A maturity model for automation support in Generative Engineering and Design

The rapid advancements in machine learning and artificial intelligence have led to the emergence of novel software tools tailored specifically for engineering design processes. Collectively referred to as ‘Generative Engineering and Design’ (GE&D), these tools offer a wide range of new capabilities that enhance and support various tasks within product development. They differ in their functional scope, methodological foundations, and modes of user interaction, offering diverse forms of computational support to designers. In prior work, a procedural model has been proposed to characterize the interaction between designers and GE&D systems. This model outlines an iterative workflow, encompassing generation, evaluation, parameter optimization, and interpretation that facilitates the transition from the problem space to viable design solutions. Building upon this foundation, this contribution addresses the gap in the procedural model between the capabilities of GE&D tools and the varying degrees of automation support they offer by introducing a maturity model for AI-based support in GE&D environments. This model distinguishes between different levels of automation support within different stages of product development workflows. The maturity levels define and categorize the nature of support across specific design tasks, while also clarifying the distribution of roles and responsibilities between human designers and computational systems. By providing a common framework and taxonomy, it also supports systematic assessment, comparison, and strategic planning for the adoption of GE&D tools. It helps stakeholders identify current capabilities, uncover gaps, and make informed decisions about tool selection, process integration, and future development priorities. To validate and illustrate the model’s applicability, different design process case studies are presented. The studies demonstrate the development of design solutions across various stages of automation, underscoring the practical relevance of the proposed maturity model for effective use of GE&D tools within engineering workflows.