freepeople性欧美熟妇, 色戒完整版无删减158分钟hd, 无码精品国产vα在线观看DVD, 丰满少妇伦精品无码专区在线观看,艾栗栗与纹身男宾馆3p50分钟,国产AV片在线观看,黑人与美女高潮,18岁女RAPPERDISSSUBS,国产手机在机看影片

正文內(nèi)容

機械加工畢業(yè)論文中英文資料外文翻譯文獻(參考版)

2024-09-06 17:08本頁面
  

【正文】 眾多的制約因素和月票數(shù)。這些算法并不強勁。傳統(tǒng)方法的優(yōu)化和搜 14 索并不收費,以及點多面廣的問題域。有效性選定的模型用于優(yōu)化工藝參數(shù),是經(jīng)過檢驗的幫助下統(tǒng)計測試,如 F檢驗,卡方檢驗等 [10] 。 一般二階多項式的回應(yīng)是,作為提供以下資料: 如 Y2型是估計響應(yīng)的基礎(chǔ)上的二階方程。 之間的關(guān)系,表面粗糙度及其他獨立變量可以發(fā)生情況如下: 其中 c是一個常數(shù),并為 A , B , C和 D的指數(shù) 為方便測定常數(shù)和指數(shù),這個數(shù)學(xué)模型,必須由線性表演對數(shù) 變換如下: 常數(shù)和指數(shù) c,為 A,B,C和 D都可以由最小二乘法。這個數(shù)學(xué)模型已被作為目標(biāo)函數(shù)和優(yōu)化進行了借助遺傳算法 響應(yīng)面分析法(丹參)是一種有益建模和分析問題的組合數(shù)學(xué)和統(tǒng)計技術(shù)的方法,在這幾個獨立變量的影響力供養(yǎng)變或反應(yīng)。 3 方法論 在這項工作中,數(shù)學(xué)模型已經(jīng)開發(fā)使用的實驗結(jié)果與幫助響應(yīng)面方法論。他們還優(yōu)化了車削加工用表面粗糙度預(yù)測模型為目標(biāo)函數(shù)。結(jié)果已得到驗證,通過比較優(yōu)化的加工條件得到了應(yīng)用遺傳算法。許多方法已經(jīng)被國內(nèi)外文獻報道,以解決加工參數(shù)優(yōu)化問題。上述模式并沒有考慮到對刀具幾何形狀對表面粗糙度的影響。該模型是銑操作進行實驗鋼標(biāo)本。為選擇適當(dāng)?shù)慕M合,切割速度和伺服,增加金屬去除率并不犧牲的表面質(zhì)量,多此進行了模型建造并繪制隨層等高 13 線圖。分別制定了一階方程涵蓋的速度范圍為 3035米 /分,一類二階方程涵蓋速度范圍的 2438米 /分的干切削條件。數(shù)學(xué)模型已經(jīng)研制成功,可用在計算切削速度,進給速度和軸向切深。 因為端銑過程介入多數(shù) f參量,重大參量的聯(lián)合只能通過塑造得到。目前已發(fā)現(xiàn)的壓力和摩擦法對芯片 工具接口減少,增加進給速度,并與下降的氣流角,而切削速度已微不足道,對一些材料依賴參數(shù),工藝參數(shù),歸納為經(jīng)驗公式,作為職能的進給速度和刀具旋轉(zhuǎn)角度為每個工作材料。上下銑 方面切削力與右手螺旋角,雖然主要區(qū)別在于表面粗糙度大,但不存在顯著差異。對主軸速度,切削深度和進給速度對切削力和表面粗糙度的影響進行了研究。所進行的若干實驗是用來決定該中心復(fù)合設(shè)計的。 迪維斯等人 [ 3 ]調(diào)查有關(guān)切削加工性能的五個銑刀具有不同螺旋角。表面光潔 度一直是一個重要的因素,在機械加工性能預(yù)測任何加工操作。 2 回顧 建模過程與優(yōu)化,是兩部很重要的問題,在制造業(yè)。實驗顯示,這項工作將被用來測試切削速度,進給速度,徑向前角和刀尖半徑與加工反應(yīng)。鑒于銑削運行在今天的全球制造業(yè)中起著重要的作用,就必要優(yōu)化加工參數(shù)。獲得最佳切削參數(shù),是在制造業(yè)是非常關(guān)心的,而經(jīng)濟的加工操作中及競爭激烈的市場中發(fā)揮了關(guān)鍵作用。因此,發(fā)展一個很好的模式應(yīng)當(dāng)包含徑向前角和刀尖半徑連同 12 其他相關(guān)因素。它也影響著芯片冰壺和修改芯片方向人流。在過去,雖然通過許多人的大量工作,已開發(fā)并建立了表面光潔度預(yù)測模型,但影響刀具幾何方面受到很少注意。由于這些過程涉及大量的參數(shù),使得難以將關(guān)聯(lián)表面光潔度與其他參數(shù)進行實驗。因此,測量表面光潔度,可預(yù)測加工性能。它可用于各種各樣的制造工業(yè),包括航空航天和汽車這些以質(zhì)量為首要因素的行業(yè) ,以及在生產(chǎn)階段,槽孔,精密模具和模具這些更加注重尺寸精度和表面粗糙度產(chǎn)品的行業(yè)內(nèi)。在加文的程式 中實現(xiàn)了 最低值,表面粗糙度及各自的 值都達到了 最佳條件。這些參數(shù)對表面粗糙度 的 建立,方差分析 極具意義 。第一次和第二次 為建立 數(shù)學(xué)模型,從加工參數(shù) 方面 ,制訂了表面粗糙度預(yù)測響應(yīng)面方法(丹參) ,在此基礎(chǔ)上的實驗結(jié)果。 namely, the cutting speed, feed and depth of cut. The above models have not considered the affect of tool geometry on surface roughness. Since the turn of the century quite a large number of attempts have been made to find optimum values of machining parameters. Uses of many methods have been reported in the literature to solve optimization problems for machining parameters. Jain and Jain [8] have used 4 neural works for modeling and optimizing the machining conditions. The results have been validated by paring the optimized machining conditions obtained using geic algorithms. Suresh et al. [9] have developed a surface roughness prediction model for turning mild steel using a response surface methodology to produce the factor affects of the individual process parameters. They have also optimized the turning process using the surface roughness prediction model as the objective function. Considering the above, an attempt has been made in this work to develop a surface roughness model with tool geometry and cutting conditions on the basis of experimental results and then optimize it for the selection of these parameters within the given constraints in the end milling operation. 3 Methodology In this work, mathematical models have been developed using experimental results with the help of response surface methodology. The purpose of developing mathematical models relating the machining responses and their factors is to facilitate the optimization of the machining process. This mathematical model has been used as an objective function and the optimization was carried out with the help of geic algorithms. Mathematical formulation Response surface methodology (RSM) is a bination of mathematical and statistical techniques useful for modelling and analyzing the problems in which several independent variables influence a dependent variable or response. The mathematical models monly used are represented by: where Y is the machining response, ? is the response function and S, f , α , r are milling variables and ∈ is the error which is normally distributed about the observed response Y with zero mean. The relationship between surface roughness and other independent variables can be represented as follows, where C is a constant and a, b, c and d are exponents. To facilitate the determination of constants and exponents, this mathematical model will have to be linearized by performing a logarithmic transformation as follows: The constants and exponents C, a, b, c and d can be determined by the method of least squares. The first order linear model, developed from the above functional relationship using least squares method, can be represented as follows: where Y1 is the estimated response based on the firstorder equation, Y is the measured surface roughness on a logarithmic scale, x0 = 1 (dummy variable), x1, x2, x3 and x4 are logarithmic transformations of cutting speed, feed rate, radial rake angle and nose radius 5 respectively, ∈ is the experimental error and b values are the estimates of corresponding parameters. The general second order polynomial response is as given below: where Y2 is the estimated response based on the second order equation. The parameters, . b0, b1, b2, b3, b4, b12, b23, b14, etc. are to be estimated by the method of least squares. Validity of the selected model used for optimizing the process parameters has been tested with the help of statistical tests, such as Ftest, chi square test, etc. [10]. Optimization using geic algorithms Most of the researchers have used traditional optimization techniques for solving machining problems. The traditional methods of optimization and search do not fare well over a broad spectrum of problem domains. Traditional techniques are not efficient when the practical search space is too large. These algorithms are not robust. They are inclined to obtain a local optimal solution. Numerous constraints and number of passes make the machining optimization problem more plicated. So, it was decided to employ geic algorithms as an optimization technique. GA e under the class of nontraditional search and optimization techniques. GA are different from traditional optimization techniques in the following ways: work with a coding of the parameter set, not the parameter themselves. s
點擊復(fù)制文檔內(nèi)容
研究報告相關(guān)推薦
文庫吧 www.dybbs8.com
備案圖鄂ICP備17016276號-1