本文關(guān)鍵詞：航空鋁合金薄壁件銑削加工工藝優(yōu)化及有限元仿真　出處：《天津工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 有限元 銑削力 加工變形 經(jīng)驗(yàn)公式 遺傳算法 銑削參數(shù)優(yōu)化

【摘要】：鋁合金薄壁件在銑削加工過程中容易發(fā)生變形,難以保證銑削加工后工件的加工用精度。對(duì)于這一問題,研究銑削參數(shù)優(yōu)化,對(duì)于提高加工精度、保證生產(chǎn)效率具有重要作用�；诂F(xiàn)代切削理論通過有限元數(shù)值模擬仿真技術(shù)、正交試驗(yàn)、Matlab數(shù)學(xué)回歸建模能力與遺傳算法的優(yōu)化能力來尋找銑削四要素的最優(yōu)組合,是現(xiàn)代化機(jī)械加工的一個(gè)重要手段。本文以航空鋁合金薄壁件7075-T651為研究載體,研究了它的銑削加工過程。設(shè)計(jì)了相應(yīng)的銑削加工實(shí)驗(yàn),并通過BP神經(jīng)網(wǎng)絡(luò)算法求解了銑削加工變形預(yù)測(cè)的模型,基于遺傳算法實(shí)現(xiàn)了銑削四要素的最優(yōu)化解,主要的研究?jī)?nèi)容如下:1、基于金屬切削加工過程的理論模型,建立熱力耦合的二維正交切削有限元模型,并對(duì)模擬切削加工過程中的關(guān)鍵性技術(shù)進(jìn)行了詳細(xì)的分析。通過對(duì)切削過程的數(shù)值模擬,得到切削過程中切削力、應(yīng)變和溫度的變化情況,得到與實(shí)際切削加工比較相符的結(jié)果。2、利用二維有限元數(shù)值模擬的基礎(chǔ),建立鋁合金薄壁件銑削加工的三維有限元模型仿真。對(duì)鋁合金薄壁件的銑削加工過程進(jìn)行了數(shù)值模擬,得到不同銑削參數(shù)下薄壁件銑削銑削力曲線圖和銑削變形曲線圖。3、進(jìn)行了鋁合金薄壁件的銑削加工實(shí)驗(yàn)驗(yàn)證,通過Kistler測(cè)力儀和Wenzel三坐標(biāo)測(cè)量機(jī)分別對(duì)對(duì)銑削加工過程中的銑削力和銑削加工后的薄壁件的變形量進(jìn)行測(cè)量,驗(yàn)證了有限元數(shù)值模擬仿真對(duì)銑削力和銑削變形的精確性,并通過Matlab的線性回歸功能得到了銑削加工的銑削力經(jīng)驗(yàn)公式。為保證加工質(zhì)量可以選擇較小的進(jìn)給量,銑削深度,銑削寬度,選擇較大的銑削速度。4、為獲得最優(yōu)化的銑削加工參數(shù),利用BP神經(jīng)網(wǎng)絡(luò)算法對(duì)銑削加工參數(shù)進(jìn)行訓(xùn)練,確定多切削參數(shù)下的BP網(wǎng)絡(luò)加工的變形預(yù)測(cè)模型。并通過遺傳算法對(duì)銑削參數(shù)進(jìn)行優(yōu)化,獲得了更好的加工精度和更高加工效率的銑削參數(shù)。其優(yōu)化參數(shù)為:銑削轉(zhuǎn)速3425r/min;銑削速度412mm/min;銑削深度0.65mm;銑削寬度2.4mm。
[Abstract]:The aluminum alloy thin-walled parts are easily deformed during the milling process, and it is difficult to ensure the machining precision of the workpiece after milling. For this problem, the study of optimization of milling parameters plays an important role in improving the machining precision and ensuring the efficiency of production. Based on modern cutting theory, finding the best combination of four elements of milling is an important means of modern machining by finite-element numerical simulation, orthogonal experiment, Matlab mathematical regression modeling ability and optimization ability of genetic algorithm. This paper studies the milling process of the aluminum alloy thin-walled part 7075-T651 as the research carrier. The design of the milling of the corresponding experiments, and through the BP neural network algorithm for the prediction model for milling deformation, genetic algorithm to achieve the optimization of milling solution based on the four elements, the main research contents are as follows: 1, based on the theoretical model of metal cutting process, cutting finite element model of two-dimensional orthogonal thermal mechanical coupling is established. And the key technique to simulate the cutting process are analyzed in detail. Through the numerical simulation of cutting process, we can get the change of cutting force, strain and temperature during cutting process, and get the result that is consistent with the actual cutting process. 2. On the basis of two-dimensional finite element numerical simulation, the 3D finite element model simulation of aluminum alloy thin-walled parts milling is established. The milling process of aluminum alloy thin-walled parts is numerically simulated, and milling force curves and milling deformation curves of thin-walled parts under different milling parameters are obtained. 3, the experimental verification Aluminum Alloy milling of thin-walled workpiece, by Kistler dynamometer and Wenzel three coordinate measuring machine of thin-walled milling force of the milling process and milling after the deformation measurement, verified the simulation accuracy of the deformation simulation of milling force and milling finite element numerical, and through the Matlab linear regression function obtained the empirical equation of milling force milling. In order to guarantee the quality of processing, small feed, milling depth, milling width are selected, and larger milling speed is selected. 4, in order to get the optimal milling parameters, the BP neural network algorithm is applied to train milling parameters, and the deformation prediction model of BP network processing under multi cutting parameters is determined. The milling parameters are optimized by genetic algorithm, and the milling parameters with better machining precision and higher machining efficiency are obtained. The optimization parameters are milling speed 3425r/min, milling speed 412mm/min, milling depth 0.65mm, milling width 2.4mm.
【學(xué)位授予單位】：天津工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TG54

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李亞平;李長(zhǎng)虹;趙先鋒;李榮隆;;211Z鋁合金銑削表面粗糙度的實(shí)驗(yàn)研究[J];機(jī)械設(shè)計(jì)與制造;2016年06期

2 葛茂杰;單國(guó)峰;于健;秦學(xué)堂;;7050-T7451鋁合金三維微銑削建模仿真[J];工具技術(shù);2016年02期

3 張偉;魏剛;肖新科;;2A12鋁合金本構(gòu)關(guān)系和失效模型[J];兵工學(xué)報(bào);2013年03期

4 張新明;劉勝膽;;航空鋁合金及其材料加工[J];中國(guó)材料進(jìn)展;2013年01期

5 曲海軍;楊金花;王貴成;劉旭;朱云明;;直角切削過程中應(yīng)變的有限元分析[J];機(jī)械科學(xué)與技術(shù);2013年01期

6 劉戰(zhàn)強(qiáng);吳繼華;史振宇;趙丕芬;;金屬切削變形本構(gòu)方程的研究[J];工具技術(shù);2008年03期

7 龐新福;杜茂華;;基于ABAQUS的二維直角切削加工有限元分析[J];工具技術(shù);2008年02期

8 陳永勝;宋立新;;多元線性回歸建模以及SPSS軟件求解[J];通化師范學(xué)院學(xué)報(bào);2007年12期

9 溫效朔;王克琦;;切削加工有限元仿真與應(yīng)力分析[J];工具技術(shù);2006年07期

10 董輝躍;柯映林;成群林;;鋁合金三維銑削加工的有限元模擬與分析[J];浙江大學(xué)學(xué)報(bào)(工學(xué)版);2006年05期

相關(guān)碩士學(xué)位論文 前1條

1 萬敏;薄壁件周銑加工過程中表面靜態(tài)誤差預(yù)測(cè)關(guān)鍵技術(shù)研究[D];西北工業(yè)大學(xué);2005年

，

本文編號(hào)：1338022