Graphic Design Effect Evaluation Based on CAD and Collaborative Design

Initially, this article delves into the obstacles encountered in graphic design, particularly the difficulties in anticipating design outcomes, along with the pivotal role played by CAD (Computer-Aided Design) technology and collaborative design in shaping the design process. To overcome these obstacles, we introduce a predictive model that seamlessly integrates CAD parametric modelling, parameters from the collaborative design environment, and the GNN (Graph Neural Network) algorithm. This model leverages design elements from the CAD system and real-time data from the collaborative design environment to deliver precise predictions of graphic design outcomes. When compared to other predictive models, our proposed model demonstrates superior predictive accuracy. Experimental results confirm its excellence, particularly when tackling intricate and nonlinear design challenges, where it exhibits remarkable adaptability and generalization capabilities. In essence, our CAD and collaborative design-based predictive model for graphic design outcomes offers a novel and efficient forecasting tool to the graphic design community. This model not only enhances design efficiency but also elevates design quality, empowering designers and decision-makers with more informed and precise design support. © 2024 U-turn Press LLC.