Hyper-Adaptive Generative Design for Personalized Micro-Additive Manufacturing: A Bayesian Optimization Framework

**Abstract:** This paper presents a novel framework for hyper-adaptive generative design optimized for personalized micro-additive manufacturing (µAM). Traditional generative design processes often face limitations in rapidly adapting to the unique material and geometric constraints inherent in µAM. Our approach leverages a Bayesian Optimization (BO) framework integrated with a multi-fidelity simulation pipeline and advanced materials data analytics to achieve significantly faster convergence and higher-quality designs for personalized micro-devices. The framework allows for real-time parameter tuning of generative algorithms, optimized for micro-scale mechanical properties and biocompatibility requirements, leading to demonstrable improvements in customization efficiency and design fidelity. This represents a significant advancement facilitating on-demand production of highly customized micro-devices with unprecedented precision and material optimization.