本文選題：強(qiáng)力珩齒 + 內(nèi)嚙合�。� 參考：《合肥工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著現(xiàn)代制造業(yè)的不斷發(fā)展,人們對(duì)于齒輪加工精度和表面紋理的要求越來越高。目前齒輪精加工工藝主要為磨齒和珩齒,兩者均能獲得較好的加工精度,區(qū)別在于通過珩齒加工后的齒輪表面紋理具有一定的隨意性,相對(duì)于磨齒加工出的齒輪在傳動(dòng)中的噪聲低。內(nèi)嚙合珩齒是現(xiàn)代強(qiáng)力珩齒機(jī)采用較多的一種形式,加工效率較高,可以實(shí)現(xiàn)齒廓齒向修型的同時(shí)改善齒輪表面紋理。Y4830CNC數(shù)控內(nèi)齒強(qiáng)力珩齒機(jī)是國內(nèi)首臺(tái)高檔數(shù)控內(nèi)齒強(qiáng)力珩齒機(jī),為保證其加工性能,本文在該機(jī)床設(shè)計(jì)研發(fā)階段對(duì)其動(dòng)態(tài)及熱特性進(jìn)行了研究與分析。本文的研究?jī)?nèi)容主要包括以下幾點(diǎn):1.闡述了使用有限元法對(duì)數(shù)控內(nèi)齒強(qiáng)力珩齒機(jī)進(jìn)行動(dòng)態(tài)及熱特性分析的基本原理,在三維軟件中構(gòu)建了機(jī)床三維模型并進(jìn)行簡(jiǎn)化,既而得到機(jī)床動(dòng)態(tài)及熱特性分析有限元模型;2.對(duì)整機(jī)及關(guān)鍵零部件進(jìn)行了模態(tài)仿真分析,得到整機(jī)及關(guān)鍵零部件的各階模態(tài)參數(shù)。通過模態(tài)試驗(yàn)驗(yàn)證了有限元模型的正確性,并基于模態(tài)仿真及試驗(yàn)結(jié)果對(duì)機(jī)床關(guān)鍵零部件的結(jié)構(gòu)進(jìn)行了優(yōu)化;3.分析計(jì)算了珩齒機(jī)主要熱源的發(fā)熱量及熱邊界條件,并使用熱-結(jié)構(gòu)耦合法對(duì)整機(jī)及主軸系統(tǒng)進(jìn)行熱特性分析,得到了機(jī)床的溫度場(chǎng)及熱變形場(chǎng)。重點(diǎn)分析了珩齒機(jī)主軸系統(tǒng)熱變形對(duì)齒輪加工精度的影響,并詳細(xì)設(shè)計(jì)了主軸系統(tǒng)熱變形測(cè)量實(shí)驗(yàn);4.根據(jù)熱特性分析結(jié)果,提取了不同位置點(diǎn)處測(cè)溫點(diǎn)溫度及熱變形數(shù)據(jù),采用模糊聚類和最大相關(guān)系數(shù)法對(duì)測(cè)溫點(diǎn)進(jìn)行篩選,最終選擇了4個(gè)測(cè)溫點(diǎn)作為構(gòu)建珩齒機(jī)主軸系統(tǒng)熱誤差預(yù)測(cè)模型的輸入變量;5.分別使用多元線性回歸算法、廣義回歸神經(jīng)網(wǎng)絡(luò)算法和基于粒子群算法的廣義回歸神經(jīng)網(wǎng)絡(luò)算法(PSO-GRNN)建立了珩齒機(jī)主軸系統(tǒng)熱誤差預(yù)測(cè)模型,并比較了不同方法的優(yōu)缺點(diǎn)及預(yù)測(cè)效果,同時(shí)給出了珩齒機(jī)主軸系統(tǒng)熱誤差補(bǔ)償系統(tǒng)設(shè)計(jì)方案。
[Abstract]:With the development of modern manufacturing industry, the requirement of gear machining precision and surface texture is becoming higher and higher. At present, the gear finishing process is mainly grinding and honing, both of which can obtain good machining accuracy. The difference is that the surface texture of gear after honing has a certain degree of arbitrariness. The noise in transmission is low compared to the gear machined by grinding teeth. Internal gear honing is a form of modern powerful honing machine, which has high processing efficiency. It is the first high-grade CNC internal tooth honing machine in China to realize tooth profile modification and improve gear surface texture. Y4830 CNC internal tooth honing machine is the first high-grade internal tooth honing machine in China to guarantee its processing performance. In this paper, the dynamic and thermal characteristics of the machine are studied and analyzed in the design and development stage. The research content of this paper mainly includes the following points: 1. In this paper, the basic principle of dynamic and thermal characteristic analysis of numerical control internal tooth honing machine with finite element method is expounded. The three-dimensional model of machine tool is constructed and simplified in 3D software, and the finite element model of dynamic and thermal characteristic analysis of machine tool is obtained. The modal analysis of the whole machine and the key parts is carried out, and the modal parameters of the whole machine and the key parts are obtained. The validity of the finite element model is verified by modal test, and the structure of key parts of machine tool is optimized based on modal simulation and test results. The heat generation and thermal boundary conditions of the main heat source of honing machine are analyzed and the thermal characteristics of the whole machine and the main shaft system are analyzed by using the thermal-structural coupling method. The temperature field and thermal deformation field of the machine tool are obtained. The influence of thermal deformation on gear machining accuracy of main shaft system of honing machine is analyzed emphatically, and the experiment of measuring thermal deformation of spindle system is designed in detail. According to the results of thermal characteristic analysis, the temperature and thermal deformation data of temperature measuring points at different locations were extracted, and the temperature measuring points were screened by fuzzy clustering and maximum correlation coefficient method. Finally, four temperature measuring points are selected as input variables to build the thermal error prediction model of honing machine spindle system. The thermal error prediction model of honing machine spindle system is established by using multivariate linear regression algorithm, generalized regression neural network algorithm and PSO -GRNN algorithm based on particle swarm optimization, respectively. The advantages and disadvantages of different methods and the prediction results are compared. At the same time, the design scheme of heat error compensation system for the main shaft system of honing machine is presented.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TG659

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