Surface Roughness Prediction Model for Ball End Milling Operation Using Artifici

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Department of Industrial and Production Engineering Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh.

* Corresponding author.

Received 3 January 2012; accepted 11 May 2012

Abstract

surface roughness is an index which determines the quality of machined products. In this study the average surface roughness value (Ra) for Aluminum after ball end milling operation has been measured. 84 experiments have been conducted varying cutter axis inclination angle (φ degree), spindle speed (S rpm), feed rate (fy mm/min), radial depth of cut (fx mm) and axial depth of cut (t mm) in order to find Ra. This data has been divided into two sets on a random basis. 68 data sets have been used for training different ANFIS model for Ra prediction. 16 test data sets have been used to validate the models. Better ANFIS model has been selected based on the minimum value of root mean square error (RMSE) which is constructed with three Gaussian membership functions (gaussMF) for each inputs and a linear membership function for output. The Selected ANFIS model has been compared with theoretical model and Response Surface Methodology (RSM). This comparison is done based on RMSE and mean absolute percentage error (MAPE). The comparison shows that the selected ANFIS model gives better result for training and testing data. Here ANFIS model has been used further for predicting surface roughness of a typical die made by ball end milling operation. An algorithm was developed to determine the feasible solutions for the cutting parameters in order to obtain a desired surface roughness for the three dimensional die. This algorithm was used to show how a near optimal combination of machining parameters can be determined for a targeted level of surface finish.

Key words: Aluminium; Surface; Manufacturing; Quality; Milling; ANFIS; Machining; AI

Shahriar Jahan Hossain, Nafis Ahmad (2012). Surface Roughness Prediction Model for Ball End Milling Operation using artificial Intelligence. Management Science and Engineering, 6(2), -0. Available from: URL: /index.php/mse/article/view/j.mse.1913035X20120602.2933

DOI: /10.3968/j.mse.1913035X20120602.2933

INTRODUCTION

The main objective of today’s manufacturing industries is to produce low cost, high quality products in short time. The selection of optimal cutting parameters is a very important issue for every machining process in order to enhance the quality of machining products and reduce the machining costs (Cus & Zuperl, 2009). It is expected that the next decade machine tools will be intelligent machines with various capabilities such as prediction of self set up required parameters to reach to the best surface qualities. Typically, surface inspection is carried out by manually inspecting the machined surfaces. As it is a post-process operation, it becomes both time-consuming and labor-intensive. In addition, a number of defective parts can be found during the period of surface inspection, which leads to additional production cost (Aykut, 2011). Milling process is one of the common metals cutting operations and especially used for making complex shapes and finishing of machined parts. The quality of the surface plays a very important role in the performance of the milling as a good quality milled surface significantly improves fatigue strength, corrosion resistance or creep life. Surface roughness also affects several functional attributes of parts, such as contact causing surface friction, wearing, heat transmission, light reflection, ability of distributing and holding a lubricant, load bearing capacity, coating or resisting fatigue. Therefore the desired finish surface is usually specified and the appropriate processes are selected to reach the desired surface quality (Lou, et al., 1999).

Unlike turning, face milling or flat end milling operations, predicting surface roughness for ball end milling by mathematical models is very difficult. In recent years, the trends are towards modeling of machining processes using artificial intelligence due to their advanced computing capability. Researchers have used various intelligent techniques, including neural network, fuzzy logic, neuro-fuzzy, ANFIS, etc. for prediction of machining parameters and enhancing manufacturing automation. Artificial Neural Network (ANN) and Fuzzy Logic are two important tools of artificial intelligence in modeling nonlinear problems. A neural network can learn from data and feedback, however understanding the knowledge or the pattern learned by it is difficult. But fuzzy logic models are easy to comprehend because they use linguistic terms in the form of IF-THEN rules. A neural network with their learning capabilities can be used to learn the fuzzy decision rules, thus it creates a hybrid intelligent system.

In the present work the adaptive neuro-fuzzy model has been developed for the prediction of surface roughness. The predicted and measured values are fairly close to each other. The developed model can be effectively used to predict the surface roughness in the machining of aluminum within the ranges of variables studied. The ANFIS results are compared with the RSM results and results from theoretical equations. Comparison of results shows that the ANFIS results are superior to others. This study attempts to design Adaptive Network-based Fuzzy Interface System (ANFIS) for modeling and predicting surface roughness in ball end milling of Aluminium as a die material; and developing an algorithm to select the cutting parameters for a desired level of surface roughness.

1. LITERATURE REVIEW

The quality of surface finish mainly depends on the interaction between the workpiece, cutting tool and the machining system. Due to the above reasons, there have been a series of attempts by researchers to develop efficient prediction model for surface roughness before machining. Survey on previous surface roughness research reveals that most of the researches proposed multiple regression method to predict surface roughness. Some research applied neural network, fuzzy logic, and neural-fuzzy approaches. Optimization of surface roughness prediction model, developed by multiple regression method, with a genetic algorithm is presented in some journals. Among them statistical (multiple regression analysis) and artificial neural network (ANN) based modeling are commonly used by researchers.

For the prediction of surface roughness, a feed forward ANN was used for face milling of high chromium steel (AISI H11) by Rai et al. (2010) and AISI 420 B stainless steel by Bruni et al. (2008). Bruni et al. proposed analytical and artificial neural network models. Yazdi and Khorram (2010) worked for selection of optimal machining parameters (i.e., spindle speed, depth of cut and feed rate) for face milling operations in order to minimize the surface roughness and to maximize the material removal rate using Response Surface Methodology (RSM) and Perceptron neural network. In 2009, Patricia Munoz-Escalona et al. (2009) proposed the radial basisfeed forward Neural Network model and generalized regression for surface roughness prediction for face milling of Al 7075-T735. The Pearson correlation coefficients were also calculated to analyze the correlation between the five inputs (cutting speed, feed per tooth, axial depth of cut, chip’s width, and chip’s thickness) with surface roughness. Zhanjie et al. (2007) used radial basis Function Network to predict surface roughness and compared with measured values and the result from regression analysis. Chen Lu and Jean-Philippe Costes (Lu & Costes, 2008) considered three variables i.e., cutting speed, depth of cut and feed rate to predict the surface profile in turning process using Radial Basis Function (RBF). Experiments have been carried out by Brecher et al. (2011) after end milling of steel C45 in order to obtain the roughness data and model ANN for surface roughness predictions. Seref Aykut (Aykut, 2011) had also used ANN to predict the surface roughness of cast-polyamide material after milling operation. Khorasani et al. (2011) have conducted study to discover the role of machining parameters like cutting speed, feed rate and depth of cut in tool life prediction in end milling operations onAl 7075 by using multi layer perceptron neural networks and Taguchi design of experiment. The determination of best cutting parameters leading to a minimum surface roughness in end milling mold surfaces used in biomedical applications was done by Oktem et al. (2006). For their research, they coupled a neural network and a genetic algorithm (GA) providing good results to solve the optimization of the problem. Jesuthanam et al. (2007) proposed the development of a novel hybrid neural network trained with GA and particle swarm optimization for the prediction of surface roughness. The experiments were carried out for end milling operations. Tsai, et al. (1999) used in process surface recognition system based on neural networks in end milling operation.

Mahdavinejad, et al. (2009), Shibendu Shekhar Roy (2005) and Jiao, et al. (2004) used combination of adaptive neural fuzzy intelligent system to predict the surface roughness machined in turning process. Jiao, et al. (2004) also used adaptive fuzzy-neural networks to model machining process especially surface roughness. Shibendu Shekhar Roy (2006) and Chen & Savage (2001) designed Adaptive Network-based Fuzzy Inference System (ANFIS) for modeling and predicting the surface roughness in end milling operation. Shibendu Shekhar Roy (2006) used two different membership functions (triangular and bell shaped) during the hybrid-training process of ANFIS in order to compare the prediction accuracy of surface roughness by the two membership functions. The predicted surface roughness values obtained from ANFIS were compared with experimental data and multiple regression analysis. The comparison indicated that the adoption of both membership functions in ANFIS achieved better accuracy than multiple regression models. Dweiri, et al. (2003) used neural-fuzzy system to model surface roughness of Alumic-79 workpiece in CNC down milling. Reddy, et al. (2009) also used ANFIS to prediction surface roughness of aluminum alloys but for turning operation. The Response Surface Methodology (RSM) was also applied to model the same data. The ANFIS results are compared with the RSM results and comparison showed that the ANFIS results are superior to the RSM results. Kumanan, et al. (2008) proposed the application of two different hybrid intelligent techniques, adaptive neuro fuzzy inference system (ANFIS) and radial basis function neural network- fuzzy logic (RBFNN-FL) for the prediction of surface roughness in end milling. A neural fuzzy system was used to predict surface roughness in milling operations by. Cabrera, et al. (2011) investigated the process parameters including cutting speed, feed rate and depth of cut in order to develop a fuzzy rule-based model to predict the surface roughness in dry turning of reinforced PEEK with 30% of carbon fibers using TiN-coated cutting tools.

Some other prediction models like Response Surface Methodology (RSM), statistical methods Multiple Regression etc. have been used in a wide range of literatures. Wang and Chang (2004) analyzed the influence of cutting condition and tool geometry on surface roughness using RSM when slot end milling AL2014-T6. Mathematical polynomial models using RSM for surface roughness prediction in terms of cutting speed, feed and axial depth of cut for end milling was developed by Alauddin, et al. (1995) for 190 BHN steel and by Lou et al. (1999) for end milling of EN32. Ozcelik and Bayramoglu (2006) present the development of a statistical model for surface roughness estimation in a high-speed flat end milling process under wet cutting conditions.

To achieve the desired surface finish, a good predictive model is required for stable machining. From the literature review, it was observed that majority of the work in the area of Artificial Intelligence application has been for turning and flat end or face milling operation. Due to this fact and also considering the importance of ball end milling operation for machining of Aluminum, which is widely used in applications like structural, cryogenic, food processing, oil and gas process industries, low temperature die manufacturing etc, the ANFIS and RSM model are developed in this research. This helps the manufacturing industry in predicting the desired surface roughness selecting the right combination of cutting parameters.

2. METHODOLOGY

2.1 Experimental Setup and Design of Experiment

The experiment was performed by using a vertical milling machine shown in Figure 1(a). The work-piece tested was an Aluminum plate of size 9cm×1cm×4cm. A two-flute tungsten carbide coated ball end mill cutter of 8mm diameter was selected as the cutting tool. Some samples were machined with various input parameters and then the experimental data was used to create fuzzy rules and their processing via neural networks. Then the results of this model were compared with the real surface roughness. A total of 84 experiments were planned and carried out. The design of experiments was carried out considering parameter variations around the cutting tool provider recommendations and the machine tool capabilities. In order to measure the average surface roughness (Ra) value, experiments were carried out by varying the cutter axis inclination angle (φ) spindle speed (S rpm), the feed rate along y-axis (fy mm/min), radial depth of cut namely feed along x-axis (fx mm) and the depth of cut (t). The cutter movement directions have been shown in Figure 1(b). For each of the experiments, three sample readings were taken and their average value was considered.

(a) Vertical Milling Machine Used It the Study

(b) Cutting Tool Movement Direction

Figure 1

Experimental Setup

In this study, a Taylor Hobson Talysurf (Surtronic 25) was used for measuring average roughness (Ra). The distance that the stylus travels is sampling length; it ranges from 0.25 mm to 25 mm for selected instrument. In this study sampling length was 8 mm.

2.2 Prediction Models

Adaptive neuro-fuzzy inference system (ANFIS) is a fuzzy inference system implemented in the framework of an adaptive neural network. By using a hybrid learning procedure, ANFIS can construct an input-output mapping based on both human-knowledge as fuzzy if-then rules and approximate membership functions from the stipulated input-output data pairs for neural network training. A hybrid method consists of backpropagation for the parameters associated with the input membership and least squares estimation for the parameters associated with the output membership functions. As a result, the training error decreases, at least locally, throughout the learning process. The training process continues till the desired number of training steps (epochs) or the desired root mean square error (RMSE) between the desired and the generated output is achieved. In this study a hybrid learning algorithm was used to identify premise and consequent parameters of first order Takagi-Sugeno type fuzzy system for predicting surface roughness in ball end milling.

In the Figure 2, a representative element of the ideal roughness profile after ball end milling operation has been shown. Using equation, (1) to (7) the theoretical values of Ra can be calculated. The theoretical Ra depends on feed fx and tool nose radius R. Here“a” is the mean line height. Ab Area below mean line and Aa is the Area above mean line.

Figure 2

Calculation of Mean Line and Roughness

The representative element with length “f” of the curve or surface profile is symmetric with respect to z-axis and surface profile with length f=fx/2 is repeated over the whole surface for gradual feed of fx in each pass. In this study RSM was used to fit linear and second order polynomials on experimental data with 95% confidence level by minitab software.

There are several correlation techniques. But the most common one is the Pearson product-moment correlation coefficient. The correlation r between two variables is expressed as equation (8).

Where n is the number of observations in the sample, xi is the x value for observation i, is the sample mean of x, yi is the y value for observation i, is the sample mean of y, Sx is the sample standard deviation of x, and Sy is the sample standard deviation of y.

3. RESULTS AND DISCUSSION

The ANFIS models have been developed as a function of machining parameters using 68 training data presented in Table 1. The fuzzy logic toolbox of MATLAB 7.6 was used to train the ANFIS and obtain the results. Different ANFIS parameters were tested as training parameters in order to achieve the perfect training and the maximum prediction accuracy. Table 2 shows 48 different architectures of ANFIS. From Table 2 the best-responding model of neuro-fuzzy system were those which have three Gaussian curve built-in membership functions (gaussMF) for each inputs and a linear output function. It can be observed that the predicted error (RMSE) for the training data is 9.9854×10-5 and for the test data it is 1.146. The 5 inputs and 1 output and their final fuzzy membership functions are shown in Figure 3(a). A total of 243 fuzzy rules were used to build the fuzzy inference system.

Figure 3

(a) Final ANFIS Model with 5 Inputs and Loutput

(b) Comparison Between the Experimental and Predicted Values by the ANFIS for Testing Data Set

Three Gaussian membership functions (gaussMF) were used for each input. The model developed by ANFIS was tested using the testing data and the predicted results were presented in Table 3. 16 sets of data were used for test the model. The predicted surface roughness values with the actual experimental values of surface roughness were plotted and compared in Figure 3(b).

Equation (9) is the response surface equation developed by RSM. It can be used for predicting surface roughness. Test data set has been used for verifying this equation and predicted results have been summarized in Table 3. The results using the theoretical equations (1) to (7) for 16 test data sets also have been listed in Table 3.

Ra = 1.35355 + 0.0874799 φ + 0.000887986 S - 0.101501 fy + 7.92503 fx - 6.14303 t - 0.00320667 φ2 - 1.20701×10-07 s2 + 0.00122325 fy2 + 9.91836 fx2 + 10.5552 t2 + 8.53234×10-06φS - 9.68995×10-04φfy+ 0.1357 φfx+ 0.00848098 φt+ 3.41726×10-05Sfy - 0.00576076 Sfx- 2.94529×10-04St - 0.101860 fyfx+ 0.0719970 fyt- 12.5766 fxt??????? (9)

It has been mentioned earlier that in this study an ANFIS, RSM and theoretical equations have been used for predicting surface roughness. The Root Mean Squired Errors (RMSE) and Mean Absolute Percentage Errors (MAPE) have been calculated for each of the above mentioned models and summarized in Table 4. It can be observed from the Table 4 that the prediction results for surface roughness are more accurate in ANFIS model if both training and testing data are considered. So finally the ANFIS model can be suggested as the best prediction model and can be used further for surface roughness prediction using ball end milling operation on Aluminum. The results listed in Table 3 are found to be within acceptable limits for the ANFIS model. Larger deviation in prediction for surface roughness in few of the cases occurs may be due to heterogeneity in work piece composition, small discrepancy in tool or work piece setting and tool or machining condition.

Figure 4

Surface Plot of Roughness Ra ?m vs

(a) Inclination Angle φ and Spindle Speed S rpm, (b) Inclination Angle φ and Feed Rate fy mm/min, (c) Inclination Angle φ and Feed fx mm, (d) Inclination Angle φ and Depth of Cut t mm, (e) Spindle Speed S rpm and Feed Rate fy mm/min, (f) Spindle Speed S rpm and Feed fx mm, (g) Feed Rate fy mm/min and Feed fx mm, (h) Feed Rate fy mm/min and Depth of Cut t mm, (i) Feed fx mm and Depth of Cut t mm.

Figure 4 shows the relationship between ANFIS predicted roughness with different input parameters. In Figure 4(a) and 4(b) it can be observed that low spindle speed S and low feed rate fy near 0o inclination angle of the spindle axis gives better surface finish. Feed fx leads to deteriorate surface quality at low inclination angle. Figure 4(c) suggests to keep fx at a medium level if cutter axis is vertical to the machining surface. At low depth of cut surface quality seems worse in Figure 4(d). Figure 4(e) and 4(f) shows that for medium level of speed feed rate fy and feed fx has low impact on surface finish. On the other hand from Figure 4(g) it is observed that higher feed rate in both direction results increased surface roughness. Figure 4(h) and 4(i) shows the interaction effect of depth of cut with feed rate fy and feed fx on Ra. At low feed rate (fy), depth of cut is more or less consistent. For low feed fx, depth of cut should be higher for getting better surface quality.

Correlation test has been done between Ra (Experimental) and different input parameters for training data set. It shows that feed rate fy (mm/min) and feed fx (mm) have a great positive correlation with Ra; Pearson correlation coefficient was r = 0.722 for fy (mm/min) and r = 0.788 for fx (mm) with P-value approximately zero for both direction of feed. And depth of cut t (mm) has a weak negative correlation with Ra; in this case r = -0.209 with p-value 0.088. Cutter axis inclination angle and spindle speed have very poor correlation with Ra.

4. APPLICATION OF PROPOSED PREDICTION MODEL

Surface roughness is one of the main quality characteristics of commercial dies. The model developed for the prediction of surface roughness of dies made of Aluminium can be used for setting the cutting parameters to obtain a predetermined surface roughness. Let us implement the ANFIS model for a practical example of manufacturing a commercial die with a particular level of surface roughness. Here the die selected as an example is made of Aluminium used in ceramic plate production. The surface quality of the die will affect the surface of the final ceramic product. The post processing of a machined surface will cost higher if the machined surface is too much rough. As a result it is important that the die surface should be of low surface roughness.

In this example the surface roughness of the die is targeted to be smooth which is defined a triangular membership function, where the desired Ra should have smallest possible value of 0μm, most promising value of 2.0 ?m and largest possible value 2.2 ?m respectively. Only a ball end mill cutter of 8 mm diameter was being used for the machining. It was previously machined with rough cut for removing the maximum material and already given a near to desired shape. Now it is needed to machine for finishing and obtaining the final die. For the final machining operation with ball end mill, the average depth of cut is needed only 0.2 mm. The shape of final die is shown in Figure 5 (a). It is round shaped plate of 71.8 mm diameter. The center portion of the die is flat. The diameter of the center of the die (flat part BC) is 20 mm and outer round part (AB) of the die is as an internal shape of a sphere of radius 100 mm. This spherical portion AB forms 15o angle at its center. The cross section view and dimension of this die is shown in Figure 5 (b) and (c) respectively. Our focus is on the surface roughness of the upper face (ABC) of the die as shown in Figure 5 (c). The possible cutting parameters are as follows,

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