【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(guò)程分為粉料生產(chǎn)、粉料混合、壓制成形及外觀檢測(cè)等工序�，F(xiàn)階段,粉料生產(chǎn)、混合及壓制均已實(shí)現(xiàn)自動(dòng)化和國(guó)產(chǎn)化,但加熱片的外觀檢測(cè)仍然依靠人工目檢的方式,這種檢測(cè)方式嚴(yán)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(cè)的主要任務(wù)是對(duì)完好加熱片與次品進(jìn)行快速區(qū)分,同時(shí)對(duì)次品加熱片的四類缺陷即掉邊、毛刺、夾雜與裂紋缺陷進(jìn)行識(shí)別。首先,設(shè)計(jì)了加熱片視覺檢測(cè)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(duì)加熱片進(jìn)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(duì)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(shí)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(cè)算法。再次,針對(duì)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(qiáng)調(diào)濾波與本文改進(jìn)的Catte_pm模型圖像增強(qiáng)算法。再次,針對(duì)加熱片圖像的背景引起最小誤差法失效的問(wèn)題,提出了一種改進(jìn)的最小誤差法。另外,分析比較了基于統(tǒng)計(jì)特征,基于主成分分析與支持向量機(jī),基于主成分分析與神經(jīng)網(wǎng)絡(luò)的缺陷分類方法的識(shí)別準(zhǔn)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(shí)別準(zhǔn)確率的關(guān)系。最后,完成了軟件子系統(tǒng)的開發(fā),并對(duì)其進(jìn)行功能及技術(shù)指標(biāo)驗(yàn)證。實(shí)驗(yàn)表明,本文算法實(shí)現(xiàn)了完好加熱片與次品的快速分類;同時(shí)針對(duì)掉邊、毛刺、夾雜及裂紋四類缺陷具有極高的識(shí)別準(zhǔn)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(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41;TM915

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