【摘要】：齒輪產(chǎn)品在很多領(lǐng)域都扮演重要角色,所以必須對產(chǎn)品進(jìn)行嚴(yán)格的檢測。人工檢測的方法存在誤差大、速度慢、檢測數(shù)據(jù)不能實(shí)時(shí)存儲等缺點(diǎn),不適合生產(chǎn)過程中的實(shí)時(shí)在線檢測。在機(jī)器視覺軟件HALCON的HDevelop開發(fā)環(huán)境下,本文設(shè)計(jì)了一種齒輪自動檢測與測量的系統(tǒng),包括圖像采集、圖像處理、圖像識別等部分,主要內(nèi)容為:1.將待檢測的齒輪置于雙軌道環(huán)形流水線的傳送帶上,當(dāng)齒輪在傳送帶上運(yùn)動到暗箱中的光電傳感器位置時(shí),獲取齒輪圖像然后將其傳入計(jì)算機(jī)內(nèi)進(jìn)行處理。2.利用機(jī)器視覺軟件HALCON對獲得的待檢測圖像執(zhí)行預(yù)處理操作。首先把獲取到的彩色齒輪圖像經(jīng)過灰度化轉(zhuǎn)換轉(zhuǎn)變成灰度圖像。然后,對灰度圖像進(jìn)行各項(xiàng)異性擴(kuò)散濾波處理,該處理過程具有除去圖像噪聲的同時(shí)保留并銳化圖像邊緣的優(yōu)點(diǎn)。3.齒輪檢測過程中,機(jī)器視覺系統(tǒng)中的暗箱可以減少外界環(huán)境光照對系統(tǒng)的影響,所以對平滑處理后的圖像采用速度最快的閾值分割算法。然后采用形態(tài)學(xué)處理和減法處理相結(jié)合的方法得到齒輪齒的個數(shù)和單個齒的面積,進(jìn)而剔除不合格產(chǎn)品。4.在選取感興趣區(qū)域后,結(jié)合Canny算子和雙線性插值法檢測齒輪的亞像素邊緣。不同類型的齒輪識別過程中,提出了使用模板匹配與圖像金字塔搜索法相結(jié)合的方法,以亞像素級別的齒輪中心孔輪廓作為形狀匹配的模板,并且該模板支持各向異性縮放。模板匹配之后,為了能使匹配結(jié)果顯示出來,對模板圖像進(jìn)行了一個仿射變換處理。實(shí)驗(yàn)證明,該處理過程的方法能快速準(zhǔn)確的對不同類型的齒輪進(jìn)行分類識別。5.獲得亞像素邊緣后,對亞像素邊緣的輪廓應(yīng)用格林定理得到齒輪的面積和中心。接著用基于Tukey的最小二乘法擬合圓形曲線,進(jìn)而可以獲得各個圓的半徑長度。然后用一維圓弧測量法獲得齒輪的齒厚、齒槽寬度以及齒距,最后經(jīng)過系統(tǒng)標(biāo)定完成測量工作。
[Abstract]:Gear products play an important role in many fields, so it is necessary to carry out rigorous testing of the products. The manual testing method has some shortcomings, such as large error, slow speed, and the detection data can not be stored in real time. It is not suitable for real-time on-line testing in the production process. The system includes image acquisition, image processing and image recognition. The main contents are as follows: 1. Put the gear to be detected on the conveyor belt of two-track annular pipeline. When the gear moves on the conveyor belt to the position of photoelectric sensor in the dark box, the image of the gear is acquired and transmitted to the computer. 2. The machine vision software HALCON is used to pre-process the acquired image. First, the acquired color gear image is transformed into gray image by gray-scale transformation. Then, the gray image is processed by anisotropic diffusion filtering, which can remove the image noise while retaining and sharpening the image. 3. In the process of gear detection, the dark box in the machine vision system can reduce the influence of the external environment illumination on the system, so the smoothed image is segmented by the fastest threshold algorithm. The sub-pixel edge of the gear is detected by Canny operator and bilinear interpolation after selecting the region of interest. In the process of identifying different types of gears, a method combining template matching and image pyramid search is proposed, in which the profile of the center hole of the gear at sub-pixel level is taken as the shape. After template matching, an affine transformation is performed to make the matching result show. Experiments show that this method can classify different types of gears quickly and accurately. 5. After obtaining the sub-pixel edge, the sub-pixel is classified. The area and center of the gear are obtained by Green's theorem. Then, the circle curve is fitted by the least square method based on Tukey, and the radius length of each circle is obtained.
【學(xué)位授予單位】：聊城大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TG86

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