Artificial Neural Network Modeling for Prediction of performance in Abrasive Jet Drilling Process for Glass material

Transcription

1 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Artificial Neural Network Modeling for Prediction of performance in Abrasive Jet Drilling Process for Glass material J.R.Samani 1, H.S.Beravala 2*, P.B.Jadav, C.J.Dusra 4 1,2,,4 Department of Production Engineering Birla VishvakarmaMahavidyalaya Engineering College, VallabhVidyanagar, 88120, beravalahardiks@gmail.com Abstract The prediction of process performance is important to set the control parameters for achieving the goals of production. Abrasive jet machining is a suitable process for machining of brittle materials like Glass, ceramic etc. Glass is a widely used engineering material in optical and other applications. For this purpose, full factorial design of experiment was carried out to obtain data regarding the effect of process parameters on abrasive jet drilling process for glass as work material. Attempt of this paper is to development of predictive models for input parameters which are air pressure, stand of distance and abrasive grit size and its effect on responses such as which are material removal rate, radial overcut and taper. Various ANN architecture was developed their accuracies in prediction are determine by calculating various errors and variance between actual experiment result and predicted results obtain by ANN models using 80:20 rule. Major conclusion of this paper is, artificial neural is used to capture relationship between input and output parameters so it works as a modeling tool to predict the performance of the process. Keywords: Abrasive jet drilling, Glass, Artificial Neural Network (ANN) 1 Introduction Abrasive jet machining is a process in which the material is removed from the work piece due to the impingement of the fine grain abrasives by a high velocity air jet. Material removal occurs through a chipping action, which is especially effective on hard, brittle material such as glass, silicon, tungsten and ceramics. Difference from the other non conventional machining process there is no thermal, mechanical and chemical damage of the work. Burzynski T. and Papini M. (2012) had used this technique to microfabricate array of components in glass for use in semiconductor, Micro Electro Mechanical Systems (MEMS) and optoelectronic industries. Coleman, J. R. (1981) had made attempt to cut a thread in glass rod, cutting titanium foil, and drilling glass wafers using AJM process.dombrowski, T.R. (198)had successfully employed AJM process to manufacture small electronics devices consisting silicon brazed on tungsten of varying thickness in which the silicon wafer must be trimmed and beveled without harming the tungsten disk.balasubramaniam R. (1999) had applied AJM process for debarringof crossed-drilled holes as secondary erosion. Tsai F. (2008) hadpolished electrical discharge machined mold steel at high degree mirror finish by AJM adding pure water with abrasive in specified quantity Benedict G. (1987), Verma A.(1984) and Chandra B. (2011)had foundajm processaffecting parameters on output such as stand of distance, mixing ratio, air pressure, grain size, abrasive types etc. In present study author have try to develop predictive model using most effective input parameters found in preliminary experiment such as stand of distance, air pressure and abrasive grain size. The nature of abrasive jet machining process is so complex that the selection of process parameter required lot of preliminary trials to establish the correct parameters. Anderson D. (1992), Beravala H.(2011,201), Ugrasen G. (201) and Pandey A. (201) had studied and investigated the complex relational between various machining parameter and response using artificial neural network technique for different manufacturing processes. 2 Experimental setup Figure 1 Experimental setup for Abrasive Jet Drilling Process Experimental setup for AJDP is shown in Figure 1. High pressure air from the compressor passes through 220-1

2 Artificial Neural Network Modeling for Prediction of performance in Abrasive Jet Drilling Process for Glass material dehumidifier and pressure control valve in to the mixing chamber. The abrasive particle and air are thoroughly mixed in mixing chamber and a stream of abrasive mixed air passes through a nozzle on the work piece. It causes the indentation and ultimately results into rupture of the particle from the surface cause drilling of circular hole in to the work piece. Controllable factor and their levels used in the experiments are shown in Table 1. Table 1: Controllable factor with their level in full factorial Design of Experiments Machining Parameter Stand of distance Units 1 2 mm Air pressure bar Abrasive Particle Size micron Silicon carbide used as an abrasives material and glass is selected as a work material. Material removal rate, radial overcut and taper are selected as response parameters. The material removal rate is obtained in terms of volumetric material removal rate by taking density of glass as a 2.7 gm/cc. The top and bottom diameters of each hole were measured using 0.1 micron accuracy Mitutoyo digital measuring microscope at four various locations. Average of this value is taken as the value for top and bottom diameters. Radial overcut is determined by halving the difference between the top diameter of hole and nozzle diameter was initially 0.88mm. Taper was obtained by dividing the difference between top and bottom hole diameters by twice the thickness. Radial overcut was determined by taking the difference between larger of the top and bottom diameters of hole and nozzle diameter was initially 0.88mm. All the experiments are conducted as per randomize full factorial experimental design. Experimental design and obtained results are shown in Table 2. Table 2: Design of Experiment and Results Run Stand of Abrasive Air Pressure Order Distance Particle Size MRR (mg/sec) ROC (mm) Taper (Rad) ANN Modeling for AJDP Process Among the various kinds of ANN approaches that exist, the back propagation learning algorithm, which has become the most popular in engineering applications, is selected for use in this study. Networks have one input layer, one or more hidden layer(s), and one output layer. To train & test the neural networks, input data patterns and corresponding targets are required. In developing ANN model, the data obtained by experiment tests for abrasive jet drilling of glass is utilized and processed as per shown in Figure 2.Stand of distance, air pressure, abrasive particle size are represented as input data while material removal rate, radial overcut, taper are output data. A number of architectures of feed forward back propagation type of neural network are tested for modeling of the abrasive jet drilling process parameters in this work

3 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Figure 2 feed forward Architecture with Rule and Activation function for AJDP EasyNN computer software is used for ANN modeling because of its simplicity in developing and training models. The neural network training data are obtained from the data listed in Table. These data are used for developing individual neural network models. The numbers of nodes in hidden layer are replaced by different architectures of neural networks and an appropriate architecture is determined which limits all the errors within 2%. As per the Table, beginning from one hidden layered neural network model to two hidden layer model having initially 4 nodes in the hidden layer, the number of nodes in hidden layer is increased up to 8 while monitoring the error resulting at the end of training. The criteria for the termination of training selected are permissible error and maximum number of cycles in training and validation. The limiting value for all the errors over the entire data is selected as 0.02 (2%) while the permissible error for validation sets is specified as 5% of the target value. The maximum number of training cycles is limited to for each learning set. Thus, training stops when any one of the above criteria namely, all errors being less than 0.05, all validation point within 5% of targett values being completed. The learning rate is kept as 0.6 and momentum as 0.8 for the stable learning and convergence of weights. For training and validation rule is used. Table Neural Network Modeling for AJDP Process Network Type Input for the neural Number of nodes in input layer= Number of inputs to the neural Output from the neural Number of nodes in output layer =Number of outputs from the neural Initial Number of Hidden Layers Maximum Number of Hidden Layers Initial Number of Cells in a Hidden Layer Maximum Number of Cells in a Hidden Layer Feed Forward Stand Of Distance(SOD),Pressure, Particle size MRR,Taper,, Radial Overcut ANN model architecture of threenodes in input layer, five nodes in first hidden layer and six nodes in second hidden layer and three nodes in output layer

4 Artificial Neural Network Modeling for Prediction of performance in Abrasive Jet Drilling Process for Glass material denoted by the authors as _5_6_ architecture. Total 45 different ANN architectures are tried and their results of errors in prediction with their number of learning cycle when training stopped with all errors being within 2% are shown in Table 4. Table 4.Artificial neural network architectures & corresponding training and test result for Abrasive jet drilling of glass No.of Sr. Model Avg. Min. Max. Error Error No Structure Error % Error % % rms % R σ learning cycles 1 _7_ _8_ _9_ _10_ _4_5_ _4_6_ _4_7_ _4_8_ _4_9_ _4_10_ _5_4_ _5_5_ _5_6_ _5_7_ _5_8_ _5_9_ _5_10_ _6_4_ _6_5_ _6_6_ _6_7_ _6_8_ _6_9_ _6_10_ _7_4_ _7_5_ _7_6_ _7_7_ _7_8_ _7_9_ _7_10_ _8_4_ _8_5_ _8_6_ _8_7_ _8_8_ _8_9_ _8_10_ _9_4_

5 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT 40 _9_5_ _9_6_ _9_7_ _9_8_ _9_9_ _9_10_ Results and Discussion As seen from table 4, by the principle of trial and error ANN modeling is processed in terms of determining the most suitable architecture for a given system. The R &σ test is one way of ascertaining the best. Another method is to compare the average or RMS error values. Forty five network architectures are attempted for training. Their various errors are determined and compared. It is observed that the average error, RMS error and σ values are found to be the best for _4_5_ architecture and _5_4_ has least maximum error and _6_4_ has least value of coefficient of determination(r). Amongst these three architectures models the _4_5_ architectureshown in Figure 4 is selected as a best ANN model among all tested models. Figure 4 ANN Model with architecture _4_5_ 5 Conclusions Numerous architectures are tried to develop suitable ANN model for predicting performance in terms of material removal rate, radial over cut and taper for abrasive jet drilling of normal glass. A feed forward back propagation neural with a _4_5_ configuration is found to be most suitable, fast and reliable on basis of lest RMS error, average error and standard error. This ANN model can be usedto give artificial intelligence to the machine controller for adaptive control for determining desirable output for a given input. Reference [1] BurzynskiT., PapiniM. (2012), modeling of surface evaluation in abrasive jet micromachining including particle second strikes: A level set methodology, journal of materials processing technology 212, pp [2] Coleman, J. R. (1981), Modern applications of nontraditional machining. Tooling Prod., Apr, pp [] Dombrowski, T.R. (198). The how and why of abrasive jet machining. Mod. Machine shop, Feb, pp [4] BalasubramaniamR. et al. (1999) An Experimental study on the abrasive jet debarring of crossed drilled holes, journal of materials processing technology 91, pp [5] Tsai F. et al. (2008) A taguchi and experimental investigation in to the optimal processing conditions for the abrasive jet polishing of SKD61 mold steel, International Journal of machine tools & manufacturing, pp [6] Benedict G. (1987) Non Traditional manufacturing processes Manufacturing engineering and materials processing, 19, chapter 2, pp [7] Verma A. et al. (1984) An experimental study of abrasive jet machining, International journal of machine tool design, Vol 24, No 1, pp [8] Chandra B. et al. (2011) A Study of effect of Process Parameters of Abrasive jet machining, International Journal Of Engineering Science And Technology (IJEST), vol., No. 1, pp [9] BeravalaH.et al. (2011) Modeling for Process variable prediction in Electro-chemical Machining Process using Artificial Neural Network Technique, International Conference on Precision, Meso, Micro, And Nano Engineering, Copen-7, Pune, pp [10] BeravalaH.et al (201) Modeling for process variables prediction for surface finish in abrasive jet polishing (AJP) process using artificial neural network technique. pp [11] Ugrasen G. et al. (201) Comparison of electrode wear in wire EDM for P-20,EN-19 and stavax materials using artificial neural networks International conference on Precision, Meso, Micro, Nano Engineering, pp [12] PandeyA. et al. (201) Artificial Neural Network for prediction of performance in ultrasonic drilling of glass, 2 nd international conference on industrial engineering, vol. 2, pp [1] Anderson D. and Mcneil G. (1992), Artificial Neural Networks technology, DACS state of the art report. ELIN A011, Rome Laboratory, 20 Aug