【正文】 it would be difficult to correlate surface finish with other parameters just by conducting experiments. Modelling helps to understand this kind of process better. Though some amount of work has been carried out to develop surface finish prediction models in the past, the effect of tool geometry has received little attention. However, the radial rake angle has a major affect on the power 2 consumption apart from tangential and radial forces. It also influences chip curling and modifies chip flow direction. In addition to this, researchers [1] have also observed that the nose radius plays a significant role in affecting the surface finish. Therefore the development of a good model should involve the radial rake angle and nose radius along with other relevant factors. Establishment of efficient machining parameters has been a problem that has confronted manufacturing industries for nearly a century, and is still the subject of many studies. Obtaining optimum machining parameters is of great concern in manufacturing industries, where the economy of machining operation plays a key role in the petitive market. In material removal processes, an improper selection of cutting conditions cause surfaces with high roughness and dimensional errors, and it is even possible that dynamic phenomena due to auto excited vibrations may set in [2]. In view of the significant role that the milling operation plays in today?s manufacturing world, there is a need to optimize the machining parameters for this operation. So, an effort has been made in this paper to see the influence of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on the surface finish produced during end milling of medium carbon steel. The experimental results of this work will be used to relate cutting speed, feed rate, radial rake angle and nose radius with the machining response . surface roughness by modelling. The mathematical models thus developed are further utilized to find the optimum process parameters using geic algorithms. 2 Review Process modelling and optimization are two important issues in manufacturing. The manufacturing processes are characterized by a multiplicity of dynamically interacting process variables. Surface finish has been an important factor of machining in predicting performance of any machining operation. In order to develop and optimize a surface roughness model, it is essential to understand the current status of work in this area. Davis et al. [3] have investigated the cutting performance of five end mills having various helix angles. Cutting tests were performed on aluminium alloy L 65 for three milling processes (face, slot and side), in which cutting force, surface roughness and concavity of a machined plane surface were measured. The central posite design was used to decide on the number of experiments to be conducted. The cutting performance of the end mills was assessed using variance analysis. The affects of spindle speed, depth of cut and feed rate on the cutting force and surface roughness were studied. The investigation showed that end mills with left hand helix angles are generally less cost effective than those with right hand helix angles. There is no significant difference between up milling and down milling with regard tothe cutting force, although the difference between them regarding the surface roughness was large. Bayoumi et al. [4] 3 have studied the affect of the tool rotation angle, feed rate and cutting speed on the mechanistic process parameters (pressure, friction parameter) for end milling operation with three mercially available workpiece materials, 11 L 17 free machining steel, 62 353 free machining brass and 2024 aluminium using a single fluted HSS milling cutter. It has been found that pressure and friction act on the chip – tool interface decrease with the increase of feed rate and with the decrease of the flow angle, while the cutting speed has a negligible effect on some of the material dependent parameters. Process parameters are summarized into empirical equations as functions of feed rate and tool rotation angle for each work material. However, researchers have not taken into account the effects of cutting conditions and tool geometry simultaneously。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。在過(guò)去，雖然通過(guò)許多人的大量工作，已開(kāi)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。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)。 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。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(lài)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。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(xiàn)圖。結(jié)果已得到驗(yàn)證，通過(guò)比較優(yōu)化的加工條件得到了應(yīng)用遺傳算法。 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(xiàn)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。這些算法并不強(qiáng)勁。眾多的制約因素和月票數(shù)目，使加工優(yōu)化問(wèn)題更加復(fù)雜化。 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。他們還優(yōu)化了車(chē)削加工用表面粗糙度預(yù)測(cè)模型為目標(biāo)函數(shù)。該模型是銑操作進(jìn)行實(shí)驗(yàn)鋼標(biāo)本。 因?yàn)槎算娺^(guò)程介入多數(shù) f參量，重大參量的聯(lián)合只能通過(guò)塑造得到。所進(jìn)行的若干實(shí)驗(yàn)是用來(lái)決定該中心復(fù)合設(shè)計(jì)的。實(shí)驗(yàn)顯示，這項(xiàng)工作將被用來(lái)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)。它也影響著芯片冰壺和修改芯片方向人流。它可用于各種各樣的制造工業(yè)，包括航空航 天和汽車(chē)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)。 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 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 in