Design Evaluation System of AI-Generated Content in the Industrial Design of Construction Machinery

The application of Artificial Intelligence Generated Content (AIGC) technology in the industrial design of construction machinery has developed rapidly. However, its generated solutions differ significantly from designer-generated solutions, posing new challenges to traditional design evaluation methods. To address the evaluation challenges of AIGC solutions, this study proposes a design evaluation framework that combines comprehensiveness and efficiency. An evaluation framework based on the Analytic Hierarchy Process (AHP) was developed through expert interviews and literature analysis. The framework includes five criteria and fifteen sub-criteria to comprehensively assess the quality of AIGC design solutions. Subsequently, the evaluation indicators were streamlined and optimized to enhance efficiency. Experimental validation using practical construction machinery design cases demonstrated that this framework maintains scientific rigor while improving the efficiency of screening and decision-making for AIGC solutions. This study provides an efficient and reliable method for the preliminary scoring and filtering of AIGC solutions in the industrial design of construction machinery, contributing to improved decision-making processes in industrial design practices.