基于壓強及下沉量測量方法的汽車輪胎靜態(tài)接地壓力研究
本文選題:輪胎壓強 + 下沉量 ; 參考:《西北師范大學》2016年碩士論文
【摘要】:汽車的舒適性和安全性在很大程度上受輪胎性能的影響。作為汽車的主要部件之一,匹配的輪胎壓強和恰當?shù)慕拥貕毫梢詼p少輪胎磨損,延長輪胎使用壽命,減少對道路的損壞。試驗研究表明,輪胎接地壓力的分布直接決定了輪胎使用過程中的性能以及路面的受力狀況,而輪胎結(jié)構(gòu)的設計決定著它的壓力分布狀況。在輪胎結(jié)構(gòu)的設計中要有足夠精確的數(shù)據(jù)作為參考,汽車輪胎靜態(tài)接地壓力研究是輪胎設計和道路壽命研究等工作的重點。目前,國內(nèi)外輪胎接地壓力的研究方法主要有光吸收法、壓敏膜法、壓力板法、壓力傳感器法以及有限元分析方法。其中有限元方法運用最為廣泛,但是這主要是一種數(shù)值仿真方法,主要提供理論指導,不能實際測量出已生產(chǎn)的輪胎性能如何。而其他測量方法,在實際的運用中,又存在著一定的局限性和不同的問題。在本論文的實際研究中,通過閱讀有關(guān)文獻和比較相關(guān)數(shù)據(jù),并且參考了《GB/T22038-2008汽車輪胎靜態(tài)接地壓力分布試驗方法》和《GB/T 2977-2008載重汽車輪胎規(guī)格、尺寸、氣壓與負荷》的計算標準后,提出了基于壓強及下沉量測量方法研究汽車輪胎靜態(tài)接地壓力的方法。本論文利用項目中設計的一個基于壓強及下沉量測量方法的測量系統(tǒng),在測量系統(tǒng)中獲得所需要的數(shù)據(jù)并生成excel文檔。然后利用excel文檔中采集的數(shù)據(jù)訓練BP神經(jīng)網(wǎng)絡模型,對輪胎接地壓力進行預測。本論文的主要工作如下:(1)閱讀了大量國內(nèi)外參考文獻,分析了現(xiàn)有輪胎接地壓力研究方法的不足以及BP神經(jīng)網(wǎng)絡的應用,在此基礎上提出了基于壓強及下沉量測量方法研究汽車輪胎靜態(tài)接地壓力的方法;(2)確定了本文的研究方法和測量依據(jù),通過整體需求分析制定了方案,然后設計了一個測量系統(tǒng)。其中硬件部分是以FPGA為控制核心,軟件部分用C#編寫的可視化用戶操作界面,上位機和下位機通過串口通信,實現(xiàn)數(shù)據(jù)的采集及分析,并以excel文件的形式儲存起來;(3)最后將采集的數(shù)據(jù)用BP神經(jīng)網(wǎng)絡建立模型,對輪胎接地壓力進行預測,并與實測值進行了比較分析。
[Abstract]:Car comfort and safety are largely affected by tire performance. As one of the main parts of automobile, matching tire pressure and proper earthing pressure can reduce tire wear, prolong tire service life and reduce road damage. The experimental results show that the distribution of tire earthing pressure directly determines the performance of tire and the stress of road surface, and the design of tire structure determines its pressure distribution. There should be enough accurate data for reference in the design of tire structure. The research on static earthing pressure of automobile tire is the focus of tire design and road life research. At present, the research methods of tire grounding pressure at home and abroad include light absorption method, pressure sensitive membrane method, pressure plate method, pressure sensor method and finite element analysis method. The finite element method is the most widely used method, but it is mainly a numerical simulation method, which mainly provides theoretical guidance, and can not actually measure the performance of the produced tire. However, there are some limitations and different problems in the practical application of other measurement methods. In the actual study of this paper, by reading the relevant literature and comparing the relevant data, and referring to "GB/T22038-2008 tire static earthing pressure distribution test method" and < GB/T 2977-2008 truck tire specifications, dimensions, After the calculation standard of pressure and load >, a method of studying static earthing pressure of automobile tire based on the measurement of pressure and subsidence is put forward. In this paper, a measurement system based on the method of pressure and subsidence measurement is designed in the project to obtain the required data and generate the excel document in the measurement system. Then BP neural network model is trained by the data collected in excel document to predict tire grounding pressure. The main work of this paper is as follows: (1) We have read a lot of references at home and abroad, analyzed the deficiency of existing research methods of tire earthing pressure and the application of BP neural network. On the basis of this, the paper puts forward the method of studying static grounding pressure of automobile tire based on pressure and subsidence measurement method, and determines the research method and measurement basis of this paper, and formulates the scheme through the whole demand analysis. Then a measuring system is designed. The hardware part takes FPGA as the control core, the software part uses the visual user interface written by C #, and the upper computer and the lower computer communicate through serial port to realize the data acquisition and analysis. Finally, the collected data are modeled by BP neural network, and the tire grounding pressure is predicted and compared with the measured data.
【學位授予單位】:西北師范大學
【學位級別】:碩士
【學位授予年份】:2016
【分類號】:U463.341
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