ARTIFICIAL NEURAL NETWORK MODELING AND OPTIMIZATION IN HONING PROCESS

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1 International Journal of Computer Engineering & Technology (IJCET) Volume 7, Issue 3, May-June 2016, pp , Article ID: IJCET_07_03_006 Available online at Journal Impact Factor (2016): (Calculated by GISI) ISSN Print: and ISSN Online: IAEME Publication ARTIFICIAL NEURAL NETWORK MODELING AND OPTIMIZATION IN HONING PROCESS Benu Singh Research Scholar, Department of Computer Application, B B Das University, Lucknow, India Sunita Bansal Professor, Department of Computer Application, B B Das University, Lucknow, India Puneet Mishra Professor, Department of Computer Science, Lucknow University, Lucknow, India ABSTRACT Determination of process parameters and maximum utility of the resources are the two main concerns while designing the manufacturing process for the products. The process parameters affect the final product shape and aesthetic look; whereas the utility refers to the output. The present study is devoted to the development of ANN models for the analysis of the honing process applied to an actual industrial component, namely the connecting rod of a motor bike. The surface quality of the honed components is measured with the help of a Talysurf Intra machine. Traditionally accepted measure of surface roughness is Center Line Average (R a ) and therefore the same has been adopted for the present study. Six process parameters, namely grit size, coolant temperature, honing speed, honing feed, honing time and operator experience are considered as the input variables. In general, honing experiment is time consuming and expensive, especially when carried out in an industrial setting. Fractional factorial design is, therefore, used to minimize the number of experimental observations. The relationship between the inputs and outputs of the honing process is determined with the help of ANNs. The ANN models developed during the present investigation have a three layer structure, namely one input layer, one output layer and one hidden layer. The developed model is used to study the effect of the process parameter on R a values. Key words: Surface Roughness, Honing, Artificial Neural Network, and Optimization 67 editor@iaeme.com

2 Benu Singh, Sunita Bansal and Puneet Mishra Cite this Article: Benu Singh, Sunita Bansal and Puneet Mishra, Artificial Neural Network Modeling and Optimization In Honing Process, International Journal of Computer Engineering and Technology, 7(3), 2016, pp INTRODUCTION Surface roughness plays an important and valuable role in the practical and theoretical applications of a manufactured component. It is widely used as an index of product quality and is in most cases a technical requirement for mechanical products. [20] This is an important design consideration as it impacts many part characteristics such as fatigue strength, assembly tolerances, coefficient of friction, wear rate, corrosion resistance and aesthetics. [7] Roughness of the machined surface is important while planning for the machining operation such as turning, milling and honing etc. Production process parameters selected for the machining of a part influence the surface roughness and reliability of components. [19] Honing is an important fine finishing operation, often used for internal cylindrical surfaces such as gun barrels, hydraulic cylinders, bearings and engine cylinder bores. [1] Excess material is removed by means of slow moving abrasive sticks pressed against the surface to be machined. Two kinds of motion, namely rotational and reciprocating, are imparted by the honing machine to the hone (or honing tool) carrying the abrasive sticks. Although honing can be used on flat and external cylindrical surfaces too, it is predominantly used for finishing internal cylindrical surfaces (holes). Guide pads on the hone allow proper alignment (or float) of the abrasive sticks with the axis of the cylindrical hole. Size of workpiece determines the thickness of the hone. Larger the size, greater is the number of sticks. The usual length of the abrasive sticks is about one-half of the workpiece length. The stroke length during reciprocation is such that the abrasive sticks protrude one-fourth to onethird of their length on either side of the hole. This is done to ensure uniform wear of the abrasive sticks. The honing tool has an inbuilt mechanism to expand the honing sticks radially outwards and impart to them the needed radial in-feed. When the honing of the piece is completed, the honing sticks are retracted radially inward and the hone is withdrawn from the workpiece. 2. LITERATURE REVIEW The aim of a neural network is to learn from events that have happened during experiments and to be able to apply this to future situations or for new independent data. Experimental studies in neurophysiology show that the response of a biological neuron is random and it is possible to obtain predictable results only by averaging many observations. [22]. The development of neural networks began with the pioneering work of McCulloch and Pitts. [17] The physiological learning rule for synaptic modification was proposed for showing the connectivity of the brain with the continually changing as an organism learns differing functional tasks. [10] The first so-called self-organising maps using competitive learning was developed by Kohonen in 1972 and were capable of reproducing important aspects of the structure of biological neural networks. [13] Later, the idea of an energy function to formulate a new way of understanding computations performed by recurrent networks with symmetric synaptic connections was used. [11] Also, the development of a back propagation algorithm was reported in [24] 68 editor@iaeme.com

3 Artificial Neural Network Modeling and Optimization In Honing Process Manufacturing process parameter modelling and optimization is one of the advanced issues in the present environment. The process parameters if modeled well, resulting in low tool wear, improved tool shelf life and better surface asperities etc. The process parameters like feed rate, production quantity, workpiece hardness, cutting tool point angle, spindle speed and depth of cut etc are the affecting parameter of surface roughness. [9] The new methods to predict the surface roughness of the workpiece material during the manufacturing process are developed time to time by the researchers. [27] The machining forces, tool-wear relationship, effect of cutting conditions and tool geometry were analyzed earlier during the manufacturing process by regression analysis. [2, 14, 15, 23, 26] The series of experiments were conducted to determine the affects of these parameters on surface roughness [18] However, it is not easy to design the process parameter for complex shaped products involving a number of factors that are not linearly related. Still, this method has been widely used in manufacturing situation when there is a single output parameter of interest, and the correlations with the input variables are not so complex. [25] 3. PROBLEM FORMULATION Honing involves complex physical phenomena, including shearing, ploughing, heat transfer and lubrication. A variety of input variables such as physical and mechanical properties of workpiece and abrasive grits, feed, speed, coolant temperature and manmachine interaction affect the process. Exact mathematical analysis of the honing process is not available in literature. Therefore, process planners have traditionally resorted to hand book knowledge/experience or rules of thumb for designing the process. As literature depicted that CLA values corresponding to various input variables has the influence on the surface quality in the honing process. [16] In the present environment, several computer application software are available for modeling and optimization in real life problem. These software s help in selecting the appropriate significant parameters to obtain the required product quality, also they enhance the manufacturing system performance. Artificial Neural Network Model is versatile to design and optimize the process parameters. Further, the threefold cross over approach is helpful to design the test datasets based on the results of the experiment. Each network is trained, validated and tested with the help of these datasets. 4. METHODOLOGY The practitioners already have used regression analysis, genetic algorithms, expert systems or artificial neural networks (ANN) for analyzing the process. [4, 8] In particular, ANNs offer a new and intelligent alternative for relating the input variables of the honing process to its output parameters. An ANN has the capability to learn or to be trained about the honing process on account of its robustness, its ability to incorporate non-linear dependencies between the input and output variables and its power of generalization. [21] Artificial neural network seems to be more robust because it exhibits the highest level of integration with computers. In contrast with other methods, ANN can manage noisy or incomplete data with ease. The neural networks possess the ability to learn from a set of data without the need for a full specification of the decision model; they are believed to automatically provide any needed data transformations. There is no need to explicitly formulate the problem, the solution algorithm or to write code and information processing is distributed over the neurons which operate in parallel. In the present work, the back propagation artificial 69 editor@iaeme.com

4 Benu Singh, Sunita Bansal and Puneet Mishra neural network models is used to simulate the honing process and establish cause and affect relationships between various input factors and output characteristics. For the selected models the difference between observed and predicted values of the response variable is checked to be within the specified tolerance limit. The R 2 (a common statistic in ANN applications) is equal to one minus ratio of the sum of squares of estimated errors (i.e. the deviation between predicted and actual value of the dependent variable) to the sum of squared deviations about the mean of the dependent variable. It is a measure of the total variation of the dependent variable. Where y i indicates the observed response, and t i indicates target or predicted response Experimental Study Connecting rod (Shown in Figure 1) of an internal combustion engine is subjected to high rate cyclic loading. High accuracy and tight tolerances of cylindrical holes in the connecting rod of an automobile are ever more demanding requirements. Close tolerances and fits to mating components, namely the crankshaft on one end and piston head on the other, are specified for the sake of component reliability. Figure 1 Geometry of connecting rod The connecting rod is manufactured by the powder metallurgy technology, forging, and sometimes even casting. The powder forged (PF) rod is fabricated by consolidating metal powders into a form, sintering the form and machining to final dimensions. The forged connecting rod is fabricated by starting with a wrought steel billet, followed by forging and machining to the required dimensions. The quality of the bore surface of a connecting rod influences oil consumption, noxious emissions, and running performance. Inner surfaces of the big end bore and small end bore of a connecting rod are finished by rough honing followed by finish honing. In the present investigation, the honing experiments were conducted on connecting rods used in the Yamaha motorcycles. SCM 420H steel is used as the material of construction of the 70 editor@iaeme.com

5 Artificial Neural Network Modeling and Optimization In Honing Process connecting rods. The chemical composition of the connecting rod material is given in Table 1, standard values in Table 2 and the level of the process variable is given in Table 3. Table 1 Chemical composition of material of the connecting rod Ingredient C Si Mn S P Cr Mo Composition Table 2 Standard values for contact pressure Abrasive material Rough honing (N/cm2) Finish honing (N/cm2) Ceramic honing stones Plastic bonded honing stones Diamond honing edges Boron nitride honing ledges Table 3 Levels of the variables Grit size Temperature Speed Feed Time Operator Skill Number 0 C rpm µm Second Years Model Development In the present study, six input factors relevant to the chosen honing process have been considered. It was decided to adopt a 2-level approach for varying each factor in order during a given set of observations. The most intuitive scheme to study these factors and how they affect the responses would be to vary the factors of interest in a 2 k full factorial design, i.e. to try all possible combinations. [5] To generate this design, 2 4 design in the factors P 1, P 2, P 3, P 4, and then added two columns for P 5 and P 6. In this, P 1, P 2, P 3, P 4, P 5 and P 6 represent the six input variables taken for experiments. To find the two new columns select the two design generators I = P 2 P 3 P 4 P 6 and I= P 1 P 2 P 3 P 5. Thus column P 5 would be found from P 5 = P 1 P 2 P 3 and column P 6 would be 71 editor@iaeme.com

6 Benu Singh, Sunita Bansal and Puneet Mishra P 6 = P 2 P 3 P 4 (Table 4). Therefore columns P 1 P 2 P 3 P 5 and P 2 P 3 P 4 P 6 are equal to identity column. Table fractional factorial design of Experiment Ex. No. Grit Size Temperature Speed Feed Time Operator P 1 P2 P3 P4 P5 Skills P Two levels of each parameter were taken for the model construction and model validation experiments. Five (05) different set of experiments (readings) was conducted, with each set comprising of 16 experiments. Table 5 Factors and levels for various set of readings Factors Set Levels P1 P2 P3 P4 P5 P 6 No. ºC Rpm µm Sec. Years 1 st High Level (+) Low Level (-) nd High Level (+) Low Level (-) rd High Level (+) Low Level (-) th High Level (+) Low Level (-) th High Level (+) Low Level (-) editor@iaeme.com

7 Artificial Neural Network Modeling and Optimization In Honing Process The present paper describes the development and application of a artificial neural network model for the prediction of surface roughness in a honing process. The proposed ANNs have a 3-layer architecture, comprising of 6 neurons in the input layer, optimized number of neurons h in the hidden layer and a single neuron in the output layer shown in Fig. 2. Figure 2 Schematic diagram for the Single Output Type Neural Network Graphs are plotted to illustrate the comparisons of ANN predictions with the experimental observations for both trained and validated data corresponding to the above models. Fig.3 and 4 show the graphical plot for the R 2 value and the slope of ANN predictions of R a training and validation datasets respectively for the Model-1S. Similarly, figure 5 & 6 shows the graphical plot for Model-2S, and figure 7 & 8 shows the graphical plot for Models 3S. Figure 3 Predictions against Observations of R a for Model-1S (Training dataset) 73 editor@iaeme.com

8 Benu Singh, Sunita Bansal and Puneet Mishra Figure 4 Predictions against Observations of R a for Model-1S (Validation dataset) Figure 5 Predictions against Observations of R a for Models-2S (Training dataset) Figure 6 Predictions against Observations of R a for Models-2S (Validation dataset) 74 editor@iaeme.com

9 Artificial Neural Network Modeling and Optimization In Honing Process Figure 7 Predictions against Observations of Ra for Models-3S (Training dataset) Figure 8 Predictions against Observations of Ra for Models-3S (Validation dataset) The correlation coefficients of the training and validation data of R a component of surface roughness is measured for all the three models the values of R 2 show a good correlation for both training and validation data. 5. RESULTS AND CONCLUSIONS The performance of a connecting rod can be expressed in terms of R a surface roughness parameter. The present work is based on modeling of the honing process carried out on a precision automobile component, namely the big end bore of a connecting rod. The component is designed to be a load bearing and wear resistant, to offer low friction and possess long life. The proposed models relate the input parameters of the honing process to R a values. The control of the surface roughness parameter leads to achieve functional requirements like reduction of wear, improvement of oil efficiency and improved life of the component. Further, the graph between observed and predicted values shows the R 2 and the linear relationship. The R 2 value plotted in graph reflects the amount of noise in the 75 editor@iaeme.com

10 Benu Singh, Sunita Bansal and Puneet Mishra data. The lower value of R 2 indicates that important and systematic factors have been omitted from the model and the model is inadequate. REFERENCES [1] Armergo, E.J.A. and Brown, R.H. (1969), The machining of metals, Prentice- Hall Inc., Englewood Cliffs, New Jersey, pp370. [2] Arora, P.K. (2008), Statistical modelling of surface roughness in turning process, M.Tech. Dissertation, Department of Mechanical Engineering, CITM, MD University, Rohtak, Haryana, India. [3] ASME, (1996), Surface Texture (Surface roughness, Waviness, and Lay): An American Standard, ASME B (Revision of ANSI/ASME B ), and ASME Press, New York. [4] Bernados, P.G. and Vosniako, G.C. (2002), Predicting surface roughness in CNC face milling using neural networks and Taguchi s design of experiments, Robotics and Computer Integrated Manufacturing, 18, pp [5] Box, G.E.P, Hunter, W.G., and Hunter, J.S. (1978), Statistics for experimenter: An Introduction to design, data analysis, and model building, New York, John Wiley and Sons. [6] Dabnun, M.A., Hashmi, M.S.J. and El-Baradie, M.A. (2005), Surface roughness prediction model by design of experiments for turning machinable glass ceramic (Macor), Journal of Materials Processing and Technology, 164(65), pp [7] Feng, C-X. and Wang, X. (2002), Surface roughness predictive modeling Neural Networks vs. regression, IIE Transactions,.35, pp [8] Feng, C-X. (2001), Experimental study of the effect of turning parameters on surface roughness, Proceedings of 2001 Industrial Engineering Research Conference, Institute of Industrial Engineers, Norcross, GA, Paper [9] Hebb, D.O. (1949), The organization of behavior, Wiley, New York. [10] Hopfield, J.J. (1982), Neural network and physical system with emergent collective computational capabilities, In proceedings of national academy of science (USA), 79, pp [11] Kohonen, T. (1972), Correlation matrix memories, IEEE Transactions on Computers, 21, pp [12] Lin, S.C. and Chang, M.F. (1998), A study on the effects of vibrations on the surface finish using a surface topography simulation model for turning, International Journal of Machine Tools and Manufacture, 38, pp [13] Lin, J.T, Bhattacharya, D. and Kecman, V. (2003), Multiple regression and neural networks analyses in composite machining, Journal of Composite Science and Technology, 63, pp [14] Malburg, M.C., Raja, J. (1993), Characterization of surface texture generated by plateau honing process, CIRP Annals, 42 (1), pp [15] McCulloch, W. and Pitts, W. (1943), A logical calculus of the ideas imminent in nervous activity, Bulletin of Mathematical Biophysics,.5, pp [16] Munoz-Escalona, P and Cassier, Z. (1998), Influence of the critical cutting speed on the surface finish of turned steel, Wear,.218, pp [17] Ogodorov, V.A. (2008), Identification of honing processes, Russian Engineering Research, 28, pp editor@iaeme.com

11 Artificial Neural Network Modeling and Optimization In Honing Process [18] Ozcelik, B. and Bayramouglu, M. (2005), The statistical modeling of surface roughness in high speed flat end milling, International Journal of Machine Tools and Manufacture, 46, pp [19] Ozel, T. and Nadgir, A. (2002), Prediction of flank wears by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools, International Journals of Machine Tools and Manufacture, 42, pp [20] Patrikar, R.M., Ramanathan, K., and Zhuang, W. (2002), Surface roughness modeling with Neural Networks, Proceedings of 9 th International Conference on Neural Information Processing, 4, pp [21] Rangwala, S.S. and Dornfeld D.A. (1989), Learning and optimization of machining operations using computing abilities of neural networks, IEEE Trans Systems, Man Cybernetics, 19, pp [22] Rumelhart, D.E. and McClelland, J. (1986), Parallel distributed processing, MIT Press, Cambridge, Mass. [23] Smith, A.E. and Mason, A.K. (1997), Cost estimation predictive modelling: regression versus neural network, The Engineering Economist, 42(2), pp [24] Wang, M.Y. and Chang, H.Y. (2004), Experimental study of surface roughness in slot end milling, International Journal of Machine Tools and Manufacture,.44, pp [25] Sweety H Meshram, Vinaykeswani and Nileshbodne, Data Filtration and Simulation by Artificial Neural Network, International Journal of Electronics and Communication Engineering & Technology, 5(7), 2014, pp [26] Nikhil Balkrishna Bole and Rohansing R. Kait, Experimental Verification of Honing Process Parameters on Surface Roughness with Form Talysurf 120 Machine In Taper Bearings, International Journal of Mechanical Engineering and Technology, 5(1), 2014, pp [27] Zhou, X. and Xi, F. (2002), Modeling and predicting surface roughness of the grinding process, International Journal of Machine Tools and Manufacture, 46, pp editor@iaeme.com