基于KAB模型的设计要素组合推导

Deduction of Design Elements Combination Based on KAB model

中文关键词: AHP-BP神经网络  感性工学  造型意象  四旋翼无人机  设计要素组合

英文关键词: AHP-BP neural network  Kansei Engineering  modeling image  quadrotor UAV  design elements combination

中文摘要:

      目的 为了提高产品感性设计开发效率及意象匹配精度，采用定性和定量相结合的方法，提出一种基于层次分析法(AHP)与BP神经网络相结合的产品意象设计要素组合推导方法。方法 首先通过网络爬虫和亲和图法建立产品意象及造型数据库，以获得意象和设计要素;其次运用AHP构建产品层次结构模型及判断矩阵，计算意象及设计要素的权重系数;接着，基于形态拆解法与权重结果获得设计要素类型及优化组合编码，再运用语义差异法(SD)获取组合编码的用户感性意象均值;最后通过感性工学和AHP-BP神经网络构建KAB关键设计要素组合预测模型。结果 基于此模型预测四旋翼无人机设计方案，应用逼近理想解排序法(TOPSIS)对其进行验证评价，结果表明通过模型计算能够得到与目标感性意象高度匹配的设计要素组合编码。结论 基于此模型能够快速获得客观准确的产品意象造型设计要素组合，提高产品设计开发过程的效率。

英文摘要:

      The paper aims to improve the efficiency of product perceptual design development and the accuracy of image matching, and propose a method of product image design elements combination derivation based on analytic hierarchy process (AHP) and BP neural network by combining qualitative and quantitative methods. Firstly, the database of product image and modeling is established by web crawler and affinity graph method to obtain the image and design elements; Secondly, AHP is used to construct the product hierarchy model and judgment matrix, and the weight coefficients of image and design elements are calculated and analyzed; Then, based on the morphological decomposition method and weight results, the type of design elements and the optimal combination code are obtained. The semantic difference method (SD) is used to obtain the average value of the user perceptual image of the combination code; Finally, Kansei Engineering and AHP-BP neural network are used to build KAB key design elements combination prediction model. Based on this model, the design scheme of quadrotor UAV is predicted, and the technique for order preference by similarity to an ideal solution (TOPSIS) is used to verify and evaluate it. The results show that the combination coding of design elements highly matched with the perceptual image of the target can be obtained through model calculation. Based on this model, the objective and accurate product image modeling design elements combination can be quickly obtained, and the efficiency of product design and development process can be improved.