Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy
This perspective addresses the problem of improving AI-driven design in manufacturing for engineers and researchers, but it is incremental as it builds on existing ideas without presenting new experimental results.
The paper tackles the challenge of applying AI to inverse design in manufacturing, where data-driven methods often fail due to sparse data and complex constraints, by proposing an integrated framework that combines domain knowledge, physics-informed machine learning, and human-AI interaction, demonstrated through an injection molding example.
Artificial intelligence (AI) is reshaping inverse design in manufacturing, enabling high-performance discovery in materials, products, and processes. However, purely data-driven approaches often struggle in realistic manufacturing settings characterized by sparse data, high-dimensional design spaces, and complex constraints. This perspective proposes an integrated framework built on three complementary pillars: domain knowledge to establish physically meaningful objectives and constraints while removing variables with limited relevance, physics-informed machine learning to enhance generalization under limited or biased data, and large language model-based interfaces to support intuitive, human-centered interaction. Using injection molding as an illustrative example, we demonstrate how these components can operate in practice and conclude by highlighting key challenges for applying such approaches in realistic manufacturing environments.