【摘要】：熱電池是一種重要的軍用電源,廣泛應(yīng)用于導(dǎo)彈、艦艇、核武器及民用航空等領(lǐng)域,而加熱片是熱電池加熱系統(tǒng)的重要組成部分,為熱電池提供熱量,其質(zhì)量好壞直接影響著熱電池是否正常工作。加熱片的制造過程分為粉料生產(chǎn)、粉料混合、壓制成形及外觀檢測等工序�，F(xiàn)階段,粉料生產(chǎn)、混合及壓制均已實現(xiàn)自動化和國產(chǎn)化,但加熱片的外觀檢測仍然依靠人工目檢的方式,這種檢測方式嚴重制約了加熱片的生產(chǎn)效率,也難以保證加熱片的質(zhì)量。本文的加熱片由活性鐵粉與高氯酸鉀粉末混合并壓制而成。加熱片表面缺陷檢測的主要任務(wù)是對完好加熱片與次品進行快速區(qū)分,同時對次品加熱片的四類缺陷即掉邊、毛刺、夾雜與裂紋缺陷進行識別。首先,設(shè)計了加熱片視覺檢測系統(tǒng)的硬件部分,選用了康耐視公司的insight7050視覺系統(tǒng)等硬件。完成硬件系統(tǒng)的安裝及調(diào)試后,利用該系統(tǒng)對加熱片進行圖像采集,獲得了有利于算法處理的圖像。其次,圖像的預(yù)處理中,提出了一種自適應(yīng)的抗噪形態(tài)學(xué)邊緣的二值化閾值。在此基礎(chǔ)上,為避免加熱片圖像的背景對處理結(jié)果造成干擾,提出了一種基于抗噪膨脹腐蝕型形態(tài)學(xué)邊緣的前景提取算法;另外,為實現(xiàn)完好加熱片及次品的快速分類,提出了一種基于抗噪腐蝕型形態(tài)學(xué)邊緣的疑似缺陷檢測算法。再次,針對加熱片圖像中的陰影區(qū),高光區(qū),陰影高光夾雜區(qū)及表面紋理,提出了一種結(jié)合高斯高頻強調(diào)濾波與本文改進的Catte_pm模型圖像增強算法。再次,針對加熱片圖像的背景引起最小誤差法失效的問題,提出了一種改進的最小誤差法。另外,分析比較了基于統(tǒng)計特征,基于主成分分析與支持向量機,基于主成分分析與神經(jīng)網(wǎng)絡(luò)的缺陷分類方法的識別準確率,并研究了基于主成分分析的兩種分類方法的降維維度與識別準確率的關(guān)系。最后,完成了軟件子系統(tǒng)的開發(fā),并對其進行功能及技術(shù)指標驗證。實驗表明,本文算法實現(xiàn)了完好加熱片與次品的快速分類;同時針對掉邊、毛刺、夾雜及裂紋四類缺陷具有極高的識別準確率。
[Abstract]:Thermal battery is an important military power source, widely used in missile, naval vessel, nuclear weapon and civil aviation, etc. The heating plate is an important part of the heating system of thermal battery, which provides heat for thermal battery. Its quality directly affects whether the thermal battery works normally. The manufacturing process of heating sheet is divided into powder production, powder mixing, pressing forming and appearance inspection. At the present stage, powder production, mixing and compaction have been realized automatically and domestically, but the appearance inspection of heating sheet still depends on manual inspection, which seriously restricts the production efficiency of heating sheet. It is also difficult to guarantee the quality of the heating sheet. The heating sheet is prepared by mixing and compacting active iron powder and potassium perchlorate powder. The main task of the surface defect detection of the heating sheet is to quickly distinguish the perfect heating sheet from the defective product, and at the same time to identify the four kinds of defects of the defective heating sheet, that is, falling edge, burr, inclusion and crack defect. Firstly, the hardware of the heating slice vision detection system is designed, and the hardware of insight7050 vision system is selected. After the hardware system is installed and debugged, the image of the heating slice is collected by the system, and the image is obtained which is propitious to the algorithm. Secondly, in image preprocessing, an adaptive binarization threshold of the edge of anti-noise morphology is proposed. On this basis, to avoid the interference of the background of the heated slice image to the processing results, a foreground extraction algorithm based on the noise-resistant swelling corrosion morphological edge is proposed. In addition, in order to realize the fast classification of perfect heating sheets and defective products, a new algorithm of suspected defect detection based on noise-resistant morphological edge is proposed. Thirdly, an improved Catte_pm model image enhancement algorithm based on Gao Si high-frequency emphasis filter and the improved Catte_pm model image enhancement algorithm is proposed for the shadow region, the highlight region, the shadow highlight area and the surface texture of the heated slice image. Thirdly, an improved minimum error method is proposed for the failure of the minimum error method caused by the background of the heated slice image. In addition, the recognition accuracy of defect classification methods based on statistical feature, principal component analysis and support vector machine, principal component analysis and neural network is analyzed and compared. The relationship between dimensionality reduction and recognition accuracy of two classification methods based on principal component analysis is studied. Finally, the software subsystem is developed, and its function and technical index are verified. The experiments show that the algorithm realizes the fast classification of perfect heating plates and defective products, and it has a high recognition accuracy for four kinds of defects, such as missing edges, burrs, inclusions and cracks.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TM915

【參考文獻】

相關(guān)期刊論文 前10條

1 鄭紅波;陳宇;趙海;秦緒佳;張美玉;;紋理去除的皮革圖像顏色分類方法[J];計算機科學(xué);2016年S2期

2 張建偉;雷霖;余化鵬;黃毅;;基于形態(tài)學(xué)的硬幣鏡面區(qū)域缺陷檢測算法研究[J];成都大學(xué)學(xué)報(自然科學(xué)版);2016年03期

3 王義文;屈冠彤;付鵬強;李強;梅恒;;基于機器視覺的光柵表面缺陷檢測系統(tǒng)[J];光電工程;2016年09期

4 廖梓龍;鐘守炎;;基于機器視覺大數(shù)據(jù)分析的表面貼裝缺陷檢測[J];機電工程技術(shù);2016年08期

5 黃志鴻;毛建旭;王耀南;周顯恩;歷艷琨;劉學(xué)兵;;基于機器視覺的啤酒瓶口缺陷檢測分類方法研究[J];電子測量與儀器學(xué)報;2016年06期

6 張宏釗;周松斌;劉偉鑫;;基于機器視覺的圓形馬口鐵罐罐口缺陷檢測[J];自動化與信息工程;2016年01期

7 吳一全;孟天亮;吳詩Zs;;圖像閾值分割方法研究進展20年(1994—2014)[J];數(shù)據(jù)采集與處理;2015年01期

8 林天圓;王杰;李金屏;;一種光照不均勻圖像的灰度校正方法[J];濟南大學(xué)學(xué)報(自然科學(xué)版);2015年06期

9 王傳東;;熱電池發(fā)展綜述[J];電源技術(shù);2013年11期

10 申小平;許桂生;;粉末冶金壓坯缺陷分析[J];粉末冶金技術(shù);2012年04期

相關(guān)博士學(xué)位論文 前3條

1 韓芳芳;表面缺陷視覺在線檢測關(guān)鍵技術(shù)研究[D];天津大學(xué);2012年

2 楊水山;冷軋帶鋼表面缺陷機器視覺自動檢測技術(shù)研究[D];哈爾濱工業(yè)大學(xué);2009年

3 鄭連清;火工品壓藥關(guān)鍵工藝與設(shè)備研究[D];重慶大學(xué);2005年

相關(guān)碩士學(xué)位論文 前4條

1 石聰;磁材表面缺陷視覺檢測方法[D];哈爾濱工業(yè)大學(xué);2013年

2 蔣東升;基于數(shù)學(xué)形態(tài)學(xué)的邊緣檢測算法研究[D];電子科技大學(xué);2012年

3 付國文;基于Retinex的圖像增強算法研究及實現(xiàn)[D];上海交通大學(xué);2011年

4 吳仕勇;基于數(shù)值計算方法的BP神經(jīng)網(wǎng)絡(luò)及遺傳算法的優(yōu)化研究[D];云南師范大學(xué);2006年

，

本文編號：2364467