International Journal on Emerging Trends in Technology (IJETT) ISSN: 2455 - 0124 (ONLINE) | 2350 - 0808 (PRINT) | (IF: 0.456) Volume 3 | Issue 2 | July - 2016 | Special Issue www.ijett.in
Use of R.S.M. for Design of Experiments and Optimization of Parameters in Milling Operations N. L. Bhirud1, R. R. Gawande 2
[email protected],
[email protected] Department of Mechanical Engineering1, 2 Bapurao Deshmukh College of Engineering, RSTMU, Nagpur 1, 2
ABSTRACT Experimentation is an important part of manufacturing domain. If these experiments are planned experiments then, the data can be converted into the empirical models, which can be used for finding out the performance of the system under different situations. It helps in planning and execution of activities with optimized use of available resources. The design of experiments is usually carried out by using methods like full factorial design, fractional factorial design, Taguchi design and Response surface design. In this paper, we have reviewed the recent literature for the use of response surface methodology (RSM) for milling operations. The aim of this work was to study and present the recent literature in this field. Initially, the use of RSM for modeling and analysis of surface roughness is presented, followed by cutting temperature, cutting force and other parameters. RSM is found to be a very useful and powerful tool for design of experiments and optimization of process parameters in milling operations. It was used by many researchers for modeling and analysis of surface roughness vibration amplitude, tool wear, cutting temperatures, cutting forces and burr dimensions. Hybrid analysis combining RSM with evolutionary algorithms like genetic algorithms and simulated annealing was also successfully employed.
Keywords Milling, Design methodology.
of
Experiments,
Response
surface
1. INTRODUCTION Out of all the manufacturing processes, machining is considered as one of the most versatile process. In recent times, machining processes are facing constant pressures of improvement in quality and reductions in cost of productions. It is needed to improve the overall performance of cutting operations to achieve these goals. Milling is one of the most multipurpose machining operations used in industries. In this case, material is removed from a work piece by using a multiple point cutting tool. Milling cutters are available in basic three categories. In plane milling cutter cutting edge is
parallel or inclined to the axis of cutter and in face milling cutter, the axis of cutter is perpendicular to the finished surface. Both these cutters are used for generating the flat surfaces. The third cutter is end milling cutter which is used for creation of slots and profiles. It is a small size face milling cutter. End mills can be used on horizontal milling machine, but it is better to use them on vertical milling machine. They are having diameter from about 3 mm to 50 mm. End mills are those tools which have cutting teeth at one end, as well as on the sides. They are usually made from high speed steel (HSS) or carbide, and have one or more flutes. Experimentation is an important aspect of engineering practices. Most of the engineering system needs to undergo experimentation to gather enough information in order to analyze the performance of that system. If these experiments are designed experiments then, the obtained data can be converted into the empirical models, which can be used for finding out the performance of the system under different situations. The goal of any experimental activity is to get the maximum information about a system with the minimum number of well designed experiments. The Design of experiments is usually carried out by using methods like full factorial design, fractional factorial design, Taguchi design and Response surface design. In full factorial design, one factor is varied at a time and experiments are performed at all levels of all the factors. Thus, large numbers of experiments are conducted and all the interactions are captured. Many times, it is not possible to conduct such large number of experiments due to lack of time and resources needed for the experimentation. The replacements could be in the form of fractional factorial designs in which only certain combinations of the levels of the factors are used foe experimentation. It is not possible to capture all the interactions in fractional designs. Taguchi method is derived from fractional factorial design. Response surface methodology is the most informative method of analysis of the result of a factorial experiment. RSM is collection of mathematical and statistical tools used for modelling and optimization of the response. It is a set of statistical DOE techniques, intrinsic regression modelling, and optimization methods useful for any field of
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International Journal on Emerging Trends in Technology (IJETT) ISSN: 2455 - 0124 (ONLINE) | 2350 - 0808 (PRINT) | (IF: 0.456) Volume 3 | Issue 2 | July - 2016 | Special Issue www.ijett.in
engineering [1]. The first step in RSM is, to find best suited approximation for relating the factors and responses. First and second order models are used in almost all RSM problems. First-order model has linear terms, whereas, Second-order models include quadratic and cross product terms. In this paper, we have reviewed the recent literature for the use of response surface methodology (RSM) for milling operations. The aim of this work was to study and present the recent literature in this field. Initially, the use of RSM for modeling and analysis of surface roughness is presented, followed by cutting temperature, cutting force and other parameters.
2. USE OF RSM FOR MILLING OPERATIONS 2.1 Surface Roughness Manufactured goods superiority and in many cases a technological condition for mechanical stuff [2]. Surface roughness is affected by controlled factors, like feed rate, cutting speed, depth of cut. It is also important to find out the effect of non-controlled factors such as non-homogeneity of the work piece and the tool, tool wear, machine-motion errors, formation of chips, and unpredictable random disturbances [3]. In this section, the work carried out on the roughness analysis and prediction is reviewed. G. Mahesh et al [3], used HSS tool for machining of Al alloy with factors like spindle speed, feed rate, axial depth of cut, and radial depth of cut, radial rake angle. Hybrid method of RSM with Second order model and GA is employed for minimizing the surface roughness. Feed rate was found to be the most influential parameter on Ra. It was noticed that, increase in feed and axial depth of cut increases the roughness. Increase in the spindle speed decreases the surface roughness. Higher surface roughness created by higher and lower rake angles. R. Sudhakaran et al [4] has carried out experiments on AL7075T6 using cutting speed, feed rate, axial depth of cut, and radial rake angle, Nose radius as control parameters and found that, higher cutting speed and a lower cutting feed reduces roughness. At high nose radius decrease in surface roughness is noticed. Roughness increases at low radial rake angle and decreases at high rake angle. Kamguem et al [5], used three different aluminium alloys (6061-T6,7075-T6, 2024-T 351) for investigating the effect of Speed, feed Depth of cut , Tool coatings and work piece materials on Surface finish and particle emissions. Coated carbide TiCN and TiCN + Al2O3 +TiN TiCN coating resulted into better surface finish and low metallic particle emission. The feed rate was identified as the dominant parameter affecting surface finish. Sehgal et al [6], machined Ferritic-Pearlitic ductile Iron using cemented carbide with variations in Spindle speed, feed rate and depth of cut for studying their effect on surface finish. It was noticed that, PSO outperformed RSM as errors of 5.5% and 0.07% for the developed model by RSM and PSO respectively. Jeyakumar et al[7], found that higher depth of cut increases wear rate, cutting force, and surface roughness. Roughness is low at higher speeds and high at lower feed rates. The parameters under study were spindle speed, feed rate, depth of cut , nose radius force and respnce varibles were tool wear and roughness while machining Al6061/SiC. Effect of Spindle speed, depth of cut and feed rate on five different roughness parameters: centre line average roughness, root mean square roughness, skewness, kurtosis and mean line peak spacing while machining of Aluminium, Brass, MS was studied by Routara et al[8]. It is found that, selection of optimized process parameters is possible using these results which will ultimately reduce the machining time. Roughness models
using the RSM and ANN were developed and compared by Erzurumlu et al[9], using speed, feed, axial and radial depth of cut and machining tolerances as parameters for Al alloy. It was found that, ANN model is slightly more accurate than RSM model for surface roughness predictions. S. K Reddy [10], [11]carried out dry and wet machining of AISI 1045, using rake angle, nose radius, speed and feed as control factors and shown that dry machining is beneficial over wet if proper cutting tools and tool geometry are selected. Similarly, the work carried out by[12]–[14] is summarized in Table 1.
2.2 Cutting Temperature According to Sudhakaran et al [15], rake angle is the most important parameter which reduces peak temperature rise of work piece. The temperature rise is found to be least at 4° rake angle. The increase in cutting speed, feed rate and axial depth of cut increases cutting temperature. They have conducted experiments on Al 6351using HSS tools and rake angle, nose radius, cutting speed, feed, and depth of cut as process parameter and measured the work piece temperature rise. A k-type thermocouple was employed to record the rise in temperature. In a similar study by Sudhakaran et al [16], helix angle was found to be the most noteworthy parameter which reduces peak temperature rise. Tamilarasan et al [17], conducted experiments on tool steel using coated carbide tools. The results were analyzed by hybrid method RSM and evolutionary algorithms (Genetic Algorithm and simulated annealing). Solution of SA was found to be better than GA. Regression and fuzzy modeling were also, been used to evaluate the input – output relationships. Patel et al[18] and Kadirgama et al[19] also did similar studies using cutting speed, feed rate and axial depth as control factors. The summary is listed in table 2.
2.3 Cutting Forces Tamilsaran et al [20], used forces, work piece temperature and sound pressure level as response variables while machining 100CrMnW4 tool steel with work material hardness, nose radius, feed rate, axial depth of cut and radial depth of cut as process parameters. Material hardness, feed per tooth and axial depth of cut were identified as main parameters that influence all responses. Dikshit et al [21], used speed, feed, axial and radial depth of cut as control factor while tangential, radial and axial forces as response variables. Axial depth of cut was identified as the leading parameters affecting cutting forces. Radial depth of cut has more influence on axial and radial forces as compared to feed. Sudhakaran et al carried out experiments with cutting Speed, feed, depth of cut for determination of cutting forces. Cutting speed was identified as the dominant factor, followed by feed rate and the axial depth of cut. Table 3 summarizes the use of RSM for cutting forces analysis.
2.4 Vibration
Amplitude Response Variables
and
other
RSM was also used for analysis of parameters like vibrations amplitude. Sudhakaran et al [22], used radial rake angle, nose radius, speed; feed and axial depth of cut as variables to measure vibration amplitude and Feed rate most was noted to be the dominant parameter. Amplitude was
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International Journal on Emerging Trends in Technology (IJETT) ISSN: 2455 - 0124 (ONLINE) | 2350 - 0808 (PRINT) | (IF: 0.456) Volume 3 | Issue 2 | July - 2016 | Special Issue www.ijett.in
lower at a higher cutting speed and a lower cutting feed. In similar study Sudhakaran et al [23], found helix angle as dominant parameter. The details are listed in Table 4. RSM was also utilized for analysis of other parameter like work hardening, residual stresses, dimensional errors, burr height by Fuh [24] et al, Khanghah et al[25]. The details are in Table 5.
3. DISCUSSIONS AND CONCLUSIONS In this paper we have reviewed the recent literature for the use of response surface methodology (RSM) for milling operations. From review, we found that response surface methodology is widely used tool for design of experiments and optimization of process parameters in milling operations. Following conclusions are drawn from the study,
• RSM is found to be a very useful and powerful tool for design of experiments and optimization of process parameters in milling operations. • Many researchers have used surface roughness as response variable and worked on reduction of roughness using various control parameters. Apart from effect of conventional parameters like cutting speed, feed and depth of cut on roughness, effect of parameters like tool geometry (rake angle, tool helix angle, and nose radius), machining tolerances, and tool coatings was also studied by some researchers. • RSM was also successfully employed for the analysis of parameters like vibration amplitude, tool wear, cutting temperatures, cutting forces and burr dimensions etc. • Hybrid analysis combining RSM with evolutionary algorithms like genetic algorithms and simulated annealing was also successfully employed
Table 1 Summary of use of RSM for Surface Roughness Author/ year
Work piece Tool Material Material
Input Variables Response Variable/s
Model Used Remarks and other techniques
G. Mahesh et al Aluminium ,2014 6063
HSS
Spindle speed, Surface feed rate, axial finish depth of cut, and radial depth of cut, radial rake angle
Second order Surface roughness model by RSM model + GA and optimization by GA. Feed rate most influential on Ra. Increase in feed and axial depth of cut increases the roughness. Increase in the spindle speed decreases the surface roughness. Higher surface roughness created by higher and lower rake angles.
M. AL7075-T6 Subramanian, Sakthivel, R. Sudhakaran, 2014
HSS
Cutting speed, Surface feed rate, axial finish depth of cut, and radial rake angle, Nose radius
Second order Higher cutting speed and a lower model. cutting feed reduces roughness. At high nose radius decrease in surface roughness is noticed. Ra increases at low radial rake angle and decreases at high angle.
R. Kamguem, Aluminium Coated Speed, feed 2013 alloys 6061- carbide TiCN Depth of cut , T6,7075-T6, and TiCN+ Tool coatings 2024-T 351) and work piece Al2O3+TiN materials
Sehgal 2013
et
al, FerriticPearlitic Ductile Iron
Cemented carbide
Surface finish , particle emissions
Spindle speed, Surface feed rate and finish depth of cut
Grade 80-5506
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Second order TiCN coating - better surface finish model. and low metallic particle emission. The feed rate is the dominant surface finish.
Second order PSO outperformed RSM as errors model + of 5.5% and 0.07% for the PSO developed model by RSM and PSO respectively.
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S. Jeyakumar et Al6061/SiC al, 2013
Tungsten carbide
spindle speed, force, tool Second order and model. feed rate, depth wear of cut , nose roughness radius
Higher depth of cut increases wear rate, cutting force, and surface roughness. Roughness is low at higher speeds and high at lower feed rates.
Routara et al, Aluminium, 2009 Brass, MS
Coated carbide
Spindle speed, 5 different Second order Claimed that, it is achievable to model. select blend of speed, feed and depth of cut and roughness depth of cut for best finish in terms parameters feed rate of these 5 parameters.
Aluminium Tuncay Erzurumlu et al 7075-T6 , 2007
feed, Surface PVD Al TiN Speed, coated solid axial and radial finish depth of cut, carbide Tolerances.
Fourth order ANN model led to slightly accurate model and surface roughness predictions ANN
S. K Reddy, AISI 1045, Dry Coated Rao, 2005 carbide
Rake angle, nose surface radius, speed, roughness feed
I, II order Speed, feed, radial rake angle and models nose radius are the key factors influencing the surface roughness.
S. K Reddy, AISI Rao, 2005 Wet
Rake angle, nose surface radius, speed, roughness feed
I, II order The benefit of dry machining over models the wet machining can be clearly recognized by selecting proper cutting tools and tool geometry.
Oktem 2005
et
1045, Coated carbide
al, Aluminium (7075-T6)
Ming et al, 2004 AL2014-T6.
Mansour al,2002
et Carbon EN32
PVD AlTiN feed, cutting surface coated solid speed, axial and roughness carbide radial depth of cut and tolerance
fourth order
Not specified Cutting speed, Surface feed, depth of finish cut, concavity and axial relief angles
Second order Better finish- with coolant model. The main factors during dry cut were the cutting speed, feed, concavity and axial relief angles and for the coolant model feed and concavity angle.
Steel Carbide
Cutting speed, surface feed, depth of roughness cut
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model + GA
Optimization methodology proposed in this study by coupling the developed RS model and the developed GA is effective and can be utilized in other machining problems
I, II order Roughness increases with feed and models depth of cut whereas reduces with cutting speed.
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Table 2 Summary of use of RSM for Cutting Temperature Author/ year
Work piece Tool Material Input Material Variables
Sudhakaran et al, 2015
Al
Response Variable/s
Second order Model + Genetic algorithm
Rake angle is the most important parameter which reduces peak temperature rise. The temperature rise is least at 4° rake angle. The increase in cutting speed, feed rate and axial depth of cut increases cutting temperature. The radial depth of cut does not have a significant effect on temperature rise.
Cutting Second order temperature, + GA and SA radial and tool wear and axial depth of MRR cut and speed
Solution of SA is better than GA. Regression and fuzzy modelling were used to evaluate the input – output relationship.
rake angle, temperature nose radius, rise cutting
HSS
6351
Model Used Remarks and other techniques
speed, feed, and depth
Tamilarasan 100MnCrW4 coated et al, 2015 a, (AISI O1) tool (TiN+TiAlN) b steel carbide tool insert
Feed,
Patel et al, M.S. 2014
Carbide
Speed, Feed, Cutting and depth Temperature
II order Depth of cut - most significant model. parameter for temperature.
Sudhakaran et al, 2013
HSS
Helix angle, temperature rise spindle speed, feed, axial and radial depth.
Second order Helix angle is the most noteworthy Model + GA parameter which reduces peak temperature rise. (Smallest between 40° and 45° angles). The increase in speed, feed, axial depth increases temperature.
Carbide
Cutting Cutting speed, feed Temperature rate and axial depth
BoxBehnken, I order model + FEA
Al 6063
K. HASTELLOY Kadirgama et C-22HS al, 2009
Feed rate is the most dominant parameter on the temperature, followed by the axial depth and cutting speed ,
Table 3 Summary of use of RSM for cutting forces Author/ year
Work piece Tool Material Material
Tamilarasan et 100CrMnW4 al, 2014 tool steel
TiN+TiAlN coated WC inserts
Input Variables
Response Variable/s
work material
Forces, work piece hardness, nose , temperature feed rate, axial sound pressure depth of cut, and level. radial depth of cut
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Model Used Remarks and other techniques Box-Behnken Material hardness, feed per design + grey tooth and axial depth of cut are relational main parameters that influence all responses. analysis Increase of nose radius is found to be comparative to increase of cutting forces, temperature and sound.
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Mithilesh K Al2014-T6 Dikshit et al, 2014
Coated solid Speed, feed, axial tangential, radial Second order Axial depth of cut is leading parameters that affect cutting carbide and radial depth and axial forces model. forces. Radial depth of cut has of added influence on axial and cut radial forces as compared to feed.
Sudhakaran et AL7075-T6 al, 2013
HSS
Cutting Speed, Cutting Forces feed Depth of cut
Second order Cutting speed was identified as model. the dominant factor, followed by feed rate and the axial depth of cut.
Table 4 Summary of use of RSM for Vibration Amplitude Tool Input Material Variables
Author/ year
Work piece Material
Sudhakaran et al, 2013
Al7075-T6 HSS
Response Variable/s
radial rake vibration angle, nose amplitude radius, speed, feed and axial depth of cut
Sudhakaran et al , 2010
Al 6063
HSS
Helix angle of vibration cutting tool, amplitude spindle Speed, feed rate, and axial and radial depth of cut.
Model Used Remarks and other techniques Second order Feed rate most dominant parameter. model. Amplitude is lower at a higher cutting speed and a lower cutting feed. A increase of vibration amplitude at low nose radius. Amplitude increases at low radial rake angle..
Second order The helix angle is the most important model. parameter and helix angle up to 45°, the vibrations in the machine are nearly narrowed. The increase in feed rate reduces the vibrations. The middle level of the speed, axial depth and radial depth reduces vibrations.
Table 5 Summary of use of RSM for other Parameters Author/ year
Work piece Tool Material Material
Input Variables Response Variable/s
Model Used Remarks and Techniques
Fuh et al 5 different high speed 1997 aluminium steel alloy plates, end mill
Hardness, speed, Dimensional Feed, Radial error and Axial depth of cut
Second model.
order Dimensional accuracy is reduced with higher values of work piece hardness, feed and radial and axial depths of cut, but increased with increased values of cutting speed
Khanghah et al, 2015
cutting speed, burr height and Second feed rate and burr thickness model. depth of cut
order Feed, spindle speed and depth of cut are major factors having great effect on the burr height and thickness.
316 stainless tungsten steel carbide tools
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