A NOVEL ANALYTICAL FRAMEWORK FOR PERCEPTUALLY ADAPTIVE 3D PRINTING SYSTEMS

In order to match created items with human perceptual expectations, including visual, tactile, and functional quality, perceptually adaptable 3D printing combines real-time sensing, computational modeling, and AI-driven control. Conventional additive manufacturing frequently ignores user-centered perception in favor of geometric precision, which can have an impact on usability, acceptance, and pleasure. A thorough analytical framework for perceptually adaptive 3D printing systems is presented in this review. It covers the principles of human perception, adaptive system architectures, AI-driven predictive and corrective models, materials and process considerations, applications in the biomedical, aerospace, consumer, and assistive domains, challenges, and future directions. Digital twins, machine learning, reinforcement learning, multimodal sensing, and cyber-physical integration are important technical enablers. While addressing constraints including perceptual variability, data scarcity, computational complexity, and ethical considerations, the review identifies opportunities to improve quality, personalization, and sustainability. This work offers a roadmap for creating human-centered, adaptive additive manufacturing systems that maximize both functional performance and perceptual quality by combining recent developments.