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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 asss the performance of end milling should include the tool geometry. In the prent work, experimental studies have been conducted to e the effect of tool geometry (radial rake angle and no radius) and cutting conditions (cutting speed and feed rate) on the machining performance during end milling of medium carbon steel. The first and cond order
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mathematical models, in terms of machining parameters, were developed for surface roughness prediction using respon surface methodology (RSM) on the basis of experimental results. The model lected for optimization has been validated with the Chi square test. The significance of the 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 algorithms (GA). The GA program gives minimum values of surface roughness and their respective optimal conditions.
1 Introduction
End milling is one of the most commonly ud metal removal operations in industry becau of its ability to remove material faster giving reasonably good surface quality. It is ud in a variety of manufacturing industries including aerospace and automotive ctors, 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 industry the days. Moreover, surface finish influences me
chanical 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 turning operations has traditionally received considerable rearch attention, where as that of machining process using multipoint cutters, requires attention by rearchers. As the process involve large number of parameters, 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 consumption apart from tangential and radial forces. It also influences chip curling and modifies chip flow direction. In addition to this, rearchers [1] have also obrved that the no radius plays a significant role in affecting the surface finish. Therefore the development of a good model should involve the radial rake angle and no 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 competitive market. In material removal process, an improper lection of cutting conditions cau surfaces with high roughness and
dimensional errors, and it is even possible that dynamic phenomena due to auto excited vibrations may t 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 e the influence of tool geometry(radial rake angle and no 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 ud to relate cutting speed, feed rate, radial rake angle and no radius with the machining surface roughness by modelling. The mathematical models thus developed are further utilized to find the optimu
m process parameters using genetic algorithms.
2 Review
海鲈鱼Process modelling and optimization are two important issues in manufacturing. The manufacturing process 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 esntial 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 process (face, slot and side), in which cutting force, surface roughness and concavity of a machined plane surface were measured. The central composite design was ud to decide on the number of experiments to be conducted. The cutting performance of the end mills was assd using variance analysis. The affects of spindle speed, depth of cut and feed rate on the cutting force and surface roughness were studied. The investigat
ion showed that end mills with left hand helix angles are generally less cost effective than tho 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] 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 materials, 11 L 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 decrea with the increa of feed rate and with the decrea 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,
rearchers have not taken into account the effects of cutting conditions and tool geometr
y simultaneously; besides the studies have not considered the optimization of the cutting process.
