[2014] Prediction of Vibrations of Single Point Cutting tool using ANN in Turning

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1 Prediction of Vibrations of Single Point Cutting tool using ANN in Turning Authors Matin A. Shaikh 1, L. B. Raut 2 1,2 Department of Mechanical Engineering, SVERI s College of Engineering Pandharpur. -matinshaikh.786@gmail.com, lbraut@gmail.com ABSTRACT The objective of this work is to develop a model to simulate the vibrational effects of rotating machine parts on the single point cutting tool. In this paper experimental studies were performed on turning process & vibration is measured with the help of accelerometer along with a device called as Fast Fourier Transformer (FFT) Analyzer. The vibration of single point cutting tool is sensed by accelerometer located on the tool-post of lathe machine. The accelerometer will send the sensed vibration to FFT Analyzer which can be convert the sensed data by using accelerometer shown in PC such as frequency, Amplitude, displacement & so on. The obtained experimental data given to an Artificial Neural Network (ANN) in Matlab, with the help of experimental data ANN is to be trained. And by using ANN can predict the vibrations by changing parameters of turning such as spindle speed, feed & depth of cut. This model of ANN can be predict vibrations of single point cutting tool to avoid the failure of cutting tool. Keywords- Vibration, Cutting tool, Turning, ANN, Prediction. INTRODUCTION` Much emphasis has been placed upon vibrations in machine tools during recent years because many people have recognized that accuracy, surface finish and, last but not least, production costs are considerably influenced by them. Today an arsenal of sophisticated instruments is available for the investigation of machine tool vibration. However, in the final analysis, the finished surface itself will reflect the dynamic behaviour of the machine tool. Cutting tool have always vibrated and will continue to do so. We strive to measure these vibrations and keep them at or below a tolerable level. While higher cutting speeds generally contribute to an improvement of the surface finish obtained, but they increase the vibrations of machine & often excite components of the machine tool at their natural frequency. The exciting force is trying to cause vibration of cutting tool. If the vibrations are increased then the failure of cutting tool occurred. Matin A. Shaikh, L. B. Raut IJMEIT Volume 2 Issue 7 July 2014 Page 643

2 The failure of cutting tool results wastage of time, money etc... In metal cutting operation our goals to increasing productivity, reliability and quality of work piece. So through prediction of vibration of cutting tool by using some developed artificial neural network. applied and respective vibration is measured with the help of accelerometer and FFT Analyzer. 2. EXPERIMENTAL SET-UP AND INSTRUMENTATION There are many parameters which affect the vibrations of cutting tool. In this experimental study, Fig. no. 1 Actual Photograph of test rig with FFT the structural parameters for the machine tool Analyzer variables are constant for every experiment and also all the experiments have been completed on the same machine tool. Similarly, cutting tool parameters are constant because the cutting tool used has the same characteristics. Also the cutting parameters have been reduced to three to simplify matters. Variable cutting conditions have been selected such as listed in Table 1. In this study 31 different cutting conditions have been considered. The whole work was done on Conventional lathe machine. The work piece material used was EN 8 Fig. no. 2 Top view of test rig with accelerometer and the tool used was TiN coated carbide insert. The cutting conditions was carried out without En8 has a hardness of 35 HRC. It is mainly used for coolant and totally 31 experiments were performed engine shafts, studs, connecting rods, dynamo and according to full factorial design. The photograph of motor shafts etc. the workpiece material in tests was experimental test rig is shown in fig. no. 1 and fig. selected to represent the major group of workpiece no. 2. The vibration parameter generally depend on materials used in the industry. The specimen was the manufacturing conditions like feed, depth of cut, cylindrical bar with 40mm diameter. After cutting speed, machine tool, and cutting tool rigidity removing the surface imperfections on the etc. In this study three main cutting parameters, feed, workpiece the 31 different cutting conditions cutting speed and depth of cut was used. Three cutting parameters for each factor were used Matin A. Shaikh, L. B. Raut IJMEIT Volume 2 Issue 7 July 2014 Page 644

3 because the considered variables are multi-level variables and their outcome effects are not linear. Table no. 1 shows the full experimental data. The Vibration graph shown by the FFT Analyzer at Spindle speed 47rpm, feed 0.85mm/revol., depth of cut 0.25mm is shown in fig. no. 3 and at spindle speed 196rpm, feed 0.85mm/revol., depth of cut 0.5mm is shown in fig. no. 4. Fig. no. 3 Vibration graph at Spindle speed 47rpm, 0.85mm/revol., depth of cut 0.25mm. Fig. no. 4 Vibration graph at Spindle speed 196rpm, 0.85mm/revol., depth of cut 0.5mm. Table no. 1 Full data to design network Test no. Cutting Speed (rpm) Feed (mm/revol.) Depth of cut(mm) (mm/sec2) Matin A. Shaikh, L. B. Raut IJMEIT Volume 2 Issue 7 July 2014 Page 645

4 3. ARTIFICIAL NEURAL NETWORK Artificial neural networks are information processing systems, and since their inception, they have been used in several areas of engineering applications. ANNs have been trained to solve nonlinear and complex problems that are not modeled mathematically. ANNs eliminate the limitations of the classical approaches by extracting the desired information using the input data. Applying ANN to a system needs sufficient input and output data instead of a mathematical equation. Furthermore it can continuously retrain for new data during the operation, thus it can adapt to changes in the system. Artificial Neural Networks are non-linear mapping structures based on the function of the human brain. They are powerful tools for modelling, especially when the underlying data relationship is unknown. ANNs can identify and learn correlated patterns between input data sets and corresponding target values. After training, ANNs can be used to predict the outcome of new independent input data. ANNs imitate the learning process of the human brain and can process problems involving non-linear and complex data. In this work, artificial neural network model have been developed to predict vibrations in the machining of EN8 material. is shown in Table no. 3. Next the number of nodes in hidden layers is being taken 2. The Levenberg- Marquardt training algorithm was found to be the best fit for application because it can reduce the MSE to a significantly small value and can provide better accuracy of prediction. The transfer function, training function, learning function and performance functions used in this study are tansig, trainlm, learngdm and MSE respectively. So a network of 3 input nodes, 2 hidden nodes and 1 output node is created, so network is structured. So neural network model with feed forward back propagation algorithm and Levenberg-Marqudt approximation algorithm was trained with data collected for the experiment. The neural network has training window is shown in fig. 5. The effectiveness of ANN model is fully depends on the trial and error process. The regression graph shown by the modelled network is shown below fig PROCEDURE FOR PREDICTION The experiment data is divided in to test data set. Test data is used to check the behaviour of the ANN model created to fit the sample of 31; preferred ratio selected is 9:22. The training data to train the network is shown in Table no. 2, as well as test data Fig. no. 5 NN training tool Matin A. Shaikh, L. B. Raut IJMEIT Volume 2 Issue 7 July 2014 Page 646

5 Fig. no. 6 Regression graph After training the network considering above explained all parameters, the network is test with test data. The graph between the actual and predicted values has also been plotted. And from the graph it is clear that the actual and predicted results come to a very close value. 5. RESULT & DISCUSSION After training the network the results shows that the training data and the predicted training data has come to a very close value. The graph shows the result of the training data of the actual value with the predicted value. For training data actual value & predicted value are compared shown in fig. no. 8. The test result also shows that the actual and predicted values are almost equal & that is compared shown in fig. no. 7. The test data has predicted values are shown in Table no.4. And the errors obtained in this model because of weights required training the ANN model & these weights NNTOOL taken randomly, this is trail error method we can t change the weights. Table no. 2 Training data for network Matin A. Shaikh, L. B. Raut IJMEIT Volume 2 Issue 7 July 2014 Page 647 Test no. Cutting Speed (rpm) Feed (mm/revol.) Depth of cut (mm) Table no. 3 Testing data for network Test no. Cutting Speed (rpm) Feed (mm/revol.) Depth of cut (mm) (mm/sec2) (mm/sec 2 )

6 IJMEIT// Vol. 2 Issue 7 //July 2014 //Page No: //ISSN x The error is calculated using absolute percent error given by the relation, Fig. no. 7 Comparison of Actual & Predicted vibrations of Test data Fig. no. 8 Neural Network training graph Table no. 4 Error between Actual value & Predicted value Experimental Number of readings Predicted Number of readings Experimental Predicted Test Actual Predicted % Error no. Value Value CONCLUSION From the results it can be easily seen that the minimum error obtained for the predicted value of test data. This study concludes that the model of ANN can be predict the vibrations of single point tool at any three parameters such as spindle speed, feed & depth of cut. And this predicted value is nearly equal to actual value of vibrations. So with the help of ANN model we can easily predict the vibrations of single point cutting tool without any experiment. And effectiveness of ANN model can be improved by modifying the number of layers and nodes in the hidden layers of the ANN network structure. REFERENCES 1. D. J. Kim, B. M. Kim has present Application of neural network and FEM for metal forming process.sep W. S. Lin, B. Y. Lee, C. L. Wu has presented Modeling the surface roughness and cutting force for turning.april Thomas M. Beauchamp Y, Youssef A. Y. Masounave J. Effect of tool vibrations on surface roughness during lathe dry turning process. 4. F. M. Longbottom and F. D. Lanbam Cutting temperature measurement while machining. Matin A. Shaikh, L. B. Raut IJMEIT Volume 2 Issue 7 July 2014 Page 648

7 5. Bahaa Ibraheem Kazem, Nihad F.H. Zangana, A Neural Network based real time controller for turning process. ISSN , Sep Alex Sohn, Lucas Lamonds and Ken Garrard, Modeling of Vibration in singlepoint diamond turning Dennis H. Shreve, Introduction to vibration technology IRD Mechanalysis, Inc. Columbus, Ohio 43229, November Ashvin P. Yajnik, Vibration in machine tools. 9. L. Håkansson, I. Claesson and P.-O. H. Sturesson, Adaptive Feedback Control of Machine-Tool Vibration based on The Filtered-x LMS-algorithm. 10. A. Bhattacharyya, Metal cutting theory & practice, New central book agency (P) ltd. 8/1 Chintamoni das lane, Kolkata India. 11. Amitabh Ghosh, Ashok Kumar Mallik, Manufacturing Science Affiliated East- West press private limited New Delhi. 12. G.R.Nagpal, Machine tool engineering Khanna publishers. 13. Serope Kalpakjian, Steven R. Schmid, Manufacturing Processes PEARSON. 14. Bhandari V.B, Design of Machine Elements, Tata McGraw-Hill Publishing Company Limited New Delhi, 2004, pp Ghosh and Mallik, Theory of Mechanism and Machines, East-West Press Private Limited New Delhi India pp Matin A. Shaikh, L. B. Raut IJMEIT Volume 2 Issue 7 July 2014 Page 649

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