机械加工刀具几何形状论文中英文资料对照外文翻译

1、中英文资料对照外文翻译文献综述 附录1： 英文原文 Selection of optimum tool geometry and cutting conditionsSelection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end millingusing a surface roughness prediction model for end milling AbstractAbstract Influence of tool geometr

2、y 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 conditi

3、ons (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 experim

4、ental 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 alg

5、orithms (GA). The GA program gives minimum values of surface roughness and their respective optimal conditions. 1 Introduction1 Introduction End milling is one of the most commonly used metal removal operations in industry because of its ability to remove material faster giving reasonably good surfa

6、ce 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, pockets, precision moulds and dies. Greater attention is given to dimensional accuracy and surface roughness of products by the

7、 industry these days. Moreover, surface finish influences mechanical properties such as fatigue behaviour, wear, corrosion, lubrication and electrical conductivity. Thus, measuring and characterizing surface finish can be considered for predicting machining performance. Surface finish resulting from

8、 turning operations has traditionally received considerable research attention, where as that of machining processes using multipoint cutters, requires attention by researchers. As these processes involve large number of parameters, it would be difficult to correlate surface finish with other parame

9、ters just by conducting experiments. Modelling helps to understand this kind of process better. Though some amount of work has 1 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 m

10、ajor affect on the power 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 developmen

11、t 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

12、machining parameters is of great concern in manufacturing industries, where the economy of machining operation plays a key role in the competitive market. In material removal processes, an improper selection of cutting conditions cause surfaces withhigh roughness and dimensional errors, and it is ev

13、en 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 todays 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 th

14、e 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 n

15、ose radius with the machining response i.e. surface roughness by modelling. The mathematical models thus developed are further utilized to find the optimum process parameters using genetic algorithms. 2 Review Process modelling and optimization are two important issues in manufacturing. The manufact

16、uring 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 t

17、he 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 concav

18、ity of a machined plane surface were measured. The central composite 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 an

19、d surface roughness were studied. The investigation showed that end mills with left hand helix 2 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 diff

20、erence between them regarding the surface roughness was large. Bayoumi et al. 4 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 commercially available workpiece m

21、aterials, 11L 17 free machining steel, 62- 35-3 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 cut

22、ting 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

23、 tool geometry simultaneously; besides these studies have not considered the optimization of the cutting process. As end milling is a process which involves a large number f parameters, combined influence of the significant parameters an only be obtained by modelling. Mansour and Abdallaet al. 5 hav

24、e developed a surface roughness model for the end milling of EN32M (a semi-free cutting carbon case hardening steel with improved merchantability). The mathematical model has been developed in terms of cutting speed, feed rate and axial depth of cut. The affect of these parameters on the surface rou

25、ghness has been carried out using response surface methodology (RSM). A first order equation covering the speed range of 3035 m/min and a second order equation covering the speed range of 2438 m/min were developed under dry machining conditions. Alauddin et al. 6 developed a surface roughness model

26、using RSM for the end milling of 190 BHN steel. First and second order models were constructed along with contour graphs for the selection of the proper combination of cutting speed and feed to increase the metal removal rate without sacrificing surface quality. Hasmi et al. 7 also used the RSM mode

27、l for assessing the influence of the workpiece material on the surface roughness of the machined surfaces. The model was developed for milling operation by conducting experiments on steel specimens. The expression shows, the relationship between the surface roughness and the various parameters; name

28、ly, 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 i

29、n the literature to solve optimization problems for machining parameters. Jain and Jain 8 have used neural networks for modeling and optimizing the machining conditions. The results have been validated by comparing the optimized machining conditions obtained using genetic algorithms. 3 Suresh et al.

30、 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.

31、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 Met

32、hodology3 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 pr

33、ocess. This mathematical model has been used as an objective function and the optimization was carried out with the help of genetic algorithms. 3.1 Mathematical formulation3.1 Mathematical formulation Response surface methodology (RSM) is a combination of mathematical and statistical techniques usef

34、ul for modelling and analyzing the problems in which several independent variables influence a dependent variable or response. The mathematical models commonly used are represented by: where Y is the machining response, is the response function and S, f , r are milling variables and is the error whi

35、ch 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 m

36、athematical 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

37、, can be represented as follows: where Y1 is the estimated response based on the first-order 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

38、 respectively, is the experimental error and b values are the estimates of corresponding parameters. 4 The general second order polynomial response is as given below: where Y2 is the estimated response based on the second order equation. The parameters, i.e. b0, b1, b2, b3, b4, b12, b23, b14, etc. a

39、re 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 F-test, chi square test, etc. 10. 3.2 Optimization using genetic algorithms Most of the researchers have used tradi

40、tional 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 ar

41、e inclined to obtain a local optimal solution. Numerous constraints and number of passes make the machining optimization problem more complicated. So, it was decided to employ genetic algorithms as an optimization technique. GA come under the class of non-traditional search and optimization techniqu

42、es. GA are different from traditional optimization techniques in the following ways: 1.GA work with a coding of the parameter set, not the parameter themselves. 2.GA search from a population of points and not a single point. 3.GA use information of fitness function, not derivatives or other auxiliar

43、y knowledge. 4.GA use probabilistic transition rules not deterministic rules. 5.It is very likely that the expected GA solution will be the global solution. Genetic algorithms (GA) form a class of adaptive heuristics based on principles derived from the dynamics of natural population genetics. The s

44、earching process simulates the natural evaluation of biological creatures and turns out to be an intelligent exploitation of a random search. The mechanics of a GA is simple, involving copying of binary strings. Simplicity of operation and computational efficiency are the two main attractionsof the

45、genetic algorithmic approach. The computations are carried out in three stages to get a result in one generation or iteration. The three stages are reproduction, crossover and mutation. In order to use GA to solve any problem, the variable is typically encoded into a string (binary coding) or chromo

46、some structure which represents a possible solution to the given problem. GA begin with a population of strings (individuals) created at random. The fitness of each individual string is evaluated with respect to the given objective function. Then this initial population is operated on by three main

47、operators reproduction cross over and mutation to create, hopefully, a better population. Highly fit individuals or solutions are given the opportunity to reproduce by exchanging pieces of their genetic information, in the crossover procedure, with other highly fit individuals. This produces new “of

48、fspring” solutions, which 5 share some characteristics taken from both the parents. Mutation is often applied after crossover by altering some genes (i.e. bits) in the offspring. The offspring can either replace the whole population (generational approach) or replace less fit individuals (steady sta

49、te approach). This newpopulationisfurtherevaluatedandtestedforsometerminationcriteria.The reproduction-cross over mutation- evaluation cycle is repeated until the termination criteria are met. 中文翻译中文翻译 6 选择最佳工具，几何形状和切削条件 利用表面粗糙度预测模型端铣 摘要：摘要：刀具几何形状对工件表面质量产生的影响是人所共知的，因此，任何成型面端铣 设计应包括刀具的几何形状。在当前的工作中，实验

50、性研究的进行已看到刀具几何（径向 前角和刀尖半径）和切削条件（切削速度和进给速度） ，对加工性能，和端铣中碳钢影 响效果。第一次和第二次为建立数学模型，从加工参数方面，制订了表面粗糙度预测响应 面方法（丹参） ，在此基础上的实验结果。该模型取得的优化效果已得到证实，并通过 了卡方检验。这些参数对表面粗糙度的建立，方差分析极具意义。通过尝试也取得了优化 表面粗糙度预测模型，采用遗传算法（ GA ） 。在加文的程式中实现了最低值，表面粗 糙度及各自的值都达到了最佳条件。 1 1 导言导言 端铣是最常用的金属去除作业方式，因为它能够更快速去除物质并达到合理良好的表 面质量。它可用于各种各样的制造工业

51、，包括航空航天和汽车这些以质量为首要因素的行 业，以及在生产阶段，槽孔，精密模具和模具这些更加注重尺寸精度和表面粗糙度产品的 行业内。此外，表面光洁度还影响到机械性能，如疲劳性能，磨损，腐蚀，润滑和导电性。 因此，测量表面光洁度，可预测加工性能。 车削过程对表面光洁度造成的影响历来倍受研究关注，对于加工过程采用多刀，用机 器制造处理，都是研究员需要注意的。由于这些过程涉及大量的参数，使得难以将关联表 面光洁度与其他参数进行实验。在这个过程中建模有助于更好的理解。在过去，虽然通过 许多人的大量工作，已开发并建立了表面光洁度预测模型，但影响刀具几何方面受到很少 注意。然而，除了切向和径向力量，径向

52、前角对电力的消费有着重大的影响。它也影响着 芯片冰壶和修改芯片方向人流。此外，研究人员 1 也指出，在不影响表面光洁度情况下， 刀尖半径发挥着重要作用。因此，发展一个很好的模式应当包含径向前角和刀尖半径连同 其他相关因素。 对于制造业，建立高效率的加工参数几乎是将近一个世纪的问题，并且仍然是许多研 究的主题。获得最佳切削参数，是在制造业是非常关心的，而经济的加工操作中及竞争激 烈的市场中发挥了关键作用。在材料去除过程中，不当的选择切削条件造成的表面粗糙度 高和尺寸误差，它甚至可能发生动力现象：由于自动兴奋的震动，可以设定在 2 。鉴于 铣削运行在今天的全球制造业中起着重要的作用，就必要优化加工

53、参数。因此通过努力， 在这篇文章中看到刀具几何 （径向前角和刀尖半径） 和切削条件 （切削速度和进给速度） ， 表面精整生产过程中端铣中碳钢的影响。实验显示，这项工作将被用来测试切削速度，进 给速度，径向前角和刀尖半径与加工反应。数学模型的进一步利用，寻找最佳的工艺参数， 并采用遗传算法可促进更大发展。 2 回顾 7 建模过程与优化，是两部很重要的问题，在制造业。生产过程的特点是多重性的动态 互动过程中的变数。表面光洁度一直是一个重要的因素，在机械加工性能预测任何加工操 作。为了开发和优化表面粗糙度模型，有必要了解目前在这方面的工作的状况。 迪维斯等人 3 调查有关切削加工性能的五个铣刀具有不

54、同螺旋角。 分别对铝合金L65 的3向铣削过程（面，槽和侧面）进行了切削试验，并对其中的切削力，表面粗糙度，凹 状加工平面进行了测量。所进行的若干实验是用来决定该中心复合设计的。切削性能的立 铣刀则被评定采用方差分析。对主轴速度，切削深度和进给速度对切削力和表面粗糙度的 影响进行了研究。调查显示铣刀与左手螺旋角一般不太具有成本效益比。上下铣方面切削 力与右手螺旋角，虽然主要区别在于表面粗糙度大，但不存在显著差异。 拜佑密等人 4 研究过工具对旋转角度，进给速度和切削速度在机械工艺参数（压力，摩擦参数）的影响， 为端铣操作常用三种商用工件材料， 11L17易切削钢，62-35-3易切削黄铜和铝2

55、024年使用 单一槽高速钢立铣刀。目前已发现的压力和摩擦法对芯片-工具接口减少，增加进给速度， 并与下降的气流角，而切削速度已微不足道，对一些材料依赖参数，工艺参数，归纳为经 验公式，作为职能的进给速度和刀具旋转角度为每个工作材料。不过，研究人员也还有没 有考虑到的影响，如切削条件和刀具几何同步，而且这些研究都没有考虑到切削过程的优 化。 因为端铣过程介入多数f参量，重大参量的联合只能通过塑造得到。曼苏尔和艾布达莱 特基地 5 已开发出一种表面粗糙度模式，为年底铣EN32M（半自由切削碳硬化钢并改进 适销性） 。数学模型已经研制成功，可用在计算切削速度，进给速度和轴向切深。这些参 数对表面粗糙度的影响已进行了响应面分析法（丹参） 。分别制定了一阶方程涵盖的速度 范围为30-35米/分，一类二阶方程涵盖速度范围的24-38米/分的干切削条件。 艾尔艾丁等 人 6 开发出一种表面粗糙度模型，用丹参，为端铣190BHN钢。为选择适当的组合，切割 速度和伺服，增加金属去除率并不牺牲的表面质量，多此进行了模型建造并绘