Peer Reviewed and Refereed Journal
VOLUME: 07, Issue 07, Paper id-IJIERM-VII-VII, September 2020
50 PARAMETRIC OPTIMIZATION TO REDUCE SURFACE ROUGHNESS OFHIGH SPEED TURNING
1Vinay Singh Lodhi, Research Scholar,
2Prof. Pragyan Jain,
Department of Mechanical Engineering,
Gyan Ganga Institute of Technology and Sciences, Jabalpur, (M.P.)
Abstract:- In the present work the study on machining of Duplex 2205 stainless steel using cubic boron nitride (CBN) tool insert under dry conditions will be conducted. Experiments will be performed on CNC turning machine to obtain the data for the training and testing of the models. The machining parameters will be considered the speed, feed and depth of cut of the turning process for predicting the surface quality. General full factorial design will be selected for experiment trials and ANOVA analysis by using Minitab to obtain the results and to check the effecting parameters in machining. JAY Algorithm in MATLAB and response surface methodology (RSM) with the help of Minitab 17 will be used for predicting the surface roughness (Ra).
Keywords:- Duplex 2205 stainless steel, CBN Insert, cutting speed, Feed and Depth of Cut Turning machine, Surface roughness, Factorial design, Response surface methodology, JAYA Algorithm, Minitab.
1. INTRODUCTION
The recent developments in science and technology have put tremendous pressure on manufacturing industries. The manufacturing industries are trying to decrease the cutting costs, increase the quality of the machined parts and to machine more difficult materials.
Machining efficiency is improved by reducing the machine time with high speed machining. When cutting ferrous and hard to machine material such as steel, cast iron and super alloys, softening temperature and the chemical stability of the tool material limits the cutting speed.
The most common metal cutting processes are milling, drilling, turning, and grinding.
Surface roughness is an important physical quantity which can influence the mechanical properties of materials, lubrication properties and fatigue strength. In most cases, it is a technical requirement for mechanical products and an index of performance. Hence, how to achieve the desired surface quality using the available cutting conditions is an important issue needed to be addressed in a manufacturing process. Various kinds of researches have been conducted to find the reasonable cutting parameters for obtaining a high-quality surface roughness.
Taking a developed prediction model for Ra as objective function in an optimization problem, using the cutting
parameters as decision variables, and then prediction with RSM & JAYA algorithm is expected to be a good way to obtain the optimum cutting parameters for a desired surface. The critical step to solve this optimization problem is to establish a high-accuracy prediction model for Ra as the objective function.
The forming mechanism of surface roughness is very complicated and process- dependent. Various machining theory studies of surface roughness have been conducted in terms of kinematics and cutting tool properties.
Based on these theories, different theoretical models for surface roughness have been built. A well-known theoretical prediction model for Ra was calculated as Ra = f2 / (32*r) in a single point turning operation, where f is the feed rate and r is the corner radius. This model was only correlated with the feed rate and corner radius, ignoring other surface-altering effects which can result in a deviation from the actual result. Major sources causing this deviation are mainly related with plastic deformation, post-machining elastic recovery, adhesion at the rake-chip interface, tool wear as well as the relative vibrations between the tool and work- piece.
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VOLUME: 07, Issue 07, Paper id-IJIERM-VII-VII, September 2020
51 1.1 Turning ProcessFigure 1.1 Turning process Turning is a machining process in which a cutting tool, typically a non-rotary tool bit, describes a helix tool path by moving more or less linearly while the work piece rotates. The tool's axes of movement may be literally a straight line, or they may be along some set of curves or angles, but they are essentially linear. Usually the term turning is reserved for the generation of external surfaces by this cutting action, whereas this same essential cutting action when applied to internal surfaces (that is, holes, of one kind or another) is called boring.
Turning can be done manually, in a traditional form of lathe, which frequently requires continuous supervision by the operator, or by using an automated lathe which does not. Today the most common type of such automation is computer numerical control, better known as CNC. When turning, a piece of relatively rigid material is rotated and a cutting tool is traversed along 1, 2, or 3 axes of motion to produce precise diameters and depths.
Turning can be either on the outside of the cylinder or on the inside (also known as boring) to produce tubular components to various geometries. The turning processes are typically carried out on a lathe, considered to be the oldest machine tools, and can be of four different types such as straight turning, taper turning, profiling or external grooving. Those types of turning processes can produce various shapes of materials such as straight, conical, curved, or grooved work piece. In general, turning uses simple single-point cutting tools.
2. LITERATURE REVIEW
In this chapter, previous work done by various researchers is discussed.
Vikas Upadhyay et al. [1]
developed the relationship between cutting and process parameters during high-speed turning of titanium (Ti-6Al-4V) alloy considering the input parameters of the ANN model are the cutting parameters: speed, feed rate, depth of cut.
The output parameter model with three process parameters measured during the machining trials, namely acceleration amplitude in axial (Vx), radial (Vy) and tangential (Vz) direction respectively. An attempt has been made to use vibration signals for the prediction of Surface roughness during turning of titanium (Ti–
6Al–4V) alloy. To develop a neural network model (ANN), feed rate, depth of cut, acceleration amplitude of vibration in radial, axial and tangential direction were taken as input parameters.
N.E. Karkalos et al. [2] selected Depth of cut, cutting speed and feed rate as input parameters and measured surface roughness after each milling experiment of titanium (Ti-6Al- 4V) alloy performed. Response Surface methodology was used to establish a quadratic relationship. Variance analysis was conducted for the evaluation of the effect of input parameters on output parameter. Then RSM was used for the optimization analysis for determination of milling cutting parameters to minimize the surface roughness. To develop a neural network model (ANN), feed rate, depth of cut, cutting was taken as input parameters.
J. Nithyanandam et al. [3] selected Depth of cut, cutting speed and feed rate as input parameters and measured surface roughness after turning experiment of titanium (Ti-6Al-4V) alloy performed. Tool insert used for performing the experiments was coated carbide inserts and Taguchi’s orthogonal array was used for conducting the experiments.
Optimal cutting parameters for better surface roughness is then determined using S/N ratio. Analysis of variance was evaluated to study the influence of parameters on output variable.
Gaurav Bartarya et al. [4]
developed a force prediction model during hard turning of EN31 steel hardened to
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VOLUME: 07, Issue 07, Paper id-IJIERM-VII-VII, September 2020
52 60±2 HRC using hone edge uncoated CBNtool. Gaurav developed the regression models to show the dependence of the cutting, radial and axial forces and surface roughness on machining parameters are significant. The predictions from the developed models were compared with the force and surface roughness. Analysis of variance was evaluated to study the effect of input parameters on output parameter.
B. Anuja Beatrice, et al. [5]
developed a model which based on Artificial Neural Network to simulate hard turning of AISI H13 steel with minimal cutting fluid application. Tool insert with a specification SNMG 120408 was selected to perform the experiments. The ANN model was expected to predict the surface roughness in terms of cutting parameters. DOC, cutting speed and feed were taken as input parameters. A taguchi technique with 3 factors, 3 levels was used to design a 27 experiments.
Surface roughness (Ra) was measured using Mitutoyo’s (SJ-210) portable surface roughness tester during each experiment.
Neelesh Sahu, et al. [6] has developed the relationship between the turning process parameter and surface roughness by using response surface methodology (RSM) with centre composite design (CCD). Cutting speed, depth of cut, feed were taken as input process parameters. Titanium Alloy (Ti-6Al-4V) were taken as workpiece material whereas tool insert used for cutting was CVD coated carbide. A developed advanced optimization algorithm named as Teaching Learning Based Optimization (TLBO) is used for parameter optimization of the equation that developed by RSM.
Analysis of variance is the used to evaluate the significant and non- significant parameters in the RSM model.
Tugrul Ozel, et al. [7] used the artificial neural network to predict the surface roughness and tool flank wear over the input machining parameters in turning using cubic boron nitride (CBN) tool. The workpiece material used for performing the experiments is AISI 52100 steel. Regression technique was used to develop the model of surface roughness and tool flank wear. Artificial neural network was then used to train the experimental data for obtaining the
suitable neural network models. ANOVA is then used to show the interaction between hardness and edge geometry, hardness and feed rate.
Tongchao Ding et al. [8]
investigated the effects of cutting parameters on cutting forces and surface roughness in milling of AISI H13 steel with coated carbide tools. Three cutting force components and roughness of machined surface were measured and then range analysis and analysis of variance (ANOVA) are performed. It is found that the axial depth of cut and the feed are the two dominant factors affecting the cutting forces.
Neeraj Agarwal et al. [9] optimized Surface roughness using Jaya algorithm.
To optimize surface roughness peak current (Ip) and pulse on time (Ton) should have minimum value where Ip=4 A, Ton= 50 µs. Duty factor (t) should be 29.5455 %, voltage (V) have 52.1212 and minimum surface roughness (Ra) achieved 2.7199 µm. Jaya algorithm took only 35 iterations to achieve the optimum solution. Jaya algorithm is best suitable for an engineering optimization problem.
J.-O. Nilsson et. al. presented an overview of duplex stainless steels (DSS) with particular emphasis on super DSS, i.e. steels containing sufficient amounts of chromium, molybdenum, and nitrogen to produce a pitting resistance equivalent greater than 40. Duplex stainless steels have an attractive combination of mechanical and corrosion properties and are thus suitable for many marine and petrochemical applications, particularly where chlorides are present.
3. DESIGN OF EXPERIMENT 3.1 Methodology
In order to complete the laid down objectives, the following methodological steps have adopted for conducting the project work.
1. Select control factors and factor levels and design sequence of experiments.
2. Select response variables and prepare setup for their measurement.
3. Select and procure work pieces and toolings.
4. Set up toolings and create machining program on CNC
Peer Reviewed and Refereed Journal
VOLUME: 07, Issue 07, Paper id-IJIERM-VII-VII, September 2020
53 turning machine5. Conduct turning experiments as per designed sequence and measure the responses.
6. Carry out statistical analysis of the responses.
7. Optimize the process parameters by using RSM & JAYA Algorithm.
8. Validate the results obtained from data analysis.
9. Suggest best model of surface roughness prediction.
Design of experiment is the techniques that enable designer to determine simultaneously the individual and interactive effect of many factors that could affect the output result. The term experiment is defined as the systematic procedure carried out under controlled conditions in order to discover an unknown effect, to test or establish a hypothesis, or to illustrate a known effect.
When analyzing a process, experiments are often used to evaluate which process inputs have a significant impact on the process output, and what the target level of those inputs should be to achieve a desired result (output).
For performing Design of Experiment knowledge of statistics, review the histogram, statistical process control, and regression and correlation analysis is needed. Design of Experiment is used for comparing alternatives, reducing variability, minimizing, maximizing, or targeting an output, identifying the significant inputs achieving an optimal process output.
In order to establish an adequate functional relationship between the responses (such as surface roughness) and the cutting parameters (cutting speed, feed, and depth of cut), a large number of tests are needed, requiring a separate set of tests for each and every combination of cutting tool and work piece material. Out of various approaches I have selected factorial design methodology techniques and General full factorial design for obtaining experimental sequence.
3.2 Full Factorial Designs
In a full factorial experiment, responses are measured at all combinations of the experimental factor levels. The combinations of factor levels represent the
conditions at which responses will be measured. Each experimental condition is a called a "run" and the response measurement an observation. The entire set of runs is the "design. The following diagrams show two and three factor designs. The points represent a unique combination of factor levels. For example, in the two-factor design, the point on the lower left corner represents the experimental run when Factor A is set at its low level and Factor B is also set at its low level.
Figure 3.1 Factorial Design Process 3.3 Two-level full factorial designs In a two-level full factorial design, each experimental factor has only two levels.
The experimental runs include all combinations of these factor levels.
Although two-level factorial designs are unable to explore fully a wide region in the factor space, they provide useful information for relatively few runs per factor. Because two-level factorials can indicate major trends, you can use them to provide direction for further experimentation. For example, when you need to further Explores a region where you believe optimal settings may exist, you can augment a factorial design to form a central composite design.
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VOLUME: 07, Issue 07, Paper id-IJIERM-VII-VII, September 2020
54 3.4 General full factorial designsIn a general full factorial design, the experimental factors can have any number levels. For example, Factor A may have two levels, Factor B may have three levels, and Factor C may have five levels.
The experimental runs include all combinations of these factor levels.
General full factorial designs may be used with small screening experiments, or in
optimization experiments.
3.5 Design of Experimental Runs Using General Full Factorial
Table 3.1 gives the selection of factor levels and values for design of turning machining experiments using full factorial design methodology.
Table 3.1 Selection of Control Factors and Factor Levels S. No. Factors Nomenclature Low Medium High
1 Cutting Speed (rpm) Speed 900 1200 1500
2 Feed (mm/rev) Feed 72 96 120
3 Depth of cut (mm) DOC 1 1.5 2
4. EXPERIMENTAL SETUP AND EXPERIMENTATION
The following sub-sections highlight the specifications of the machine, cutting tool, work piece, process parameters for machining, sensors, measuring instruments and experimental procedure.
4.1 CNC Lathe Machine
In the present study MTAB MAXTURN+
model of CNC lathe machine used for the experimentation as shown in Figure 4.1.
The CNC machine is installed in the CNC laboratory of the Mechanical Workshop, Department of Mechanical Engineering, PDPM IIITDM Jabalpur and is equipped
with a Siemen’s 828D controller for precise turret motion and spindle speed control. The machine can be operated in manual, semi- automatic or fully automatic modes. It has ±0.005 mm position accuracy and ±0.004 mm repeatability during the cutting process when it is operated with a NC program.
Figure 4.1 shows the CNC used for experimentation. CNC turning centre having a spindle power of 5.5 kW was used for experimentation. This study employed the dry turning with hardware setup that includes a CBN insert tool, a befitted shank, and the tool holder for the CBN insert tool.
Figure 4.1 MAXTURN PLUS CNC lathe machine
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VOLUME: 07, Issue 07, Paper id-IJIERM-VII-VII, September 2020
55 4.2 Work pieceTable 4.1 Material Composition of Duplex steel Element C Cr Ni MO N Mn Si
Weight
%
0.3 % 21 - 23
%
4.5 - 6.5 %
2.5 - 3.5 %
0.16% 2% 1%
Table 4.2 Physical properties of Duplex steel Density,
g/cm3 Melting Range,
°C±15°C
Specific Heat, J/kg.°C
Thermal Conductivity, W/m.K
Elastic Modulus, Gpa
Hardness
Brinell HB Tensile Strength, Mpa
7.9 1649 500 16.3 193 270[-] 650-880
Work piece used in the experiment is shown in Error! Reference source not found. 2. The diameter of the work piece was 25 mm and total length was 100 mm. The cutting length was 20 mm for each experiment.
Figure 4.2 Work piece 4.3 Machining Parameters
Appropriate parameters are recommended by the machine tool manufacturer to ensure the quality of the product and the life of the machine. Cutting harder materials requires more power and generates more heat in the nose (hotspot) of the cutting tools. The cutting conditions are the combination of spindle speed, feed rate, and depth of cut during machining.
Since each parameter has a limited range, based on the specifications
of the machine, a proper combination of the parameters has to be chosen before cutting. Selecting the appropriate cutting conditions also involves considering the life of the tool. Therefore, optimizing the performance and life of the tool requires proper set up of the cutting conditions. In this study, range of spindle speed, feed rate, and depth of cut were chosen based on literature review and range obtained from the tool manufacturer. Table 4.3 shows the parameters and their ranges.
Table 4.3 Process Parameters and their ranges Speed (rpm) Feed (mm/rev) Doc (mm)
900-1500 72-120 1-2
4.4 Measuring Instruments
Measuring instrument used in this study were dynamometer and accelerometer to measure cutting forces and acceleration respectively during the cutting process.
Surface roughness tester is used to measure surface roughness of machined work piece.
5. STATISTICAL ANALYSIS
It is apparent that in order for an accurate interpretation of the information produced to be provided, a high level of
data processing and analysis is required.
Functional signal features correlated with surface roughness and process conditions are generated using a number of methods. These features, sometimes coupled with experimental results, are finally fed to and evaluated by cognitive decision-making support systems for the final diagnosis. This can be communicated to the human operator or fed to the machine tool numerical controller in order to suggest or execute appropriate adaptive or corrective actions.
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56 There are different methods of signalanalysis as described below.
5.1 Different Methods of Statistical Analysis
Methods for correlating the measured process parameters to surface roughness fall primarily into two categories. The first category consists of methods those make use of an analytical models such as statistical regression analysis, response surface methodology, taguchi methods etc. The second category is one of example based models with inductive learning capabilities such as pattern recognition, decision surfaces, mapping techniques, clustering and artificial intelligence models (neural networks, genetic algorithms and knowledge based
systems). In present study, signals are analysed by the method of statistical analysis and artificial intelligence approach. Statistical regression analysis and Response surface methodology is used for statistical analysis.
5.2 Modeling of Responses Using Statistical Regression Analysis and Response Surface Methodology
As an important subject in the statistical design of experiments, the Response Surface Methodology (RSM) is a collection of mathematical and statistical techniques useful for the modeling and analysis of problems in which a response of interest is influenced by several variables.
Figure 5.1 3D Response Surface along with Contour Plot of the Response.
The method of least squares is used to estimate the parameters in the approximating polynomials. The response surface analysis is then performed using the fitted surface. The model parameters can be estimated most effectively if proper experimental designs are used to collect the data.
6. RESULTS AND DISCUSSIONS 6.1 Validation of Generated Models In order to validate the results obtained, turning experiment was carried out by using the five reserved values of speed, feed and depth of cut from table and the obtained responses were recorded. After experimentation surface roughness is measured by surface roughness tester for validation and it is given by,
Table 6.1 Surface Roughness Validation Speed,
s
Feed, f Doc, D F A (Exp.) 1300 0.15 0.95 318.306 2.3968 2.7058 1300 0.19 0.75 379.799 2.0417 2.3055 1800 0.19 0.55 413.11 2.168 2.2137
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57 2300 0.11 0.75 219.394 4.1603 1.97342300 0.15 0.55 334.611 3.7849 1.6222 6.2 Error Between Experimental and Predicted Surface Roughness
In this section I have calculated the percentage error between the predicted and experimental values of surface roughness using the formula
6.3 Validation of Regression Model for Surface Roughness
Table 6.2 Surface Roughness Validation by Regression Method Speed Feed Doc F A (Regression) (Exp.) %Error 1300 0.15 0.95 318.306 2.3968 2.3985 2.7058 -12.8121 1300 0.19 0.75 379.799 2.0417 2.7005 2.3055 14.6305 1800 0.19 0.55 413.11 2.168 2.4684 2.2137 10.3215 2300 0.11 0.75 219.394 4.1603 1.8157 1.9734 -8.6853 2300 0.15 0.55 334.611 3.7849 1.7649 1.6222 8.0854 1. Regression Model of Ra: predicts
the surface roughness by using process parameters and Resultant Force, acceleration as an input parameter. As a model is having % error of 14.63 in prediction of
surface roughness, it is not as much significant for surface roughness prediction. Thus, conclusion is made that only regression analysis does not give better result.
6.4 Validation of RSM Model for Surface Roughness
Table 6.3 Surface Roughness Validation by RSM
Speed Feed Doc F A (RSM) (Exp.) %Error 1300 0.15 0.95 318.306 2.3968 2.425 2.7058 -11.5793 1300 0.19 0.75 379.799 2.0417 2.5638 2.3055 10.0748 1800 0.19 0.55 413.11 2.168 1.9769 2.2137 -11.4147 2300 0.11 0.75 219.394 4.1603 1.8290 1.9734 -7.8950 2300 0.15 0.55 334.611 3.7849 1.8031 1.6222 10.0327 2. RSM Model of Ra: predicts the
surface roughness by using process parameters and Resultant Force, acceleration as an input parameter. As a model is having % error of 11.57 in prediction of surface roughness, it is a significant model for prediction of surface roughness compared to regression model.
7. CONCLUSIONS
The following concluding remarks can be drawn from the activities conducted in the project:-
1. Design of experiments is a very structured methodology for
planning and designing a sequence of experiments.
2. Regression Model of Ra: predicts the surface roughness by using process parameters As a model is having % error of 14.63 in prediction of surface roughness, it is not as much significant for surface roughness prediction.
Thus, conclusion is made that only regression analysis does not give better result.
3. RSM Model of Ra: predicts the surface roughness by using process parameters. As a model is having % error of 11.57 in prediction of surface roughness, it
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VOLUME: 07, Issue 07, Paper id-IJIERM-VII-VII, September 2020
58 is a significant model forprediction of surface roughness compared to regression model.
4. JAYA Algorithm Prediction of Ra:
predicts the surface roughness by using process parameters. As % error of model is 9.028, which is least among all the models generated in this study. Hence this method can be used for prediction of surface roughness.
From above models it can be concluded that considering all the parameter for prediction of surface roughness gives better prediction than considering only process parameter and it can also be concluded that JAYA gives better prediction over RSM and linear regression models.
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