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基于形狀分布式模型檢索的農(nóng)機(jī)裝備快速設(shè)計(jì)方法
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國家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2017YFD0700100)、山東省農(nóng)業(yè)重大應(yīng)用技術(shù)創(chuàng)新項(xiàng)目(SD2019NJ011)和山東省重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2019GNC106120)


Rapid Design Method for Agricultural Machinery Based on Shape-distribution Model Retrieval
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    為促進(jìn)農(nóng)機(jī)裝備設(shè)計(jì)重用,提出一種基于形狀分布式模型檢索的農(nóng)機(jī)裝備快速設(shè)計(jì)方法。首先,對(duì)三維模型進(jìn)行歸一化處理,并依次計(jì)算模型表面各三角網(wǎng)格面積,根據(jù)網(wǎng)格面積將每個(gè)模型的三角網(wǎng)格分為Max、Mid、Min 3類,采用Sobol準(zhǔn)隨機(jī)序列對(duì)分類后網(wǎng)格交叉組合取點(diǎn),以采樣點(diǎn)數(shù)3為基本采樣單位對(duì)三維模型進(jìn)行組合式特征點(diǎn)采樣;然后,對(duì)提取的距離D2、面積D3、曲率C1、角度A3形狀特征進(jìn)行融合,分別計(jì)算4種特征值變異系數(shù),以變異系數(shù)所占比例作為各形狀特征權(quán)重,將加權(quán)后的不同特征變量值拼接成具有多特征的形狀分布直方圖,采用χ2距離度量直方圖間的相似性;最后,以VS2010與Matlab 2016b為開發(fā)環(huán)境,以O(shè)pen Cascade為幾何造型平臺(tái),使用自行構(gòu)建的農(nóng)機(jī)裝備關(guān)鍵零部件模型庫進(jìn)行實(shí)驗(yàn)。結(jié)果表明,在農(nóng)機(jī)三維模型庫中,特征查準(zhǔn)率由大到小依次為距離D2、曲率C1、角度A3、面積D3特征,本文提出的自適應(yīng)加權(quán)融合特征(AWSD)算法在查全率0~0.5區(qū)間內(nèi)顯著優(yōu)于D2檢索算法,在0.5~1.0區(qū)間內(nèi)檢索效果與D2特征近似;AWSD算法檢索效率符合基本要求,綜合檢索精度較D2形狀分布算法提高了8.5%;拖拉機(jī)輪轂與收獲機(jī)摘穗板檢索實(shí)例表明,AWSD算法在檢索主觀滿意度方面優(yōu)于距離D2與曲率C1算法。

    Abstract:

    A new rapid design method for agricultural machinery was proposed to promote agricultural design reuse technology. The method was implemented based on shape distribution model retrieval. Firstly, the CAD models in agricultural database were normalized. According to mesh area, all the triangular meshes that belong to each model were divided into Max, Mid and Min groups. The Sobol quasi-random sequence was used to sample feature point from each group. Then three points were defined as the basic sample unit, which was used to fuse the D2, D3, C1 and A3 features together. The variation coefficient of each feature was calculated and the proportion was regarded as sub-feature weight of fusion feature. Thereafter, the multi-feature histogram of shape distribution was formed by connecting different weighted features. The χ2 distance was selected to measure the similarity between feature histograms. Finally, the effectiveness of the proposed method was proved with selfbuilt agricultural model database using VS2010, Matlab 2016b and Open Cascade. The results showed that the retrieval precision of distance D2 feature was higher than that of curvature C1, angle A3 and area D3 features in agricultural model database. The proposed method of adaptive weighted shape distribution (AWSD) performed better than D2 when retrieval recall was ranged from 0 to 0.5, and as effective as D2 in the range of 0.5~1.0. Compared with D2 retrieval method, the comprehensive retrieval precision of AWSD method was increased by 8.5%. The retrieval cases of tractor wheel hub and harvester picking board demonstrated AWSD outperformance in the aspect of objective retrieval satisfaction. The new model retrieval method that could fuse multi-shape distribution features injected fresh energy to agricultural machinery rapid design.

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劉洪豪,張開興,盧山,劉賢喜.基于形狀分布式模型檢索的農(nóng)機(jī)裝備快速設(shè)計(jì)方法[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2020,51(5):395-403. LIU Honghao, ZHANG Kaixing, LU Shan, LIU Xianxi. Rapid Design Method for Agricultural Machinery Based on Shape-distribution Model Retrieval[J]. Transactions of the Chinese Society for Agricultural Machinery,2020,51(5):395-403.

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  • 收稿日期:2019-09-24
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  • 在線發(fā)布日期: 2020-05-10
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