本文關(guān)鍵詞： 硬軟骨料 金剛石鋸片 加工信號(hào) 監(jiān)測(cè)　出處：《廣西大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：混凝土是一種復(fù)合材料,不同骨料力學(xué)性質(zhì)差異較大,使得它的鋸切狀態(tài)相對(duì)隨機(jī)。多數(shù)情況下,混凝土的鋸切過(guò)程難以進(jìn)行有效監(jiān)測(cè),鋸片在使用途中極易出現(xiàn)偏擺,這樣嚴(yán)重影響了加工效率。因此本文采用了金剛石鋸片對(duì)混凝土內(nèi)部各組分(硬骨料、軟骨料)和混凝土進(jìn)行了鋸切實(shí)驗(yàn)研究,為提高混凝土的鋸切效率和鋸片的利用率提供理論依據(jù)和應(yīng)用指導(dǎo)。主要內(nèi)容及結(jié)論如下:(1)對(duì)硬骨料和軟骨料的鋸切機(jī)理進(jìn)行了研究,分析了硬軟骨料在不同鋸切用量下鋸切力和振動(dòng)幅值的變化規(guī)律以及材料去除機(jī)制。實(shí)驗(yàn)結(jié)果表明軟骨料的去除機(jī)制存在著輕微的塑性變形和脆性崩碎兩種情況,硬骨料則完全是脆性崩碎;x和y方向的鋸切力和振動(dòng)幅值變化趨勢(shì)較為一致,但鋸切用量對(duì)y方向的鋸切力影響不大;在相同的鋸切用量下軟骨料的鋸切力和振動(dòng)幅值一般要小于硬骨料;切深對(duì)硬骨料的鋸切力和振動(dòng)幅值的影響程度最大,進(jìn)給速度對(duì)軟骨料的鋸切力和振動(dòng)幅值的影響程度最大。(2)利用多尺度小波分析方法研究了硬軟骨料的加工信號(hào)各頻率段的特征變化情況。發(fā)現(xiàn)硬軟骨料的加工信號(hào)較為穩(wěn)定且隨機(jī)性小,其特定頻率段與鋸切過(guò)程存在著良好的對(duì)應(yīng)關(guān)系,合理地利用加工信號(hào)可實(shí)現(xiàn)對(duì)硬軟骨料鋸切過(guò)程的穩(wěn)定性以及鋸片狀態(tài)的監(jiān)測(cè)。(3)對(duì)硬軟骨料和混凝土加工信號(hào)的各頻率段進(jìn)行了比較。實(shí)驗(yàn)結(jié)果表明混凝土加工信號(hào)中156.25-312.5Hz頻率段主要源于鋸片與混凝土中硬骨料之間的相互作用,1250-2500Hz頻率段主要源于鋸片與混凝土中軟骨料之間的相互作用。(4)本文的最后以混凝土加工信號(hào)中的156.25-312.5Hz和1250-2500 Hz頻率段作為鋸片狀態(tài)特征的主要來(lái)源,使用蝙蝠算法改進(jìn)的bp網(wǎng)絡(luò)預(yù)測(cè)鋸片狀態(tài)。同時(shí)考慮到力和振動(dòng)傳感器采集數(shù)據(jù)方面的差異,對(duì)兩種不同信號(hào)樣本的預(yù)測(cè)結(jié)果進(jìn)行了融合,并與實(shí)際鋸片狀態(tài)值進(jìn)行了比較,發(fā)現(xiàn)可實(shí)現(xiàn)對(duì)鋸片狀態(tài)的準(zhǔn)確判斷。
[Abstract]:Concrete is a kind of composite material, and the mechanical properties of different aggregates vary greatly, which makes the sawing state of concrete relatively random. In most cases, the sawing process of concrete is difficult to monitor effectively. It is easy to appear deflection in the use of saw blade, which seriously affects the processing efficiency. Therefore, diamond saw blade is used to study the internal components of concrete (hard aggregate, cartilage) and concrete sawing experiments. In order to improve the sawing efficiency of concrete and the utilization rate of saw blade, the theoretical basis and application guidance are provided. The main contents and conclusions are as follows: 1) the sawing mechanism of hard aggregate and cartilage is studied. The variation of sawing force and vibration amplitude of hard soft aggregate under different sawing contents and the material removal mechanism were analyzed. The experimental results show that the removal mechanism of cartilage has two kinds of cases: slight plastic deformation and brittle breakage. . Hard aggregate is completely brittle crumbling; The variation trend of sawing force and vibration amplitude in the direction of x and y is consistent, but the sawing amount has little effect on the sawing force in the direction of y. The sawing force and vibration amplitude of cartilage under the same sawing dosage are generally smaller than that of hard aggregate. Cutting depth has the greatest influence on sawing force and vibration amplitude of hard aggregate. The influence of feed speed on the sawing force and vibration amplitude of cartilage is the biggest. Multi-scale wavelet analysis was used to study the characteristics of processing signals of hard soft aggregates at different frequencies. It was found that the processing signals of hard soft aggregates were relatively stable and less random. There is a good correspondence between the specific frequency range and the sawing process. The stability of sawing process of hard soft aggregate and the monitoring of saw blade state can be realized by reasonable use of processing signal. The frequency bands of hard soft aggregate and concrete processing signal are compared. The experimental results show that the 156.25-312.5 Hz frequency range of concrete processing signal mainly comes from sawing blade and concrete hard aggregate. The interaction. The frequency range of 1250-2500 Hz is mainly derived from the interaction between saw blade and cartilage in concrete. At the end of this paper, the frequency bands of 156.25-312.5Hz and 1250-2500 Hz in the concrete processing signal are taken as the main source of the state characteristics of the saw blade. The improved BP neural network based on bat algorithm is used to predict the state of saw blade. Considering the difference of data collected by force and vibration sensors, the prediction results of two different signal samples are fused. Compared with the actual saw blade state value, it is found that the accurate judgment of saw blade state can be realized.
【學(xué)位授予單位】：廣西大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TU528

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