Schellander and Franz Haas
J. Manuf. Mater. Process. 2026, 10(4), 116; https://doi.org/10.3390/jmmp10040116 - 29 Mar 2026
Viewed by 588
Abstract
A key technological challenge for automotive manufacturers is producing multiple vehicle variants on a single production line. At the body-in-white shop of Magna’s complete vehicle plant in Graz, this is addressed through transportable positioning devices that serve as part carriers and adapters between [...] Read more.
A key technological challenge for automotive manufacturers is producing multiple vehicle variants on a single production line. At the body-in-white shop of Magna’s complete vehicle plant in Graz, this is addressed through transportable positioning devices that serve as part carriers and adapters between different products, while ensuring consistent geometric alignment throughout the process. Geometrical deviations in these devices can adversely impact product quality along the entire vehicle assembly chain. This paper presents the development and implementation of two patented use cases: a cyber-physical inspection system, fully operational in serial production, and a cyber-physical assembly system, tested successfully in the prototype phase. The first actively mitigates the effects of device deviations in real time, while the second enables the on-demand configuration of flexible, advanced positioning devices via precision part matching, effectively preventing systematic deviations. Challenges and insights from both systems are discussed. Four previously introduced building blocks for automating quality control processes are validated and generalized for broad applicability across manufacturing processes and project phases via cross-system comparative analysis: the integrated capture of process and product data, automated data analytics, automated decision-making, and autonomous process intervention. This work proposes a validated, scalable framework integrating the design and implementation of cyber-physical systems to support zero-defect manufacturing. Full article
(This article belongs to the Special Issue Emerging Trends in Robotics and Automation for Advanced Manufacturing)
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