Modelling vat photopolymerization: a comprehensive review and perspectives on digital twinning and advancing multi-wavelength processes

Vat photopolymerization (VPP) is a high-precision additive manufacturing technology that selectively cures photosensitive resins to create complex 3D structures. The emergence of multi-wavelength VPP – enabling capabilities such as wavelength-selective multi-material printing and photo-inhibition-aided processes – introduces new challenges in modelling and process control. Conventional single-wavelength models often fail to capture the coupled optical, thermal, chemical, and mechanical phenomena underlying material interactions and dynamic process behaviours, limiting predictive accuracy and optimisation. This review critically evaluates the state of VPP modelling, encompassing physics-based and data-driven approaches, and highlights their strengths, limitations, and challenges for both single- and multi-wavelength systems. Building on this analysis, an AI-driven digital twin framework is proposed, that integrates multiscale, multi-physics simulations with in-situ sensor data and machine learning–based surrogate models. This approach enables real-time monitoring, prediction, and adaptive control, improving process efficiency, material utilisation, and print quality. It also provides a pathway for designing and testing sustainable, multi-functional materials tailored for next-generation VPP systems. By combining high-fidelity simulations with adaptive AI, this work establishes a roadmap for intelligent, scalable, and sustainable VPP technologies, supporting high-resolution, multi-material, and multifunctional additive manufacturing across diverse industrial applications.HighlightsReview evaluates modelling methods for single- and multi-wavelength VPP.Framework integrates optical, thermal, kinetic and mechanical multi-physics models.AI-driven digital twin enables adaptive, real-time monitoring and process control.Hierarchical modelling links physics-based simulations with data-driven learning.Roadmap advances intelligent, sustainable, and multi-material VPP technologies.