机械加工外文翻译--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(编辑修改稿)

机械加工外文翻译--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(编辑修改稿)内容摘要：

local optimal solution. Numerous constraints and number of passes make the machining optimization problem more plicated. So, it was decided to employ geic algorithms as an optimization technique. GA e under the class of nontraditional search and optimization techniques. GA are different from traditional optimization techniques in the following ways: work with a coding of the parameter set, not the parameter themselves. search from a population of points and not a single point. use information of fitness function, not derivatives or other auxiliary knowledge. use probabilistic transition rules not deterministic rules. is very likely that the expected GA solution will be the global solution. Geic algorithms (GA) form a class of adaptive heuristics based on principles derived from the dynamics of natural population geics. The searching 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 putational efficiency are the two main attractions of the geic algorithmic approach. The putations 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 chromosome 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 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 geic information, in the crossover procedure, with other highly fit individuals. This produces new “offspring” solutions, which share some characteristics taken from both the parents. Mutation is often applied after crossover by altering some genes (. bits) in the offspring. The offspring can either replace the whole population (generational approach) or replace less fit individuals (steady state approach). This new population is further evaluated and tested for some termination criteria. The reproductioncross over mutation evaluation cycle is repeated until the termination criteria are met. 6 4 Experimental details For developing models on the basis of experimental data, careful planning of experimentation is essential. The factors considered for experimentation and analysis were cutting speed, feed rate, radial rake angle and nose radius. Experimental design The design of experimentation has a major affect on the number of experiments needed. Therefore it is essential to have a well designed set of experiments. The range of values of each factor was set at three different levels, namely low, medium and high as shown in Table 1. Based on this, a total number of 81 experiments (full factorial design), each having a bination of different levels of factors, as shown in Table 2, were carried out. The variables were coded by taking into account the capacity and limiting cutting conditions of the milling machine. The coded values of variables, to be used in Eqs. 3 and 4, were obtained from the following transforming equations: where x1 is the coded value of cutting speed (S), x2 is the coded value of the feed rate ( f ), x3 is the coded value of radial rake angle(α) and x4 is the coded value of nose radius (r). Experimentation A high precision „Rambaudi Rammatic 500‟ CNC milling machine, with a vertical milling head, was used for experimentation. The control system is a CNC FIDIA12 pact. The cutting tools, used for the experimentation, were solid coated carbide end mill cutters of different radial rake angles and nose radii (WIDIA: DIA20 X FL38 X OAL 102 MM). The tools are coated with TiAlN coating. The hardness, density and transverse rupture strength are 1570 HV 30, gm/cm3 and 3800 N/mm2 respectively. AISI 1045 steel specimens of 10075 mm and 20 mm thickness were used in the present study. All the specimens were annealed, by holding them at 850 ◦C for one hour and then cooling them in a furnace. The chemical analysis of specimens is presented in Table 3. The hardness of the workpiece material is 170 BHN. All the experiments were carried out at a constant axial depth of cut of 20 mm and a radial depth of cut of 1 mm. The surface roughness (response) was measured with Talysurf6 at a mm cutoff value. An average of four measurements was used as a response value. 5 Results and discussion The influences of cutting speed, feed rate, radial rake angle and nose radius have been assessed by conducting experiments. The variation of machining response with respect to the 7 variables was shown graphically in Fig. 1. It is seen from these figures that of the four dependent parameters, radial rake angle has definite influence on the roughness of the surface machined using an end mill cutter. It is felt that the prominent influence of radial rake angle on the surface generation could be due to the fact that any change in the radial rake angle changes the sharpness of the cutting edge on the periphery, changes the contact length between the chip and workpiece surface. Also it is evident from the plots that as the radial rake angle changes from 4◦ to 16◦, the surface roughness decreases and then increases. Therefore, it may be concluded here that the radial rake angle in the range of 4◦ to 10◦ would give a better surface finish. Figure 1 also shows that the surface roughness decreases first and then increases with the increase in the nose radius. Thi。