Description

This Master Prompt focuses on transforming CAD/CAM from a predominantly deterministic design-and-manufacturing workflow into an intelligent, data-driven and experimentally validated optimization environment. It guides researchers in defining controllable CAD/CAM variables, process parameters, constraints, performance objectives and experimental domains before selecting statistical, optimization, artificial-intelligence or machine-learning methods. The framework integrates Design of Experiments (DOE), Taguchi methods, regression, response modeling, artificial neural networks (ANN), surrogate modeling, cross-validation, multi-objective optimization and intelligent decision support where justified by the research problem.