Methodological framework for evaluating the generative capabilities of AI assisted CAD parametric modeling for AM

Abstract. This study proposes a theoretical methodology for the analysis of the performance of artificial intelligence systems in the automated generation of parametric CAD models, focusing on additive manufacturing. In addition, it provides a steps-based method that includes the formulation of metrics, testing, statistical analysis, error mapping, and formulation of recommendations. This method also provides a composite risk indicator that is able to provide the quantification of limitations, as well as support the formulation of decisions in industry. This method is scalable, portable, and can be applied for other applications such as topological optimization and generative design. Apart from its applicability, this research provides the first theoretical framework within which the standardization of artificial intelligence processes within CAD models can be achieved.