【摘要】：今年,我國大飛機C919完成首航,航母艦隊投入使用,國人為之自豪。五軸機床在此發(fā)揮了重要作用。五軸機床在切削自由曲面時具備優(yōu)異的性能,廣泛應(yīng)用于船舶、航空航天、國防軍工等領(lǐng)域。五軸機床在三軸機床基礎(chǔ)上增加了兩個轉(zhuǎn)動軸,提升加工性能的同時極大增加了機構(gòu)運動的復(fù)雜性,目前國內(nèi)外對于五軸機床的加工性能測試缺乏相應(yīng)的評定標準。成都飛機工業(yè)公司根據(jù)國外檢測試件缺陷和實際生產(chǎn)經(jīng)驗設(shè)計出S試件。通過五軸機床切削S試件,能綜合反映機床的加工性能和動態(tài)特性。但目前建立S試件與五軸機床誤差項間的映射函數(shù)較為困難,基于S試件的機床誤差溯源理論仍然有待完善。深度學(xué)習(xí)是一種機器學(xué)習(xí)算法,能夠直接處理圖像等高維結(jié)構(gòu)數(shù)據(jù),自動建立準確的映射函數(shù),在多個相關(guān)領(lǐng)域的取得成功。本文在現(xiàn)有誤差溯源方法基礎(chǔ)上,參考深度學(xué)習(xí)在相關(guān)任務(wù)的應(yīng)用辦法,提出一種基于S試件和深度學(xué)習(xí)的五軸機床誤差溯源方法,對于完善S試件的機床誤差溯源理論并提高五軸機床的加工性能具有重要意義。具體研究內(nèi)容如下:1、設(shè)計實際刀具位姿切削S試件的仿真算法,建立五軸機床單項誤差與S試件輪廓度誤差的正向映射函數(shù)。包括基于多體理論建立五軸機床空間誤差模型、通過Matlab輔助分析各單項誤差與S件輪廓度誤差的對應(yīng)關(guān)系。2、設(shè)計用于擬合S試件與誤差項間映射函數(shù)的深度學(xué)習(xí)卷積神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu),特殊改進包括將S試件三維點云數(shù)據(jù)通過拉伸和投影轉(zhuǎn)化為三張不同維度的誤差圖,為此設(shè)計三通道的結(jié)構(gòu);誤差數(shù)據(jù)的分布不穩(wěn)定,精度高,采用批量正則化操作調(diào)控數(shù)據(jù)分布;實現(xiàn)剪枝策略對網(wǎng)絡(luò)結(jié)構(gòu)進行優(yōu)化。3、實現(xiàn)基于深度學(xué)習(xí)和S試件的五軸機床誤差溯源實驗方案,包括規(guī)劃數(shù)據(jù)集訓(xùn)練策略、規(guī)格標準;基于ipython notebook建立可在線編程、可視化的客戶端測試框架;搭建維護實驗室深度學(xué)習(xí)服務(wù)器與遠端登陸等服務(wù);最后基于Caffe平臺進行實驗,驗證了整體方案的可行性和正確性。
[Abstract]:This year, China's large aircraft C919 completed its first flight, aircraft carrier fleet put into use, Chinese pride. Five-axis machine tools play an important role here. Five-axis machine tool has excellent performance in cutting free surface. It is widely used in ship, aerospace, national defense industry and so on. The five-axis machine tool adds two rotating shafts on the basis of three-axis machine tool, which improves the machining performance and greatly increases the complexity of mechanism movement. At present, there is no corresponding evaluation standard for the testing of machining performance of five-axis machine tool at home and abroad. Chengdu aircraft Industry Company designs S specimen according to the foreign test sample defect and actual production experience. The machining performance and dynamic characteristics of the machine tool can be synthetically reflected by cutting S specimen with five axis machine tool. However, it is difficult to establish the mapping function between S-specimen and five-axis machine tool error term at present, and the theory of machine tool error traceability based on S-specimen still needs to be perfected. Depth learning is a machine learning algorithm, which can directly process high-dimensional structural data such as images, automatically establish accurate mapping functions, and succeed in many related fields. On the basis of existing error tracing methods and referring to the application of depth learning in related tasks, this paper presents a method of error traceability for five-axis machine tools based on S specimen and depth learning. It is of great significance to improve the error tracing theory of S-specimen and improve the machining performance of five-axis machine tool. The main contents of this paper are as follows: 1. The simulation algorithm of cutting S specimen is designed, and the forward mapping function between the single error of five axis machine tool and the contour error of S specimen is established. The spatial error model of five-axis machine tool is established based on multi-body theory, and the corresponding relationship between the individual error and the profile error of S part is analyzed by Matlab. 2. Deep-learning convolution neural network structure is designed to fit the mapping function between S specimen and error item. Special improvements include transforming S sample 3D point cloud data into three different dimension error maps by stretching and projecting. The three-channel structure is designed for this purpose. The distribution of error data is unstable and accurate, so batch regularization operation is used to control the data distribution. The pruning strategy is realized to optimize the network structure. 3. To realize the experimental scheme of error traceability of five-axis machine tool based on depth learning and S sample, including planning data set training strategy and specification standard; Based on ipython notebook, the test framework of online programming and visual client is established; the service of maintenance laboratory depth learning server and remote landing is built; finally, the feasibility and correctness of the whole scheme are verified based on the Caffe platform.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TG659

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