【正文】 his 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 bywhere 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 genetic 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 ConclusionsThe 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 genetic algorithms has resulted in a fairly useful method of obtaining machining parameters in order to obtain the best possible surface quality.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(yù)測(cè)模型端銑摘要： 刀具幾何形狀對(duì)工件表面質(zhì)量產(chǎn)生的影響是人所共知的，因此，任何成型面端銑設(shè)計(jì)應(yīng)包括刀具的幾何形狀。這些參數(shù)對(duì)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(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)。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(jìn)行實(shí)驗(yàn)。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(guān)心的，而經(jīng)濟(jì)的加工操作中及競(jìng)爭(zhēng)激烈的市場(chǎng)中發(fā)揮了關(guān)鍵作用。實(shí)驗(yàn)顯示，這項(xiàng)工作將被用來測(cè)試切削速度，進(jìn)給速度，徑向前角和刀尖半徑與加工反應(yīng)。表面光潔度一直是一個(gè)重要的因素，在機(jī)械加工性能預(yù)測(cè)任何加工操作。所進(jìn)行的若干實(shí)驗(yàn)是用來決定該中心復(fù)合設(shè)計(jì)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?yàn)槎算娺^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(jìn)行實(shí)驗(yàn)鋼標(biāo)本。許多方法已經(jīng)被國內(nèi)外文獻(xiàn)報(bào)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(yù)測(cè)模型為目標(biāo)函數(shù)。這個(gè)數(shù)學(xué)模型已被作為目標(biāo)函數(shù)和優(yōu)化進(jìn)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(jì)技術(shù)的方法，在這幾個(gè)獨(dú)立變量的影響力供養(yǎng)變或反應(yīng)。一般二階多項(xiàng)式的回應(yīng)是，作為提供以下資料：如Y2型是估計(jì)響應(yīng)的基礎(chǔ)上的二階方程。傳統(tǒng)方法的優(yōu)化和搜索并不收費(fèi)，以及點(diǎn)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(fù)雜化。，并沒有一個(gè)單一的點(diǎn)。遺傳算法（ GA ）表格一類是自適應(yīng)啟發(fā)式原則的基礎(chǔ)上得出的，從動(dòng)態(tài)的人口自然遺傳學(xué)。計(jì)算是分三個(gè)階段進(jìn)行，以獲得結(jié)果在一代人或迭代。每單獨(dú)串的健身評(píng)估關(guān)于特定目標(biāo)函數(shù)。基因突變是經(jīng)常被采用交叉后，通過改變某些基因（即比特)后代可以取代整個(gè)人口（兩代人的方法）或取代少適合個(gè)人（ 獨(dú)立的做法）。所考慮的因素，對(duì)實(shí)驗(yàn)和分析切削速度，進(jìn)給速度，徑向前角和刀尖半徑。在此基礎(chǔ)上，總?cè)藬?shù)81實(shí)驗(yàn)（全階乘設(shè)計(jì)） ，每相結(jié)合的不同層次的因素，如表2所示，進(jìn)行了。 實(shí)驗(yàn)一種高精度“rambaudi rammatic 500 ”數(shù)控銑床，有垂直銑削頭。編碼值的變量，可用于環(huán)境質(zhì)量標(biāo)準(zhǔn)。因此，它必須有精心設(shè)計(jì)的一套實(shí)驗(yàn)。復(fù)制實(shí)現(xiàn)評(píng)價(jià)周期重復(fù)，直至終止準(zhǔn)則。非常適合個(gè)人或解決方案是給予機(jī)會(huì)，以復(fù)制，交換件，其遺傳信息，在交叉的程序，與其他高度合適的個(gè)人。為了使用GA解決所有問題，可變物典型地被輸入入代表可能解決方案對(duì)特定問題的串(二進(jìn)制編制程序)或染色體結(jié)構(gòu)。力學(xué)一加文很簡(jiǎn)單，其中涉及抄襲的二進(jìn)制字符串。 。加文來根據(jù)類別的非傳統(tǒng)的搜索和優(yōu)化技術(shù)。這些算法并不強(qiáng)勁。有效性選定的模型用于優(yōu)化工藝參數(shù)，是經(jīng)過檢驗(yàn)的幫助下統(tǒng)計(jì)測(cè)試，如F檢驗(yàn)，卡方檢驗(yàn)等[10] 。之間的關(guān)系，表面粗糙度及其他獨(dú)立變量可以發(fā)生情況如下：其中c是一個(gè)常數(shù)，并為A ， B ， C和D的指數(shù)為方便測(cè)定常數(shù)和指數(shù)，這個(gè)數(shù)學(xué)模型，必須由線性表演對(duì)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。3 方法論在這項(xiàng)工作中，數(shù)學(xué)模型已經(jīng)開發(fā)使用的實(shí)驗(yàn)結(jié)果與幫助響應(yīng)面方法論。結(jié)果已得到驗(yàn)證，通過比較優(yōu)化的加工條件得到了應(yīng)用遺傳算法。上述模式并沒有考慮到對(duì)刀具幾何形狀對(duì)表面粗糙度的影響。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(jìn)行了模型建造并繪制隨層等高線圖。數(shù)學(xué)模型已經(jīng)研制成功，可用在計(jì)算切削速度，進(jìn)給速度和軸向切深。目前已發(fā)現(xiàn)的壓力和摩擦法對(duì)芯片工具接口減少，增加進(jìn)給速度，并與下降的氣流角，而切削速度已微不足道，對(duì)一些材料依賴參數(shù)，工藝參數(shù)，歸納為經(jīng)驗(yàn)公式，作為職能的進(jìn)給速度和刀具旋轉(zhuǎn)角度為每個(gè)工作材料。對(duì)主軸速度，切削深度和進(jìn)給速度對(duì)切削力和表面粗糙度的影響進(jìn)行了研究。迪維斯等人[ 3 ]調(diào)查有關(guān)切削加工性能的五個(gè)銑刀具有不同螺旋角。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)。鑒于銑削運(yùn)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(gè)很好的模式應(yīng)當(dāng)包含徑向前角和刀尖半徑連同其他相關(guān)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(yù)測(cè)模型，但影響刀具幾何方面受到很少注意。因此，測(cè)量表面光潔度，可預(yù)測(cè)加工性能。在加文的程式中實(shí)現(xiàn)了最低值，表面粗糙度及各自的值都達(dá)到了最佳條件。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(cè)響應(yīng)面方法（丹參） ，在此基礎(chǔ)上的實(shí)驗(yàn)結(jié)果。附錄附錄1：英文原文Selection of optimum tool geometry and cutting conditionsusing a surface roughness prediction model for end millingAbstract 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 genetic algorithms (GA). The GA program gives minimum values of surface roughness and their respective optimal conditions.1 IntroductionEnd 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 of slots, p