基于三维小波变换的农机CAD模型特征提取与评价方法

为提高三维CAD模型特征提取准确度,满足农业机械制造领域CAD模型智能化检索需求,提出一种三维CAD模型的特征提取与评价方法。首先对农业机械模型库中三维模型进行平移归一化、旋转归一化、尺度归一化处理,规范化后的CAD模型进行三维离散小波变换,CAD模型经过单层三维小波变换后被表示为8个维度的频域系数子空间;然后对低频子空间LxLyLz进行二次小波变换,二层级小波变换后单个CAD模型被分解为15个特征量,采用AHP层次分析法确定不同频域空间特征量权重,以距离度量法定义含有权重系数的相似性度量值,完成不同模型间特征提取及相似性比较,计算并建立三维CAD模型频域特征索引表;最后以VS2010与Matlab 2016b为开发环境,以Open Cascade为几何造型平台,使用ESB通用模型库及农业机械零部件参数化模型库验证算法有效性。在ESB模型库中T型零件检索结果表明,在保持相同查准率条件下,本文小波变换算法排序误差更小。拨禾轮CAD模型检索结果显示,相似性评价度量方法不仅考虑模型的形态相似性,同时兼顾了应用领域语义相似性,算法实用性高。农机装配体检索实例应用结果表明,借助本文检索算法,可从海量模型库中检索出所需模型,根据实际设计要求对检索出的相似模型进行编辑和修改,可快速完成零部件模块化设计。三维小波变换检索算法查准率受小波函数性质影响,coif类小波函数具有较强的检索适用性,层次分析法能有效区分不同分解层权重,二层级小波变换能够达到现阶段机械零件模型检索精度要求。小波变换设计重用检索算法可满足模型检索设计需求,拓宽设计者设计思路,促进智能化设计。

Feature Extraction and Evaluation for Agricultural CAD Model Based on 3D Wavelet Transformation

A new 3D model retrieval method was proposed to improve feature extraction accuracy, which could further satisfy the needs of agricultural intelligence retrieval. Firstly, the CAD models in agricultural database were normalized. Then CAD models were transformed into eight subband partitions by 3D discrete wavelet. The two-level wavelet transform was made for LxLyLz, which were the lowest frequency parts. After that a single CAD model could be represented by 15 frequency functions. The AHP method was used to calculate the weight of different functions, with which the similarity value was defined for similarity comparison. Finally, the effectiveness of the proposed method was proved with ESB and agricultural model database by using VS2010, Matlab 2016b and Open Cascade. The retrieval results in ESB database showed that the ranking deviation of wavelet algorithm was lower than those of others under the same retrieval condition. The harvester reel retrieval results presented that the similar evaluation considered the geometry morphology as well as functional request. The applied instance demonstrated that once the similar models were selected, designer only needed to make a small re-edit and modification to finish the total design work. The precision of wavelet transform retrieval algorithm varied with different wavelet functions and coif wavelet showed high applicability. APH method could sufficiently distinguish the weight of various decomposed parts. At present, two-level wavelet transform was quite enough to meet model retrieval requirement. The wavelet transform method aiming at design retrieval could satisfy design needs and broaden design ideas, which can promote intelligent design.