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Advanced Composite Modeling and Process System Engineering for Sustainable High-Performance Manufacturing

This special issue belongs to the section “Manufacturing Processes and Systems“.

Special Issue Information

Dear Colleagues,

This Special Issue explores the evolution of sustainable process engineering via the integration of advanced material testing, physics-based modeling, and AI-driven optimization. As industries such as construction, automotive, aerospace, and renewable energy strive to reduce their environmental impact while enhancing their performance, the convergence of multiscale simulation, thermal, rheological, mechanical analysis, and predictive analytics is proving essential.

We welcome contributions that bridge composite materials science and process systems engineering, with a focus on innovations that enhance durability, damage resistance, and resource efficiency. We will also focus on advanced composite material systems, including glass-reinforced applications and glass furnace technologies, supported by computational and experimental approaches that promote the intensification of processes, enable defect mitigation, and promote sustainability across diverse manufacturing environments.

This Special Issue reflects the collective expertise of the Guest Editors in progressive failure modeling, advanced composite design, and AI-driven process control. It aims to showcase transformative approaches that advance process intensification, defect mitigation, and sustainability across high-performance manufacturing systems.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Progressive failure modeling and damage prediction;
  • Multiphysics and multiscale simulation of composite and metamaterial systems;
  • AI-enabled process optimization and defect analytics;
  • Rheological and thermal analysis for sustainable manufacturing;
  • Digital twins and virtual testing workflows;
  • Bio-inspired and stitched composite structures;
  • Reduced Order Models (ROM) for accelerated simulation;
  • Data-driven process design and control.

We welcome original research, case studies, and reviews that demonstrate measurable advancements in process performance and sustainable engineering practices.

Dr. Steve D.C. Pham
Dr. Kwek-Tze Tan
Guest Editors

Dr. Bruno A. Purnode
Guest Editor Assistant

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. Processes 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

  • progressive failure analysis
  • fiberglass composites
  • composite material testing
  • rheology and thermal analysis
  • process modeling and simulation
  • multiscale modeling
  • predictive analytics in manufacturing
  • structural performance optimization
  • digital twin and reduced order models
  • durability and reliability modeling

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Processes - ISSN 2227-9717