APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR MODELING SURFACE ROUGHNESS IN CENTERLESS GRINDING OPERATION

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1 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Guwahati, Assam, India APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR MODELING SURFACE ROUGHNESS IN CENTERLESS GRINDING OPERATION Mondal 1* S.C., Mandal 2 P. 1* Assistant Professor, Department of Mechanical Engineering, Indian Institute of Engineering Science and Technology, Shibpur, Howrah , India, scmondall@gmail.com 2 PG Scholar, Department of Mechanical Engineering, Indian Institute of Engineering Science and Technology, Shibpur, Howrah , India, prosun.mech@gmail.com Abstract There is growing need among the manufacturers to model process performance in a centreless grinding process using appropriate modelling techniques. This paper presents an application of artificial neural network (ANN) for modeling surface roughness in centreless grinding process. The design of grinding factors is based on a full factorial design of experiment. Centerless grinding operation is widely used in modern manufacturing industry because of its high level of accuracy for micro-finishing of shaft, pin material compare to conventional grinding operation. The experimental data is collected for machining a pin of C 40 steel material used in the bottom block of crane-hook. The design factors (regulating wheel speed, depth of cut and coolant flow) are selected based on experimental design methodology. Full factorial design method is applied for taking three factors at three levels each and a total 3 3 or 27 number of experiments are done in all possible combination of these parameters. The network model is trained by back propagation algorithm. Out of 27 data 20 experiments used to train the network and for validation and the rest 7 data used for test. The learning rate, momentum co-efficient and the number of neurons in the hidden layer are found by trial error method. Optimum architecture has been found based on mean square error and convergence rate. The learning rate, momentum co-efficient and the number of neurons in the hidden layers are found by trial and error method. In present work for architecture network with α =0.02, and η= 0.6, the mean square error for training is and for testing is which is minimum. So it is found that optimum network is The performance of this particular trained neural network has been tested with the experimental data and found to be satisfactory. Thus the proposed ANN model is efficiently used for predicting surface roughness in centerless grinding operation. Keyword: Centre-less Grinding, Surface roughness, Modelling, Design of experiment, Artificial Neural Network 1 Introduction Today s manufacturing industries are very much concerned about the quality of their products. Manufacturing industries are focused on producing high quality products in time at minimum cost. Surface finish is one of the crucial performance parameters that have to be controlled within suitable limits in a grinding process. Therefore, prediction or monitoring of the surface roughness of machined components has been an important area of research. Centre-less grinding is characterized by its complexity, nonlinearity and sensibility to a large number of input factors e.g. Depth of cut, regulating wheel speed and coolant supply that influence system stability and output performance. The most important quality characteristics for the input factor like Depth of cut, regulating wheel speed and coolant supply of centre-less grinding process are mrr and surface roughness. The enhancement of such complex process efficiency requires model based process simulation, which is a powerful tool for evaluating the performance of complex systems. Empirical models, such as the regression analysis model, the fuzzy logic model, and the neural network model, have, generally, shown satisfactory prediction accuracy, particularly useful for the on-line response evaluation and control. In many cases, data from design of experiment (DOE) were used to establish the regression models or to develop the fuzzy rule sets or 608-1

2 APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR MODELING SURFACE ROUGHNESS IN CENTERLESS GRINDING OPERATION to train the neural networks. For better performance researchers have done a lot of work on surface roughness modeling in grinding operation. Aguiar et al. (2008) predicted surface roughness in grinding using artificial neural network. Shrivastava et al. (2011) developed an Intelligent Modeling of Surface Roughness during Diamond Grinding of Advanced Ceramics by two different approaches multiple regression analysis (MRA) and artificial neural network (ANN) and compared the same. They found that ANN gave more accurate result. From the above literature, very little work has been done for modeling of centerless grinding process. This paper applied artificial neural network for modeling surface roughness in centerless grinding process. 2 Literature review Brinkseier et al. (1998) described grinding as a very complex manufacturing process. Various factors have affect on this process. Thus reproducible results (output) are rarely obtained. The most important one is that the cutting ability of the grinding wheel changes considerably during the grinding time. In practice, the grinding process is carried out with cutting parameters which are safe but not optimal. Hashimoto et al. (2004) described the stability of the centerless grinding process to the optimum set-up condition for precision or productive operations. Billerman et al. (2012) developed a novel nonlinear model for centerless grinding process. The model describes the dynamic behavior of the process. The result shows that the dynamic behavior of the centerless grinding process can be represented with a cubic stiffness function that is obtained from the analysis of the surface topology. Zhou et al, (1996) investigates the relationship between the process setup parameters and lobing behavior of the centerless grinding, and provides general guidelines for selecting proper setup parameters to minimize the lobing effect. Li et al. (2007) presented a dynamic model that simulated plunge centerless grinding and predicted its instability-related characteristics. The work-piece quality depends to a great extent on the experience of the operator according to Kim et al. (2001). Artificial neural networks have been studied for many years in the hope of achieving the humanlike performance in the field of speech, image recognition and the pattern classification. These neural networks are composed of many non-linear computational elements operating in parallel. Neural networks, because of their massive nature, can perform computations at a higher rate, according to Augier et al. (2008). Because of their adaptive nature using the learning process, neural networks can adapt to changes in the data and learn the characteristics of the input signals. According to Kwak et al. (2004), the ability to learn is a fundamental trait of the neural network. Although a precise definition of learning is difficult to formulate. The learning in a neural network means to find an appropriate set of weights that are connection strengths from the elements to the other layer elements. For this reason artificial neural network tool has attracted interest of several researchers in the surface roughness prediction. Shrivastava et al. (2011) developed an Intelligent Modeling of Surface Roughness during Diamond Grinding of Advanced Ceramics. They have done a comparative study between two different approaches namely multiple regression analysis (MRA) and artificial neural network. Mukhopadhyay et al. (2005) reviewed the application of acoustic emission techniques for monitoring forming and grinding processes. Aguiar et al. (2006) developed a model to predict Surface Roughness in Grinding using Neural Networks. Mamun et al. (2012) developed Numerical Model of Surface Roughness in Grinding under Minimum Quantity Lubricants (MQL) using Response Surface Method (RSM). Kumar et al. (2007) also developed model to predict wear and surface roughness in electro-discharge diamond grinding using two techniques, namely design of experiments and neural network 3 Artificial neural network Artificial neural network models are inspired by animals central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. A neural network is a massively parallel distributed processor that has a natural propensity for storing experimental knowledge and making it available for use. In this study, to construct the neural network models, three cutting parameters viz., depth of cut, regulating wheel speed and coolant flow rate are used as the input neurons and corresponding surface roughness as the output neuron. Figure 1 shows typical architecture of neural network. To construct the models, the hyperbolic tangent sigmoid function in the hidden layer and linear activation function in the output layer are considered Figure 1 Artificial neural network architecture

3 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Guwahati, Assam, India Determination of hidden layer(s) and numbers of neurons in the hidden layer(s), learning rate (η) and momentum coefficient (α) are the considerable task. Optimum number of neurons in the hidden layer learning rate (η) and momentum coefficient (α) are decided by trial with increasing the number of neurons. Minimum mean square error (MSE) is the selection criteria. In this study, to train the network, Levenberg-Marquardt algorithm is selected for training (Eq. 1). The best network is selected based on the minimum mean square error (MSE). 4.1 Data collection for the centerless grinding process Grinding experiments were conducted on centerless grinding machine-tool as shown in Figure 3 which was made by MIC machine tool and industries, Ghaziabad, Machine No-616 and model is GCL 63.The range of the diameter of the workpiece that can be ground is 2-100mm. E 1 p n p k = 1 = 1 p p 2 ( d ) k C k (1) Eq. 1 is the expression for mean square error (MSE) where, N is the number of pattern, n is the number of p node in the output layer, Ck is the desired/experimental output for k th node of the p th p pattern and C k is the calculated/predicted output of kth node of pth pattern. Nguyen-Widrow weight initialization algorithm has been applied which generates initial weight and bias values for a layer so that the active regions of the layer's neurons will be distributed roughly evenly over the input space (Demuth H. et. al., 1998). 4 Experimentation The component material was C40 mild steel rod of 17 mm diameter and 100 mm by length, used as crane-hook-pin as shown in the figure 2. In experiment C40 mild steel was used because this grade of steel offers better forming & bending quality. It was used for applications, where critical bending operations were required. Due to carbon range of 0.4% and that of manganese of 0.9%, it can also be quenched & tempered and thus it is very suitable for components where, critical bending has to be achieved & high tensile and toughness can be obtained by means of quenching & tempering. The component used in the experimentation was supported by specially made work rest blade with a 30 angle. A vitrified grinding wheel A463V5L10, with an abrasive of aluminum oxide, was used. Maximum grinding wheel speed was 1910 rpm. Maximum regulating wheel speed was 450 rpm and constant infeed is 95mm. The machine was equipped with dynamic grinding wheel balancing. The measurements were carried out with a Mitutoyo Surftest SJ-301 with cutoff length 2.5 mm and number of sampling length 5, stylus type surface texture-measuring instrument. The measurement results are displayed digitally or graphically on the touch panel, and output to the built in printer. It has a maximum measuring range of 350 µm (-200 to +150µm). It can support various surface parameters for evaluating surface texture. The input process parameters and there level is shown in the Table 1. Table 1 Input process parameters and their levels Factors Symbols Low level Wheel speed Depth of cut Coolant flow Figure 3 Experimental Set-up Medium level High level V w d c C f 1/3 2/ Figure 2 Finished Jobs

4 APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR MODELING SURFACE ROUGHNESS IN CENTERLESS GRINDING OPERATION 4.2 Experimental Design A series of experiments have been conducted to evaluate which grinding parameters affect the surface roughness. Three grinding parameters such as wheel speed (V w ), depth of cut (d c ), and Coolant flow valve opening (C f ) were selected for experimentation. Grinding wheel adopting aluminum oxide abrasives with vitrified bond in the grinding wheel was used to grind C40 steel pin. A surface roughness tester (Mitutoyo SJ-301) was used to measure the roughness. Table 1 listed controllable factors (grinding parameters) and their levels considered in this study. Each factor had three levels (process ranges). 5 Analysis and Results In Table 2 there are total 27 different experimentations performed for different conditions. Back propagation neural network algorithm has been used in the present work. To train the neural network, depth of cut, regulating wheel speed and coolant flow valve opening are used as input parameters, and corresponding surface roughness of the machined product as the output parameter. Table 2 Experimental design matrix ExNo d c (µm) V w (rpm) C f (% of / / / / / / / / / / / / / / / / / / In the current study, early stopping technique was implemented. For this, out of 27 datasets, randomly 20 sets were used for training, 7 datasets for testing and validation purpose. The validation set determines when the training should stop by monitoring the error. Testing set does not participate in the training of the network but is used to test the generalization of the trained network. The number of hidden layer, number of nodes in the hidden layer, learning rate (η), and momentum coefficient (α) are decided by trial and error. Table 3 shows that mean square error of training and testing for different network architecture, learning rate (η), and momentum coefficient (α).different combinations of learning rate (η), and momentum coefficient (α) and number of hidden layer have been tried. Depending upon the mean square error, optimum network architecture has been arrived at. In the present case, from table 3 for architecture network with η=0.02, and α=0.6 mean square error for training is and for mean square error for testing is is minimum. So it is found that optimum network is Figure 4 shows performance curve for architecture Figure 4 Variation of MSE for network Regression analysis was conducted for training, validation and testing pattern and it was observed from the regression analysis that correlation 608-4

5 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Guwahati, Assam, India coefficient (R) for training(figure 5) was ;. For testing (Figure 6), correlation coefficient(r) was and overall R value is which implies moderate correlation between experimental and predicted surface responses. Figure 6 ANN model regression plot for neural network for testing Figure 5 ANN model regression plot for neural network for training Figure 7 Bar chart predicted and experimental Ra value testing set Comparative study of experimental and ANN predicted surface roughness for randomly selected seven testing have been shown in Figure 7 bar chart. In the bar chart along the X-axis is Experiment No which is selected for testing and along the Y-axis is Surface roughness (Ra) in µm. Bar chart shows that experimental Ra value and predicted Ra value are very close to each other. So we can say that network architecture will give very satisfactory result. 6 Conclusions Back propagation neural network based surface roughness prediction methodology has been adopted using various important parameters like depth of cut, regulating wheel speed and coolant flow valve opening influencing the surface roughness. It has been observed that neural network could well learn the pattern and could be used for future prediction of surface roughness. The predicted surface roughness from the present neural network model is very close to the values measured experimentally, thus showing the efficacy of back propagation neural network for predicting surface roughness in centerless grinding. Future work can be done using response surface modeling and compared the results to infer the best alternatives for modeling surface roughness in centerless grinding operation

6 APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR MODELING SURFACE ROUGHNESS IN CENTERLESS GRINDING OPERATION Acknowledgement The author gratefully acknowledges the kind support and cooperation provided by the technical staffs at Machine shop of IIEST, Shibpur, Howrah. References Augier, P.R. Cruz, E.D.C. Paula, W.C.F. and Bianchi C.E. (2008), Predicting Surface Roughness in Grinding using Neural Networks, Advances in Robotics, Automation and Control, CC BY-NC-SA 3.0 license. Researches in Engineering Mechanical and Mechanics Engineering, Vol. 12(5). Mukhopadhyay, C.K. Jayakumar, T. Venugopal, S. Mannan, S.L. and Raj, B. (2005), A review of the application of acoustic emission techniques for monitoring forming and grinding processes, Journal of Materials Processing Technology, Vol. 159(1), pp Shrivastava, P.K. and Dubey, A.K. (2011), Intelligent Modeling of Surface Roughness during Diamond Grinding of Advanced Ceramics, Proceedings of the World Congress on Engineering, Vol. I, WCE 2011, London, U.K. Brinksmeier, H. K. Toe, N. and Czenkusch, C. (1998), Modeling and optimization of grinding processes, Journal of Intelligent Manufacturing, Vol. 9, pp Hashimoto F.and Lahoti (2004), Optimization of Setup Conditions for Stability of the grinding Process, CIRP Annals Manufacturing Technology, Vol 53(1), pp Zhou S., Gartner J. R., Howes T.D.( 1996) On the Relationship between Setup Parameters and Lobing Behavior in Centerless Grinding CIRP Annals - Manufacturing Technology,Vol. 45(1), pp Li, H., Shin, Y.C. (2007), A time domain dynamic simulation model for stability prediction on infeed centerless grinding processes. ASME Journal of Manufacturing Science and Engineering, vol. 129(1), pp Kim, H.Y. Kim, S.R. Ahn, J.H. and Kim, S.H. (2011), Process monitoring of centreless grinding using acoustic emission, Journal of Material Processing Technology, Vol. 111, pp Kwak, J.S. and Ha, M.K. (2004), Neural network approach for diagnosis of grinding operation by acoustic emission and power signals, Journal of Materials Processing Technology, Vol. 147(1), pp Kumar, S. and Choudhury, S.K. (2007), Prediction of wear and surface roughness in electro-discharge diamond grinding, Journal of Materials Processing Technology, Vol. 191, pp Mamun, A.A. and Dhar, N.R. (2012), Numerical Modeling of Surface Roughness in Grinding under Minimum Quantity Lubricants (MQL) using Response Surface Method (RSM), Global Journal of 608-6