MODELING OF MACHINING PROCESS USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) TO PREDICT PROCESS OUTPUT VARIABLES: A REVIEW

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1 International Journal of Mechanical and Materials Engineering (IJMME), Vol.6 (2011), No.2, MODELING OF MACHINING PROCESS USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) TO PREDICT PROCESS OUTPUT VARIABLES: A REVIEW P.S. Desale and R.S. Jahagirdar Mechanical Engineering Department, Shree Shivaji Vidya Prasarak Sansth s, B S Deore College of Engineering Deopur, Dhule , Maharashtra state, India. purudesale@yahoo.com Received 9 November 2010, Accepted 04 May 2011 ABSTRACT Machining process output cutting parameters are function of input cutting parameters. Prediction of output cutting parameters results into the saving in time and cost of manufacturing the product. Machinability is an important property of material. It is about cutting the material with maximum material removal shortest time, maximum tool life and best surface finish. The high quality of surface finish is very important to meet market requirements. Recently to achieve objective of lesser prediction error many researchers are using adaptive neuro fuzzy inference system (ANFIS). This paper reviews the application of adaptive neuro fuzzy inference system for prediction of output variables in milling and turning machining processes. Keywords: Machining, Adaptive neuro-fuzzy inference system, Input variables, output variables. 1. INTRODUCTION In an industry various manufacturing processes such as machining, casting, forming and joining are used for production purpose. Machining process attracted the attention of researchers for modeling the input and output variables. Machining is a destructive process in which material is removed from the raw material in the form of chips to produce the desired shaped component. Machining process can be used to machine axisymmetric, non axisymmetric and prismatic components. Milling process is used to machine all the types of components. Turning is used to machine axisymmetric components. The major objective of researchers was to predict the output variables. The input variables Table 1 are responsible for output variables. Therefore, Output variables = f (Input variables) (1) Both hard and soft computing techniques were applied for prediction of output variables. However the soft computing is better in prediction. Recently ANFIS technique application in Milling and turning providing promising solution in prediction of output cutting parameters. The milling and turning machining processes are discussed in preceding part of this paper. The paper also review the research work done using ANFIS for Milling and Turning machining processes. This paper is concluded with observations and research scope in ANFIS domain based on previous work. Table 1 Input and Output Variables Input Variables Machine tool - Specifications (Capacity, power, capacity, accuracy, etc.) Tool Material, geometry, rigidity, etc. Work piece Geometry, properties Cutting conditions Speed (S), feed (F), depth of cut (D), cutting time (t) Cutting fluid properties, pressure, Temperature Output Variables Tool Life / Wear Power consumption Surface finish / roughness Dimensional accuracy / dimensional deviation Vibration (Machine/tool) Temperature (Tool, material) Noise Material removal rate Cutting forces The output cutting parameters are predicted using input cutting parameters. In this papers methodologies applied for prediction such as experimental setup, cutting tool selection, material / work piece for prediction with properties, input variables used for prediction, output variable predicted, prediction accuracy, training and testing data used for ANFIS, software used to get the results, are discussed.. This discussion will be a guideline and useful for the researchers in their work. This objective of this paper is to acknowledge the previous work done and to provide useful guide line for encouraging the researchers to use ANFIS as a modeling tool. 2. ADAPTIVE NURO-FUZZY INFERENCE SYSTEM (ANFIS) ANFIS is a fuzzy inference system implemented in the frame work of adaptive networks. Using a given 178

2 input/output data set, the ANFIS method constructs a fuzzy inference system (FIS) whose membership function parameters are tuned (adjusted) using a back propagation gradient descent and a least-squares type of method. This allows fuzzy systems to learn from the data they are modeling. FIS structure is a network-type structure similar to that of a neural network, which maps inputs through input membership functions and associated parameters, and then through output membership functions and associated parameters to outputs. ANFIS applies two techniques in updating parameters. For premise parameters that define membership functions, ANFIS employs gradient descent to fine-tune them. For consequent parameters that define the coefficients of each output equations, ANFIS uses the least-squares method to identify them. This approach is thus called hybrid learning method since it combines the gradient descent method and the least-squares method. ANFIS modeling process starts by obtaining a data set (input output data pairs) and dividing it into training and checking data sets. The training data set is used to find the initial premise parameters for the membership functions by equally spacing each of the membership functions. A threshold value for the error between the actual and desired output is determined. The consequent parameters are found using the least-squares method. Then an error for each data pair is found. If this error is larger than the threshold value, update the premise parameters using the gradient decent method. The process is terminated when the error becomes less than the threshold value. Then the checking data set is used to compare the model with actual system. A lower threshold value is used if the model does not represent the system (Shing, 1993). complex system, possible to intuitively setup reasonable membership functions and then employ the ANNs training process to generate a set of fuzzy if-then rules that approximate a desired data set. To present ANFIS architecture two fuzzy if then rule are considered. Rule 1: If x is A1 and y is B1 then z1 = p1x + q1y + r1 (2) Rule2: If x is A2 and y is B2 then z2 = p2x + q2y + r2 (3) Where x and y are the inputs, Ai and Bi are the fuzzy sets, zi (i = 1, 2) are the outputs within the fuzzy region specified by the fuzzy rules, pi, qi and ri are the design parameters that are determined during the training process. The ANFIS architecture to implement these two rules is shown in Fig. 1, in which a circle stands for a fixed node, whereas a square indicates an adaptive node. Five ANFIS layers: (i) Input membership function layer O1i = µ Ai (x), i = 1, 2, (4) O2i = µ Bi (y), i = 1, 2, (5) (ii) Rule layer ωi = µ Ai (x) µ Bi (y), i = 1, 2,.. (6) (iii) Normalization layer ώ 1 = ω1 / ω1 + ω2 (7) (iv) Output membership function layer ώ1 z1 (8) (v) Output layer ώi zi (9) Figure 1 ANFIS Architecture The ANFIS is a fuzzy Sugeno model of integration where the final fuzzy inference system is optimized via the ANNs training. It maps inputs through input membership functions and associated parameters, and then through output membership functions to outputs. The initial membership functions and rules for the fuzzy inference system can be designed by employing human expertise about the target system to be modeled. Then ANFIS can refine the fuzzy if-then rules and membership functions to describe the input/output behavior of a 3. APPLICATION OF ANFIS TO MACHINING PROCESSES 3.1 Milling Milling is a multipoint tool cutting process in which the cutter rotates at some speed while the work feeds past the cutter. The peripheral speed of the cutter called cutting speed, movement of the work piece under the cutter per unit time called feed rate or table feed, depth of cut in the direction along the cutter axis called axial depth of cut, depth of cut normal to the cutter axis called radial depth of cut, and number of passes are process parameters. These parameters may be optimized for obtaining the minimum cost of machining and minimum production 179

3 time. To predict performance of process and optimization, soft computing techniques have been applied. Milling is basic machining process by which a surface is generated by removal of Chips from a work piece as it fed to multipoint rotary cutter in a direction perpendicular to the axis of the cutter. Surface can be generated by up milling and down milling. Up milling is also called conventional milling in which the cutter rotates in opposite direction of feed of the work piece. Where as in down milling which is also known as climbs milling, cutter rotates in the same direction of feed of the work piece. The tendency of machined surface to show tooth marks in is less in down milling. Therefore for the purpose of this paper down milling process has been considered. The surface roughness is the finer irregularities of the surface texture. This is the most significant output parameter of the product to be processed and attracted the attention of researchers to generate a prediction model. It is known that the adaptive network-based fuzzy inference system (ANFIS) is a successful approach for dealing with the nonlinear mapping. By using a hybrid learning algorithm which combines the gradient method and the least squares estimate to identify parameters, the ANFIS can construct an input output mapping based on both human knowledge (in the form of fuzzy if then rules) and the stipulated input output data pairs. Therefore, Lo (2003) used the ANFIS with the hybrid learning algorithm to model the relationship between the surface roughness and the milling parameters (i.e., spindle speed, feed rate and depth of cut) in the end milling process. Here it should be noticed that, to the authors best knowledge, only the literature presented by Lo (2003) studied the issue of using the ANFIS to predict the work piece surface roughness after the end milling process. The optimal results obtained by Lo (2003) for predicting surface roughness are that the accuracy predicted by using the ANFIS with the triangular membership function is 95.35%, and the accuracy predicted by the ANFIS with the trapezoidal membership function is 92.69%. Despite the success of applying the ANFIS to predict the surface roughness of the end milling operation given by Lo (2003), there are still some issues, such as how to determine the most suitable membership functions and how to simultaneously find the optimal premise and consequent parameters by directly minimizing the root-mean-squared-error (RMSE) performance criterion, that need to be resolved. The surface roughness model of CNC down milling using for Alumic 79 material proposed by Dwieri et al (2003). Effect of machining variables such as spindle speed, feed rate, depth cut and number of flutes on the surface finish of Alumic -79 material is predicted in order to improve and increase the range of application. Optimum surface roughness of µm for flute at the spindle speed of 2000rpm, feed rate of 0.06 mm/tooth, and depth of cut of 2mm is achieved in their work. They also found for the two flutes that the minimum surface roughness of µm is achieved at spindle speed of 2000 rpm, feed rate of 0.05 mm/tooth and depth of cut of 2 mm. Four flute modeling F Dweiri used Matlab 5.3 toolbox to obtain the results. Sugeno fuzzy model with bell shaped membership function was used for fuzzy inference model. The gradient descent to fine tune premise parameters and to identify consequent parameter least square method is applied. The output came close to the predicted value with an error of less than 5%. Ho et al. (2009) used ANFIS with Taguchi genetic learning algorithm to determine the most suitable membership function and to simultaneously find the optimal premise and consequent parameters by directly minimizing the root mean square error performance criterion. They predicted the surface roughness of 6061 aluminum alloy on a high speed steel four flute end milling cutter with a ¾ diameter. They used 48 experimental training data and 24 testing data of Lo (2003) for prediction model to compare the results using Taguchi genetic learning algorithm. The HTGLA (hybrid Taguchi genetic learning) based ANFIS effectively predicted the surface roughness of the end milling process using three milling parameters spindle speed, feed rate and depth of cut. The optimum prediction error of the HTGLA based ANFIS is 4.06% which is less than Lo (2003) and from the Matlab tool box. Uros et al. (2009) in their paper developed a reliable method to predict flank wear during end milling process. A neural-fuzzy scheme is applied to perform the reduction of flank wear from cutting force signals. In this contribution they also discussed the construction of an ANFIS system that seeks to provide a linguistic model for the estimation of tool wear from the knowledge embedded in the neural network. Machining experiments conducted using the proposed method indicate that using an appropriate maximum force signals, the flank wear can be predicted within 4% of the actual wear for various end-milling conditions. 3.2 Turning In turning processes, a single point cutting tool moves along the axis of a rotating work piece. The peripheral speed of the work piece called cutting speed, movement of the tool along the axis of job for one revolution of job called feed, and radial depth of cut of the tool are the process parameters. These parameters may be optimized for obtaining the minimum cost of machining and minimum production time. However, for optimization, performance of the process has to be predicted. Machining performance prediction is two main attributes of quality of turned job are surface finish and dimensional deviation. Surface finish is defined as the degree of smoothness of a part s surface after it has been manufactured. Surface finish is the result of the surface roughness, waviness, and flaws remaining on the part. Dimensional deviation is defined as the radial difference between the set depth of cut and the obtained depth of cut. Researchers studied the effect of number of factors such as feed rate, cutting speed, depth of cut, work material characteristics, unstable built up edge, tool nose radius, tool angles, stability of material, tool and work piece setup, use of cutting fluids, radial vibration, tool 180

4 material, etc. on surface finish. The process researchers used four main methods, viz., multiple regressions, mathematical modeling based on physics of process, fuzzy set theory, and neural network. The review in this paper will focus on ANFIS technique. In their paper Ho et al. (2002) Modeling and prediction of surface roughness of a work piece by computer vision in turning operations play an important role in the manufacturing industry. This paper proposes a method using an adaptive neuro-fuzzy inference system to accurately establish the relationship between the features of surface image and the actual surface roughness, and consequently can effectively predict surface roughness using cutting parameters (cutting speed, feed rate, and depth of cut) and gray level of the surface image. Experimental results show that the proposed ANFISbased method outperforms the existing polynomial network-based method in terms of modeling and prediction accuracy. The camera captures surface images with resolution, 1/30 s grabbing speed, and 8-bit digit output. A number of cutting tests are carried out using a CNC lathe with a tungsten carbide tool and working on S45C steel bars. The feasible ranges of the cutting parameters are as follows: cutting speed V ( m/min), feed rate F ( mm/rev), and depth of cut D ( mm). Based on the cutting parameter combinations, 57 turning experiments were performed. The actual roughness of machined surface is measured by a profile meter (Surf corder SE-1100) within a sampling length of 8 mm and measurement speed of 0.5 mm/s. The surface roughness Ra is the arithmetic average of the absolute value of the heights of roughness irregularities from the mean value measured. The architecture of the ANFIS used in the proposed method there are four input parameters (V, F, D, Ga) and one output value, the predicted surface roughness Ra. The experimental data of 57 turning experiments are utilized to train the used ANFIS model with 3 fuzzy sets for each input parameter. The hybrid batch learning rules are used in the training. The trained ANFIS establishes the relationship between the features of surface image and the actual surface roughness. Once the cutting parameters (V, F, and D) and the gray level Ga are given, then the predicted surface roughness Ra can be easily obtained. The absolute arithmetic mean checking error of modeling surface roughness using the 57 sets of training test data is %. Experimental results show that the proposed method is superior to the Polynomial Network (PN) based method. To further verify the prediction accuracy, several experiments using verification data are performed. It is found that the errors are smaller than 4.6%, and the mean error is only 0.38%. Compared with the mean error 6.2% of the PN-based method, the proposed ANFIS-based method is more useful to accurately predicted surface roughness. Lee et al. (2004) proposed a method using an adaptive neuro-fuzzy inference system to establish the relationship between actual surface roughness and texture features of the surface image. The accurate modeling of surface roughness can effectively estimate surface roughness. The input parameters of a training model are spatial frequency, arithmetic mean value, and standard deviation of gray levels from the surface image, without involving cutting parameters (cutting speed, feed rate, and depth of cut). Experiments demonstrate the validity and effectiveness of fuzzy neural networks for modeling and estimating surface roughness. Experimental results show that the proposed ANFIS-based method outperforms the existing polynomial-network-based method in terms of training and test accuracy of surface roughness. 4. CONCLUSION In this paper, a review of application of ANFIS technique in modeling of machining process for prediction of process output variables has been presented. The objective is to present the review of major machining process at one place so as to provide a specific literature to the reader. Adaptive Neuro-Fuzzy Inference System have been used for estimating the surface roughness, tool wear and tool life. However, the results are not as impressive as in the case of surface roughness prediction. This is due to highly statistical nature of tool life and tool wear and difficulty in identifying a measurable parameter with which the tool wear can be well-correlated. Most of the authors have used cutting force components as input parameters in their models. Several other signals such as acoustic emission, vibrations, and temperatures have also been tried. It is observed that instead of raw data from sensors, features extracted from the signals are more effective in modeling the tool wear and tool life. ANFIS has been effectively employed for predicting the surface roughness of machined components in milling and turning. However, most of the models do not predict the surface roughness as a function of time, concentrating on the time zone when surface finish changes only slightly with time. The research was concentrated only on the effect of milling process input Variables speed, feed and depth of cut on process output variable work piece surface roughness and cutter flank wear. Work is not reported using ANFIS to optimize undesirable output variables such as vibration, noise, temperature, coolant properties, backlash in a machine tool components etc. The research using ANFIS was conducted on materials Alumic-79 and 6061aluminum alloy only. 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