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1、 xxxxxxxxxxxxxxxxxx毕业设计(论文)外文资料翻译 教 学 点： xxxxxxxxxxxxxxxxxxxxxxx 专 业 ： 机械电子工程 姓 名 ： xxxxxxxxx 准考证号： xxxxxxxxxxxx (用外文写)外文出处： High speed CNC machining of AISI 304 stainless steel;Optimizationg of Procese parameters by MOGA 附 件： 1.外文资料翻译译文；2.外文原文。 指导教师评语：文章选题符合毕业设计要求，译意正确，语句通顺；译文量基本可以。 签名： 年 月 日附件1：外文

2、资料翻译译文高速数控AISI 304不锈钢加工；工艺参数优化的多目标遗传算法V. S. Thangarasu*, G. Devaraj, R. Sivasubramanian摘要这项工作是建立与基本参数的关系的反应，即表面粗糙度（Ra）及材料去除率（MRR）。田口通过Box-Behnken响应（响应面法）方法被用于开发预测公式和多目标遗传算法（MOGA）是用于高速数控铣削过程具有较高的主轴转速优化，更好的表面光洁度和材料去除率的进给速度和切削深度。RA和MRR是各种工艺参数可控结式主轴转速，进给速度和切削深度，材料的硬度，湿的或干的加工，类型的插入，和工作动力，与刀具的几何形状的刀具磨损率。为

3、加工参数的选择需要科学，条件和刀具的最合适的类型，一直觉得多年来消除减小误差的人为干预，提高系统生产力。高速数控铣削过程是科学的优化使用这种方法制定一个数学模型，涉及的表面粗糙度和材料去除率切削参数的端铣，精确的主轴转速，进给速度，切削深度和插入式，减少了人为干预的过程。关键词：数控铣床，优化，表面光洁，DOE，方差分析，材料去除率，箱Benkhen法，遗传算法，不锈钢1.景区简介中小企业参与国内和全球客户的精密零件的制造。这些公司的能力主要取决于他们以合理的价格与加工的最佳实践生产兼容产品的能力。大多数这些制造业也取决于技术和用于切削条件和刀具选择的最优选择机床操作经验。没有拇指规则是用来适

4、合特定产品的条件。因此，相当大的努力取得进展，以消除工手册为基础的切削条件，为某一特定工作/订单的刀具选择保守的使用方法。为加工参数的选择需要科学，条件和刀具的最合适的类型，一直觉得多年来消除减小误差的人为干预。人们早已认识到，在切削条件，如进给速度，切削速度和切削深度，应科学地选择优化的加工操作和提高生产力经济学。尽管在低速数控加工建立最佳的切削速度的早期作品，一些研究人员（moshat等人。，2010；穆斯塔法阿里，2011；方等人。，2007）报道，工艺参数需要优化高速数控铣床是中小制造业不可缺少的和昂贵的过程。总的趋势是降低加工成本、时间和提高精度和生产率。这项工作是建立与基本参数的关

5、系的反应，即表面粗糙度（Ra）及材料去除率（MRR）。田口法（基于响应面法）方法被用于开发数控高速铣削过程中具有较高的主轴速度预测公式，具有更好的表面光洁度和morematerial去除率的进给速度和切削深度。表面质量是最重要的客户要求；加工零件的表面质量的指标是表面粗糙度。表面粗糙度的各种工艺参数可控结式主轴转速，进给速度和切削深度，材料的硬度，湿的或干的加工，和工作动力，刀具的磨损率随着刀具几何。研究工作（拉蒙quiza萨尔迪纳斯等人。库萨等人2006。2007）已经解决了切削速度的影响，饲料，切削深度，刀尖半径等因素对表面粗糙度，表面粗糙度和材料去除率的同时优化（材料去除率）并没有讨论，

6、因为他们是独立的，非线性的。这项工作是获得了一个数学模型，涉及的表面粗糙度和材料去除率切削参数的端铣，精确的主轴转速，进给速度，切割和插入式深度。2对中小企业的案例研究计划的案例研究的数控加工进行确定的各种参数的影响深度。因此，重要的参数，在文献中表示（moshat等人。，2010，拉蒙quiza萨尔迪纳斯等人2006，银白杨和弗氏2008）和信息已在下列数控工作订单制造企业车间收集。（我）先进制造实验室，CIT，哥印拜陀；（ii）coindia现代工具室单元II，民用机场，哥印拜陀；（三）斯里兰卡gowrish数控有限公司chinnavedampatti，哥印拜陀；（四）斯里兰卡partha

7、sarathicnc公司chinnavedampatti，哥印拜陀；（五）genune数控有限公司kalappatti，哥印拜陀；（六）宝石精密工程，及甘纳巴迪，哥印拜陀。参数的确定是研究在2007和2008进行了。3文献调查详细调查研究最近发表在著名的期刊进行了深入的研究以获得最好的知识和约束。通过moshat等人的研究。（2010）在数控铣削加工参数采用基于PCA的田口方法，为优化的目的而不同时优化的表面粗糙度和材料去除率的研究，优化研究。routara等人。（2010）给出了轮廓的软质材料铣削参数优化研究数控端面铣削UNS c34000中铅黄铜多的表面粗糙度特性和一个单一的响应研究的基础

8、上确定要研究的参数。在实验室进行了案例研究提示实时研究，为制造企业在这里找到解决方案。穆斯塔法和阿里（2006）分析了工件的长度和直径的影响，切削深度和进给量，同时也调查了切削速度，这是一个重要的加工参数，保持恒定。田口方法在这项工作中使用，以获得更可靠和最佳的结果。库萨等人。（2011）解释了过度使用高速铣精加工的铝和镁更频繁的使用的结果的表面质量高和更短的加工时间省略磨。kadirgama和Noor等人（2008）强调了表面粗糙度的优化铣削铝合金（AA6061-T6）硬质涂层刀片采用响应曲面法（RSM）和径向基函数网络（RBFN）预测的推力和表面粗糙度。kechagias（2011）提出了

9、刀具的几何形状和在端铣铝合金5083，利用田口L18标准正交阵列实验研究了表面纹理的切削参数的影响。方等人。（2007）已经进行了广泛的研究在过去的后刀面磨损和月牙洼磨损，在高速切削加工对刀具刃口磨损的效果，对切削力和振动的3D高速完成车削镍基超合金Inconel 718。银白杨和弗洛里奇（2008）编制了高速铣削钛合金的个案研究，对不同的冶金和加工条件对研究考虑提供基础。钛合金是基于案例研究为中等切削速度和进给率得到飞机的材料更好的材料去除的输入参数的设置进行了高速铣削过程。Aggarwal和Singh（2005）回顾了各种线性和非线性优化技术和详细的相对优势进行了讨论和推断的非线性优化方法

10、，最适合于加工过程的优化。作者的建议是遵循适当的优化方法的基础上，在手的问题，也要使用基础统计的方法得到初始基本可行解和非线性遗传算法根据各自的问题和解决方案的需求。真正的框架是由曾与陈进行研究开发（2005）在两个阶段的参数优化利用田口稳健设计方法的准确性较好，这促使与田口DOE和同时优化过程的两个阶段分析方法，把各种参数对表面粗糙度要求的案例研究。赛济德（2005）的端面铣削过程进行了以获得最佳的参数，切屑的形成和表面粗糙度的仿真模型，分析并建议考虑参数的数量更高层次的分析得到的参数包括切削力的最佳设置，刀具的磨损率和湿的或干的过程。fidan和ElSawy（2002）突出了知识为基础的解

11、决方案，使用不同的切削条件下的铣削过程的优化，找到了获得加工涉及大量的变参数软件解决方案。楼等。（1999）开发的数控铣削过程但只集中于平均粗糙度但不在材料去除率，表面粗糙度的预测技术。上述文献的分析给了我们一个机会来了解国家的艺术和为中小型企业需要时间的，在实验进行的案例研究。附件2：外文原文High speed CNC machining of AISI 304 stainless steel; Optimization of process parameters by MOGAV. S. Thangarasu*, G. Devaraj, R. SivasubramanianAbstrac

12、tThis work is to establish the relationship with the basic parameters to the responses namely Surface roughness (Ra) and Material Removal Rate (MRR). The Taguchi based Box-Behnken RSM (Response Surface Methodology) method is used to develop prediction formula and Multi Objective Genetic Algorithm (M

13、OGA) is used for High speed CNC milling process optimization with higher Spindle speed, Feed rate and Depth of cut for better surface finish and material removal rate. The Ra and MRR is resultant of various controllable process parameters are Spindle speed, Feed rate and Depth of Cut, Hardness of th

14、e material, wet or dry machining, type of insert, and Dynamic forces on the job, tool wear rate with Cutter geometry. The need for scientific selection of machining parameters, conditions and the most suitable type of cutting tool has been felt over the years to eliminate the human intervention to r

15、educe the errors and to improve productivity of the system. High speed CNC milling process is scientifically optimized using this method formulating a mathematical model that relates the surface roughness and MRR with cutting parameters in end milling, precisely to the Spindle speed, Feed rate, Dept

16、h of cut and insert type, which reduces human intervention on the process.Keywords: CNC milling, optimization, surface finish, DOE, ANOVA, material removal rate, Box-Benkhen method, genetic algorithm, stainless steel1. IntroductionSmall and medium size enterprises are involved in manufacturing of pr

17、ecision components for domestic and global customers. The capability of these companies mainly lies on their ability to produce compatible products at affordable price with best practices of machining. Most of these manufacturing industries are depending on the skill and experience of machine tool o

18、perators for optimal selection of cutting conditions and choice of cutting tools. There is no thumb rule is in use to suit the product specific conditions. Thus considerable efforts are in progress to eliminate the use of tool makers handbook-based conservative method of selection of cutting conditi

19、ons and cutting tool selection for a specific job/ order. The need for scientific selection of machining parameters, conditions and the most suitable type of cutting tool has been felt over the years to eliminate the human intervention to reduce the errors. It has long been recognized that condition

20、s during cutting, such as feed rate, cutting speed and depth of cut, should be selected scientifically to optimize the economics of machining operations and to improve productivity. Despite early works on establishing optimum cutting speeds in low speed CNC machining, a few researchers (Moshat et al

21、., 2010; Mustafa and Ali, 2011; Fang et al., 2007) have reported that the process parameters need to be optimized as high speed CNC milling is an indispensable and costly process for small and medium manufacturing industry. The general trend is to reduce the machining cost and time and improving the

22、 accuracy and productivity.This work is to establish the relationship with the basic parameters to the responses namely Surface roughness (Ra) and Material Removal Rate (MRR). The Taguchi based RSM (Response Surface Methodology) method is used to develop prediction formula for High speed CNC milling

23、 process with higher Spindle speed, Feed rate and Depth of cut with better surface finish and morematerial removal rate. The surface quality is one of the most important customer requirements; the indicator of surface quality on machined parts is surface roughness. The surface roughness is resultant

24、 of various controllable process parameters are Spindle speed, Feed rate and Depth of Cut, Hardness of the material, wet or dry machining, and Dynamic forces on the job, tool wear ratewith Cutter geometry. The research works (Ramon Quiza Sardinas et al. 2006; Cusa et al. 2007) have addressed the eff

25、ects of the cutting speed, feed, depth of cut, nose radius and other factors on the surface roughness but simultaneous optimization of Surface roughness and MRR (Material Removal Rate) was not discussed because they are independent and nonlinear. This work is to obtain a mathematical model that rela

26、tes the surface roughness and MRR with cutting parameters in end milling, precisely to the Spindle speed, Feed rate, Depth of cut and insert type.2. Case study on small and medium enterprisesPlanned case study on the CNC machining was conducted to ascertain the depth of influence of various paramete

27、rs. Thus important parameters are indicated in the literature (Moshat et al., 2010, Ramon Quiza Sardinas et al 2006, Abele and Frolich 2008) and information has been collected at the shop floor of the following CNC job order manufacturing companies. (i) Advanced Manufacturing Laboratory, CIT, Coimba

28、tore; (ii) COINDIA Modern tool room Unit- II, Civil Aerodrome, Coimbatore; (iii) Sri Gowrish CNC Pvt. Ltd. Chinnavedampatti, Coimbatore; (iv) Sri ParthasarathiCNC Pvt. Ltd Chinnavedampatti, Coimbatore; (v) Genune CNC Pvt. Ltd. Kalappatti, Coimbatore; (vi) Gem precision Engineering, Ganapathy, Coimba

29、tore. The parameters ascertained are for the studies conducted during the 2007 & 2008.3. Literature surveyDetailed survey on the research works recently published in the eminent journals was studied in depth to acquire best knowledge and constraints of the study. The research paper by Moshat et

30、al. (2010) studied on optimization of CNC milling process parameters using PCA based Taguchi method that had served the purposes of optimization but not simultaneously optimize the surface roughness and the material removal rate in the study. Routara et al. (2010) had given the outline of the soft m

31、aterial milling parameters with their study on optimization of CNC end milling of UNS C34000 medium leaded brass with multiple surface roughness characteristics and a single response study provided base in determining the parameters to be studied. The case studies conducted at the laboratory have pr

32、ompted for the real time studies and to find the solution for the manufacturing firms around the place. Mustafa and Ali (2006) analyzed the effect of the length and diameter of working piece, cutting depth and feed that were also investigated while the cutting speed, which is an important machining

33、parameter, was kept constant. Taguchi method was used in this work in order to obtain more reliable and optimum results. Cusa et al. (2011) explained about the excessive use of High-speed milling for precision machining of aluminium and magnesium and more frequently used results in high quality of t

34、he surface and shorter machining times by omitting grinding. Kadirgama and Noor et al (2008) highlighted about optimization of the surface roughness when milling Aluminium alloys (AA6061-T6) with carbide coated inserts using Response Surface Method (RSM) and Radian Basis Function Network (RBFN) to p

35、redict thrust force and surface roughness. Kechagias (2011) brought out the influence of cutter geometry and cutting parameters during end milling on the surface texture of aluminium alloy 5083 that was experimentally investigated using Taguchi L18 standard orthogonal array. Fang et al. (2007) had g

36、iven the extensive research has been conducted in the past on tool flank wear and crater wear in high-speed machining by investigating the effect of tool edge wear on the cutting forces and vibrations in 3D high-speed finish turning of nickel-based super alloy Inconel 718. Abele and Frolich (2008) h

37、ave compiled the case study of high speed milling of titanium alloys and have provided base for different metallurgical and machining conditions to be taken into account for the study. The case study of titanium based alloys were conducted for the high speed milling process for sets of input paramet

38、ers with moderate cutting speeds and feed rate to get better material removal of aircraft materials. Aggarwal and Singh (2005) reviewed various linear and non linear optimization techniques in detail and relative advantages are also discussed and inferred for the non linear optimization methods, the

39、 most suited for the optimization of machining processes. The suggestion of the authors is to follow appropriate set of optimization methods based on the problem on hand and also to use a base statistical method to get the initial basic feasible solution and a non linear like genetic algorithm according to the respective problem and solution requirements. The real frame work was developed by the study conducted by Tzeng and Chen (2005) on tw