Artificial Intelligence Enhanced Taguchi–RSM Framework for Intelligent and Sustainable Optimization

This chapter presents an AI-enhanced Taguchi–Response Surface Methodology (RSM) framework that merges statistical design with computational intelligence for intelligent and sustainable optimization. The Taguchi method provides robust parameter screening using orthogonal arrays, while RSM offers precise regression modeling and response visualization. Artificial intelligence tools, including neural networks and genetic algorithms, refine the hybrid process by capturing nonlinear relationships and adaptive patterns. The framework minimizes experimental effort, enhances prediction accuracy, and supports multi-objective optimization. Applications across manufacturing, civil engineering, materials processing, and energy systems demonstrate its versatility and sustainability. The proposed hybrid model bridges traditional experimentation with AI-driven automation, enabling data-driven innovation and efficient decision-making in the Industry 4.0 landscape.