摘要
目的 交互式遗传算法(IGA)是设计方案优化的常用算法,需要基于可视化种群方案做出评价。目前材质设计自动批量生成可视化种群的技术尚不完善,拟解决这一瓶颈问题,为智能优化技术服务于CMF设计提供技术基础。方法 对梭织布艺沙发的纹理组织形式进行编码,驱动凹凸贴图的自动生成,开发3D产品CMF设计方案的批量化构建技术,并引入了美观度与视觉体感两个情感化评价指标,通过评价数据识别隐含的设计师意象信息,推进方案的优化与迭代。结果 实现了布艺沙发CMF可视化方案的批量生成及其情感化评价、交叉重组、变异等系列遗传操作,以推动CMF设计方案优化。结论 案例应用表明,所提出的纹理编码驱动的凹凸贴图自动生成方法对3D产品CMF设计方案的批量生成在技术上是可行的,自动渲染的CMF方案在效率和真实感方面可以满足情感指标的交互评价需求。
Interactive Genetic Algorithm(IGA)is a common algorithm for design solution optimization,which needs to be evaluated based on visual population solutions.The technology for automatic batch generation of visual populations for material design is currently imperfect.The work aims to address this bottleneck and provide a technical basis for intelligent optimization techniques to serve CMF design.The texture organization of woven fabric sofas was coded to drive the automatic generation of bump maps,develop a batch construction technique for 3D product CMF design solutions.Two emotional evaluation indicators,aesthetics and visual body feeling were introduced to identify the implicit designer's imagery information through the evaluation data and promote the optimization and iteration of solutions.A series of genetic operations such as batch generation of CMF visualization solutions for fabric sofas and their emotive evaluation,cross recombination,variation,etc.were achieved to promote the optimization of CMF design solutions.The case application shows that the proposed texture coding-driven bump mapping automatic generation method is technically feasible for the batch generation of 3D product CMF design solutions,and the automatically rendered CMF solutions can meet the interactive evaluation needs of emotional metrics in terms of efficiency and realism.
作者
张幂
陈庆
刘肖健
ZHANG Mi;CHEN Qing;LIU Xiao-jian(Zhejiang University of Technology,Hangzhou 310023,China;China Academy of Art,Hangzhou 310024,China)
出处
《包装工程》
CAS
北大核心
2023年第16期79-88,共10页
Packaging Engineering
基金
国家社科基金艺术学重大项目(20ZD09)。
