Strategies for Modern Art Product Morphogenesis and Functional Optimization Based on Computer-Aided Design

Abstract

The research constructs a computer-aided design-based morphological design system for art products and processes it with a BP neural network and genetic algorithm for optimization. Computer-aided design techniques such as face segmentation, morphological proportion design, homogeneous curve drawing and organization, chamfer setting, and management methods are introduced to design the morphology of art products. By analyzing the error value of each part of the product in the design of sculpture products in this paper’s art product morphology design system, the performance of the product morphology design of this paper’s system is examined. The color characteristics of the art products are deconstructed and analyzed by comparing the hue, brightness, and purity of the designed sculpture samples. The mean square error values of each part of the sculpture product (head, arms, torso, legs) are 0.0046, 0.0019, 0.0073, and 0.0036, respectively, and the overall average mean square error is 0.0044, which makes the art product morphology design model based on computer-aided design of this paper have good accuracy. The values of hue, brightness, and purity of the main color of the sculpture product are relatively stable, with the range of hue being roughly 4~8 and the main range of brightness and purity being 20%~40% and 20%~40%, respectively. The brightness and purity of auxiliary colors and accent colors are both steady, and the purity of auxiliary colors is greater than that of the main color. The accent color has the largest range of hues in the selection of hues.