Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy

Abstract: AI is reshaping inverse design across manufacturing domain, enabling high-performance discovery in materials, products, and processes. However, purely data-driven approaches often struggle in realistic settings characterized by sparse data, high-dimensional design spaces, and nontrivial physical constraints. This perspective argues for a new generation of design systems that transcend black-box modeling by integrating domain knowledge, physics-informed learning, and intuitive human-AI interfaces. We first demonstrate how expert-guided sampling strategies enhance data efficiency and model generalization. Next, we discuss how physics-informed machine learning enables physically consistent modeling in data-scarce regimes. Finally, we explore how LLMs emerge as interactive design agents connecting user intent with simulation tools, optimization pipelines, and collaborative workflows. Through illustrative examples and conceptual frameworks, we advocate that inverse design in manufacturing should evolve into a unified ecosystem, where domain knowledge, physical priors, and adaptive reasoning collectively enable scalable, interpretable, and accessible AI-driven design systems.