1、1 Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling Abstract : 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 geometr
2、y. 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
3、 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 r
4、oughness 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、 Introduction End milling is on
5、e of the most commonly 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 produc
6、tion of slots, pockets, precision and dies. Greater attention is given to dimensional accuracy and surface roughness of products by the industry these days. Moreover, surface finish influences mechanical properties such as fatigue behaviour, wear, corrosion, lubrication and electrical conductivity.
7、Thus, measuring and characterizing surface finish can be considered for predicting machining performance. Surface finish resulting from turning operations has traditionally received considerable research attention, where as that of machining processes using cutters, requires attention by researchers
8、. As these processes involve large number of parameters, it would be difficult to correlate surface finish with other parameters just by conducting experiments. Modeling helps to understand this kind of process better. Though some amount of work has been carried out to develop surface finish predict
9、ion 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 consumption apart from tangential and radial forces. It also influences chip curling and modifies chip flow direction. In addition to this, researchers 1
10、have also observed that the nose radius plays a significant role 2 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 tha
11、t 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 competitive market. In material removal p
12、rocesses, 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 todays manufacturing world,
13、 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 medi
14、um 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 i.e. surface roughness by modeling. The mathematical models thus developed are further utilized to find the optimum process parameters
15、 using genetic algorithms. 2 、 Review Process modeling 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 perf
16、ormance 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
17、on 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 composite design was used to decide on the number of experiments to be conducted. The cutting performance of the end mills w
18、as 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
19、is no significant difference between up milling and down milling with regard to the cutting force, although the difference between them regarding the surface roughness was large. 4 have studied the affect of the tool rotation angle, feed rate and cutting speed on the mechanistic process parameters (
20、pressure, friction parameter) for end milling operation with three commercially available 3 workpiece materials, 11 L 17 free machining steel, 62- 35-3 free machining brass and 2024 using a single fluted HSS milling cutter. It has been found that pressure and friction act on the chip tool interface
21、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 ma
22、terial. However, researchers have not taken into account the effects of cutting conditions and 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 o
23、f the significant parameters an only be obtained by modeling. 5 have 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 ax
24、ial depth of cut. The affect of these parameters on the surface roughness 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
25、 conditions. 6 developed a surface roughness model 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 sur
26、face quality.7 also used the RSM model 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 rough
27、ness and the various parameters； 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
28、 of many methods have been reported in 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 usin
29、g genetic 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 individual process parameters. They have also optimized the turning process using the surface roughness prediction model as the objective function. 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
