Simulation-Driven Innovation in Composites Manufacturing

Composites have become key materials for strategic industries such as aerospace, new energy vehicles, and high-end equipment due to their superior specific strength, specific modulus, and designability. However, their manufacturing process involves complex physicochemical changes and multi-scale structural evolution, and the black-box relationship between process parameters and final performance has long constrained the high-quality, high-efficiency, and low-cost manufacturing of products. In recent years, with the rapid development of high-performance computing, multiphysics simulation, and artificial intelligence (AI), simulation technology is evolving from a traditional auxiliary analysis tool into a core engine driving integrated innovation in design-manufacturing-performance of composite materials. High-fidelity simulation can reveal the process mechanism, optimize manufacturing parameters, predict defects, and shorten the R&D cycle, laying a theoretical foundation for the intelligent and precise control of composites manufacturing.

This Research Topic aims to promote a deeper mechanistic understanding and foster model innovation by showcasing advanced simulation methods spanning micro to macro scales. Special attention is given to elucidating key phenomena such as resin flow, curing reactions, fiber deformation, interface formation, and defect generation. Another focus is the advancement of integrated process-performance design, encouraging simulation-driven optimization research in areas like additive manufacturing (3D printing), automated layup, resin transfer molding, and autoclave molding, with the goal of building a bridge between manufacturing processes and service performance prediction. Additionally, the research seeks to accelerate the digital transformation of composites manufacturing by exploring innovative paradigms that combine digital twins, AI, machine learning (ML), and simulation technologies. These approaches are highlighted for their potential in process monitoring, online optimization, quality control, and reliability assessment, thereby further advancing the digitalization and intelligent development of composites manufacturing.

This collection welcomes original research papers and review articles, covering, but not limited to, the following topics:

• Multi-scale and multiphysics simulation: modeling from microscopic (fiber/matrix), mesoscopic (monolayer) to macroscopic (structural) scales, involving rheology, thermochemistry, mechanics, and their coupling effects.
• Advanced manufacturing process simulation: simulation and optimization of processes such as automated fiber placement, liquid molding, filament winding, extrusion molding, autoclave molding, subtractive machining, and additive manufacturing.
• Defect prediction and process optimization: modeling, prediction, and suppression strategies for manufacturing defects such as porosity, dry spots, wrinkles, residual stress, and deformation.
• Digital and intelligent methods: ML proxy models based on simulation data, AI inversion of process parameters, construction of digital twins of manufacturing processes, and uncertainty quantification analysis.
• Innovative materials and process modeling: modeling methods for green and sustainable composite materials (such as bio-based and recyclable materials), novel smart materials, and their unique manufacturing processes.

We sincerely invite researchers from academia and industry to submit their papers, share their insights and findings, and jointly promote the field of composites manufacturing and simulation to a new height.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Mini Review
• Original Research
• Perspective
• Review

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Mini Review
• Original Research
• Perspective
• Review

Keywords: Machine learning, surrogate models, Digital twins, Composite manufacturing simulation, multiphysics modeling, Defect prediction, Process optimization, AI, ML

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.