【摘要】：作為衡量國家制造業(yè)和綜合國力水平的標志,高檔數(shù)控機床在現(xiàn)代工業(yè)中的地位日益加重。在機床多種誤差源中,熱誤差在機床總誤差重的比重可達到40%~70%,因此,控制并削弱熱誤差對提高精密機床的精度非常重要。國內(nèi)外研究表明,熱誤差補償技術(shù)重點圍繞著溫度敏感點選擇、熱誤差建模和補償?shù)热齻�方面給予研究。雖然目前已有比較完善的理論體系和補償實施技術(shù),但仍然存在一些影響其廣泛應(yīng)用、且亟需解決的關(guān)鍵性問題,如溫度測點優(yōu)化過程中溫度變量之間的共線性影響以及模型預(yù)測穩(wěn)健性不足等。針對上述熱誤差補償技術(shù)的缺陷,本文從溫度敏感點選擇、熱誤差建模和補償?shù)确矫嫣岢隽讼嚓P(guān)理論和方法,并進行了大量實驗數(shù)據(jù)分析,最后以實際機床為例進行了熱誤差補償實驗驗證。論文主要的研究內(nèi)容如下:1)提出了一種數(shù)控機床穩(wěn)健性熱誤差建模方法,將以灰色關(guān)聯(lián)度為核心的溫度敏感點優(yōu)化方法和以主成分回歸分析為核心的建模算法配合使用,共同對溫度敏感點優(yōu)化所需遵循的大權(quán)重、低耦合和少布點策略進行處理,既簡化了測點優(yōu)化的復(fù)雜度,又有效地提升了模型的預(yù)測精度和穩(wěn)健性。2)采用傳統(tǒng)的模糊聚類結(jié)合灰色關(guān)聯(lián)度的溫度敏感點優(yōu)化算法,對全年分季度的機床熱誤差數(shù)據(jù)給予了研究,闡述了該方法選擇出的溫度敏感點存在變動性特征及其對模型精度影響。3)研究了多元線性回歸算法、時間序列分布滯后和主成分回歸算法的模型精度和穩(wěn)健性特征,并總結(jié)得到三種模型在熱誤差建模補償中的適用范圍。另外,提出了基于主成分回歸分析的分布滯后模型精度提升方法。4)介紹了熱誤差補償技術(shù)的軟件實施方法,包括數(shù)控平臺、熱誤差測量集成系統(tǒng)、最佳溫度敏感點的優(yōu)化選擇、熱誤差數(shù)學(xué)模型建立、熱誤差補償與精度評定。評定結(jié)果表明補償效果顯著。
[Abstract]:As a symbol of national manufacturing industry and comprehensive national strength, the status of high-grade CNC machine tools in modern industry is getting more and more serious. Among the various error sources of machine tool, the proportion of thermal error in the total error weight of machine tool can reach 400.Therefore, it is very important to control and weaken the thermal error to improve the precision of precision machine tool. Studies at home and abroad show that the thermal error compensation technology focuses on three aspects: temperature sensitive point selection, thermal error modeling and compensation. Although there is a relatively sound theoretical system and compensation implementation techniques, there are still some key problems that affect their wide application and are in urgent need of solution, For example, the collinearity effect between temperature variables and the lack of robustness of the model in the optimization of temperature measurement points. In view of the defects of the above thermal error compensation technology, this paper puts forward some related theories and methods from the aspects of temperature sensitive point selection, thermal error modeling and compensation, and carries out a lot of experimental data analysis. Finally, the experimental verification of thermal error compensation is carried out with the actual machine tool as an example. The main contents of this paper are as follows: 1) A robust thermal error modeling method for NC machine tools is proposed. The temperature sensitive point optimization method with grey correlation degree as the core and the modeling algorithm with principal component regression analysis as the core are used together. In order to simplify the complexity of the optimization of temperature sensitive points, the strategy of large weight, low coupling and less point placement is used in the optimization of temperature sensitive points. Moreover, the prediction accuracy and robustness of the model are improved effectively. 2) the thermal error data of machine tools are studied by using the traditional temperature sensitive point optimization algorithm based on fuzzy clustering and grey correlation degree. The variability of temperature sensitive points selected by this method and its influence on model accuracy are described. 3) the model accuracy and robustness of multivariate linear regression algorithm, time series distribution lag and principal component regression algorithm are studied. The application range of three models in thermal error modeling compensation is summarized. In addition, a method of improving the accuracy of distributed lag model based on principal component regression analysis is proposed. 4) the software implementation method of thermal error compensation technology is introduced, including numerical control platform, integrated thermal error measurement system, Optimal selection of optimal temperature sensitive points, mathematical model of thermal error, thermal error compensation and accuracy evaluation. The evaluation results show that the compensation effect is remarkable.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TG659

【參考文獻】

相關(guān)期刊論文 前10條

1 鄭艷霞;;國內(nèi)數(shù)控機床熱誤差建模研究現(xiàn)狀[J];機械工程與自動化;2016年06期

2 苗恩銘;劉義;楊思炫;陳維康;;無偏估計拆分算法在數(shù)控加工中心主軸熱誤差建模中的應(yīng)用[J];中國機械工程;2016年16期

3 楊軍;梅雪松;趙亮;馬馳;馮斌;施虎;;基于模糊聚類測點優(yōu)化與向量機的坐標鏜床熱誤差建模[J];上海交通大學(xué)學(xué)報;2014年08期

4 苗恩銘;龔亞運;徐祗尚;周小帥;;數(shù)控機床熱誤差補償模型穩(wěn)健性比較分析[J];機械工程學(xué)報;2015年07期

5 田國富;胡軍;郭玉學(xué);;多元線性回歸理論在數(shù)控機床熱誤差補償中的應(yīng)用[J];機械工程與自動化;2013年02期

6 苗恩銘;龔亞運;成天駒;陳海東;;支持向量回歸機在數(shù)控加工中心熱誤差建模中的應(yīng)用[J];光學(xué)精密工程;2013年04期

7 姚煥新;牛鵬程;龔亞運;邵善敏;苗恩銘;;數(shù)控機床熱誤差補償中分布滯后模型的建立[J];農(nóng)業(yè)機械學(xué)報;2013年03期

8 楊建國;姚曉棟;;數(shù)控機床誤差補償技術(shù)現(xiàn)狀與展望[J];世界制造技術(shù)與裝備市場;2012年03期

9 王惠文;王R，

本文編號：2372451