【正文】 ion. Many such generations are run till the maximum number of generations is met or the average of four selected fitness values in each generation bees steady. This ensures that the optimization of all the variables (cutting speed, feed rate, radial rake angle and nose radius) is carried out simultaneously. The final statistics are displayed at the end of all iterations. In order to optimize the present problem using GA, the following parameters have been selected to obtain the best possible solution with the least putational effort: Table 7 shows some of the minimum values of the surface roughness predicted by the GA program with respect to input machining ranges, and Table 8 shows the optimum machining conditions for the corresponding minimum values of the surface roughness shown in Table 7. The MRR given in Table 8 was calculated by where f is the table feed (mm/min), aa is the axial depth of cut (20 mm) and ar is the radial depth of cut (1 mm). It can be concluded from the optimization results of the GA program that it is possible to select a bination of cutting speed, feed rate, radial rake angle and nose radius for achieving the best possible surface finish giving a reasonably good material removal rate. This GA program provides optimum machining conditions for the corresponding given minimum values of the surface roughness. The application of the geic algorithmic approach to obtain optimal machining conditions will be quite useful at the puter aided process planning (CAPP) stage in the production of high quality goods with tight tolerances by a variety of machining operations, and in the adaptive control of automated machine tools. With the known boundaries of surface roughness and machining conditions, machining could be performed with a relatively high rate of success with the selected machining conditions. 6 Conclusions 10 The investigations of this study indicate that the parameters cutting speed, feed, radial rake angle and nose radius are the primary actors influencing the surface roughness of medium carbon steel uring end milling. The approach presented in this paper provides n impetus to develop analytical models, based on experimental results for obtaining a surface roughness model using the response surface methodology. By incorporating the cutter geometry in the model, the validity of the model has been enhanced. The optimization of this model using geic algorithms has resulted in a fairly useful method of obtaining machining parameters in order to obtain the best possible surface quality. 11 中文翻譯 選擇最佳工具，幾何形狀和切削條件 利用表面粗糙度預測模型端銑 摘要： 刀具幾何形狀對工件表面質量產生的影響是人所共知的，因此，任何成型面端銑設計應包括刀具的幾何形狀。這些參數(shù)對表面粗糙度的建立，方差分析極具意義。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 質量為首要因素的行業(yè)，以及在生產階段，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品的行業(yè)內。由于這些過程涉及大量的參數(shù)，使得難以將關聯(lián)表面光潔度與其他參數(shù)進行實驗。它也影響著芯片冰壺和修改芯片方向人流。獲得最佳切削參數(shù)，是在制造業(yè)是非常關心的，而經濟的加工操作中及競爭激烈的市場中發(fā)揮了關鍵作用。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。表面光潔度一直是一個重要的因素，在機械加工性能預測任何加工操作。所進行的若干實驗是用來決定該中心復合設計的。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。 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。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。該模型是銑操作進行實驗鋼標本。許多方法已經被國內外文獻報道，以解決加工參數(shù)優(yōu)化問題。他們還優(yōu)化了車削加工用表面粗糙度預測模型為目標函數(shù)。這個數(shù)學模型已被作為目標函數(shù)和優(yōu)化進行了借助遺傳算法 響應面分析法（丹參）是一種有益建模和分析問題的組合數(shù)學和統(tǒng)計技術的方法，在這幾個獨立變量的影響力供養(yǎng)變或反應。 一般二階多項式的回應是，作為提供以下資料： 如 Y2型是估計響應的基礎上的二階方程。傳統(tǒng)方法的優(yōu)化和搜索并不收費，以及點多面廣的問題域。眾多的制約因素和月票數(shù)目，使加工優(yōu)化問題更加復雜化。 ，并沒有一個單一的點。 遺傳算法（ GA ）表格一類是自適應啟發(fā)式原則的基礎上得出的，從動態(tài)的人口自然遺傳學。 。加文來根據(jù)類別的非傳統(tǒng)的搜索和優(yōu)化技術。這些算法并不強勁。有效性選定的模型用于優(yōu)化工藝參數(shù)，是經過檢驗的幫助下統(tǒng)計測試，如 F檢驗，卡方檢驗等 [10] 。 之間的關系，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。 13 3 方法論 在這項工作中，數(shù)學模型已經開發(fā)使用的實驗結果與幫助響應面方法論。結果已得到驗證，通過比較優(yōu)化的加工條件得到了應用遺傳算法。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。為選擇適當?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量，多此進行了模型建造并繪制隨層等高線圖。數(shù)學模型已經研制成功，可用在計算切削速度，進給速度和軸向切深。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)，歸納為經驗公式，作為職能的進給速度和刀具旋轉角度為每個工作材料。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。 迪維斯等人 [ 3 ]調查有關切削加工性能的五個銑刀具有不同螺旋角。 2回顧 12 建模過程與優(yōu)化，是兩部很重要的問題，在制造業(yè)。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)。因此，發(fā)展一個很好的模式應當包含徑向前角和刀尖半徑連同其他相關因素。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測模型，但影響刀具幾何 方面受到很少注意。因此，測量表面光潔度，可預測加工性能。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。第一次和第二次為建立數(shù)學模型，從加工參數(shù)方面，制訂了表面粗糙度預測響應面方法（丹參） ，在此基礎上的實驗結果。1 Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling Abstract : Influence of tool geometry on the quality of surface produced is well known and hence any attempt to assess the performance of end milling should include the tool geometry. In the present work, experimental studies have been conducted to see the effect of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on the machining performance during end milling of medium carbon steel. The first and second order mathematical models, in terms of machining parameters, were developed for surface roughness prediction using response surface methodology (RSM) on the basis of experimental results. The model selected for optimization has been validated with the Chi square test. The significance of these parameters on surface roughness has been established with analysis of variance. An attempt has also been made to optimize the surface roughness prediction model using geic algorithms (GA). The GA program gives minimum values of surface roughness and their respective optimal conditions. Introduction End milling is one of the most monly used metal removal operations in industry because of its ability to remove material faster giving reasonably good surface quality. It is used in a variety of manufacturing industries including aerospace and automotive sectors, where quality is an important factor in the production