Central Manufacturing Technology Institute, Bangalore , India,

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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 Investigation on the influence of cutting parameters on Machine tool Vibration & Surface finish using MEMS Accelerometer in high N.Kusuma *, Megha Agrawal, P.V.Shashikumar Central Manufacturing Technology Institute, Bangalore , India, Abstract The purpose of this research is to investigate on the influences of cutting parameters on machine tool vibration & surface finish using MEMS Accelerometer in high. The cutting parameters considered are depth of cut, feed rate and spindle speed. In this work, efforts has been made to acquire vibration data on spindle housing using MEMS Accelerometer, measure surface finish and analyse the influence of cutting parameters on machine tool vibration and surface finish using ANOVA technique and also predict the surface roughness using ANN. Here the ANOVA results for full factorial and taguchi design of experiments techniques has been compared and found that taguchi design of experiment is better and reliable to obtain optimal number of experiments. Further the cutting parameters are optimised using genetic algorithm approach, which are required to be sent to CNC machine to improve the surface roughness and control vibration. Keywords: MEMS, Cutting Parameters, ANOVA, Artificial Neural Network (ANN). 1 Introduction: During cutting process in a milling machine, vibration is frequent problem which affects dimensional accuracy of the parts being machined, surface finish and tool life. Vibrations are induced due to machine faults, cutting parameters, cutting tool, work piece deformation, etc. These vibrations are generally collected using accelerometers by mounting on various machine tool elements. In the present technology, there is lot of demand for using micro or MEMS accelerometers in place of normal accelerometers especially in micro and nano machines. To adapt latest technology MEMS accelerometer is used in this present work, these MEMS Accelerometers are of micro size, low cost, low power consumption, easy to integrate into systems. Surface quality is one of the most important requirements in manufacturing industries which is indicated by surface roughness. Surface roughness is mainly affected by cutting conditions (depth of cut, spindle speed, feed rate, etc), process parameters such as tool geometry (tool nose radius, rake angle, edge geometry, etc) and also machine tool vibration. To improve the surface roughness, it is very essential to know the influences or significant effects of cutting parameters on surface roughness and machine tool vibration. The high quality performance and improved production cost in milling machine may be obtained by improving surface roughness. This is achieved by controlling the cutting parameters in the CNC machine. Controlling of cutting parameters in standard CNC machines is very challenging task as CNC control systems are in closed loop environment. The purpose of this research is to investigate on the influences of cutting parameters on machine tool vibration & surface finish. A survey has been made and many researchers have attempted to construct several mathematical models based on statistical regression and artificial neural network to find the relationship between cutting parameters, surface roughness and vibration and also attempt has been made to predict the surface roughness and optimise the cutting parameters. A brief review of the literature survey of the same is highlighted below. Tug rul O zel, Yig it Karpat (2005), used neural network modelling to predict surface roughness and tool flank wear for different variety of cutting conditions in hard turning machine and found that decrease in the feed rate and increase in cutting speed results in better surface roughness. Also increase in work piece hardness resulted in better surface roughness. Durmus Karayel (2009), used ANN method for Prediction and control of Surface roughness in CNC lathe. Control of Surface roughness is done by sending the surface roughness value as feed back to CNC system until the difference 375-1

2 Investigation on the influence of cutting parameters on Machine tool Vibration & Surface finish using MEMS Accelerometer in high between desired and measured value is acceptable for machining accuracy. K.A. Risbood, U.S. Dixit, A.D. Sahasrabudhe (2003), developed ANN models for predicting surface finish and dimensional deviation by measuring cutting forces and vibration and found that turning with carbide tool improves the surface finish with increase in feed. P.G. Benardos, G.C. Vosniakos (2002), developed a neural network modelling for the prediction of surface roughness in CNC face milling machine with Taguchi design of experiments and the most influential factors were found to be feed rate per tooth, Fx component of cutting force, depth of cut. P.Palanisamy, I.Rajendran, S.Shanmugasundaram (2007), have developed mathematical model based on both the material behaviour and the machine dynamics to determine surface roughness and cutting force for milling operations using genetic algorithm. They found that end mill tests done on mild steel gives maximum material removal rate and less amplitude of vibration with optimal cutting parameters. 2 Methodology: The methodology adopted in this work involves conducting experiment on milling machine based on design of experiment (DoE), machine tool vibration data acquisition using MEMS accelerometer, measurement of surface roughness, analysis using analysis of variance (ANOVA) and investigation on influence of cutting parameters on the vibration data and surface roughness values, prediction of surface roughness value, and optimisation of cutting parameters required for adaptive control of cutting parameters using artificial neural network and genetic algorithm approach. 2.1 Design of Experiment (DoE): There are various ways in which design of experiments may be designed and it always depends on the number of factors and number of levels in each factors. Two methods of DoE are full factorial DoE and Taguchi DoE. Full factorial DoE: A full factorial design of experiment consists of two or more factors, each with discrete possible values or "levels", and experiments are performed for all possible combinations of these levels across all such factors. This experiment allows us to study the effect of each factor on the response variable, as well as the effects of interactions between factors on the response variable. Full factorial DoE was designed initially in the presented work considering three cutting parameters or factors such as depth of cut (DoC), feed rate (FR) and spindle speed (SS) with three levels of operation for each factor and the response variables are vibration and surface roughness. The number of levels for each factors considered in this DoE is as shown in Table-1: Table-1: Full Factorial DOE factors and its levels Variables or parameter Level 1 Level 2 Level 3 Depth of Cut (mm) Feed Rate (mm/rev) Spindle Speed (mm/min) If Y = -of-factors, x = of levels of each factor Total number of expts = Y x = 3 3 Therefore total number of expts = 27 experiments Taguchi Design of Experiment: As the number of experiments were too many in full factorial design which involves more machining time and cost, DoE was applied using Taguchi design to get an optimal number of experiments thereby reducing the machining time and cost involved. Taguchi method uses a special set of array called orthogonal array. In general, according to orthogonal array, the number of degrees of freedom is equal to the number of factor multiplied by number of levels for that factor minus one. As we have considered three cutting parameters with 3 levels each. Total degree of freedom = 3 x (3-1) = 6 However according to orthogonal array properties best suitable / closest array chosen is L 9 array for three factors with 3 levels. Therefore total no of experiments using Taguchi L 9 = 3 x 3 = 9 experiments. Table-2 shows the 27 experiments to be conducted for full factorial DoE and possible 9 combinations of experiments chosen for Taguchi DoE (grayed). Table-2: Full Factorial & Taguchi (grayed) Design of Experiment. mm mm/min SS RPM. mm mm/min SS RPM

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 Table-2: Full Factorial & Taguchi (grayed) Design of Experiment, Continued. mm mm/min SS RPM. mm mm/min SS RPM Experimentation: As per full factorial and Taguchi DoE method, experiments were conducted on high (make: Schaublin) with Siemens 840D controller. A micro Piezo electric accelerometer with built-in signal conditioning was mounted on the spindle housing to acquire the vibration data. FFT analyser was used to convert time domain vibration data into frequency domain data and data recorder for recording purpose as shown in the Figure-1. Figure-1: Experimental Setup Material used is Aluminium and tool used is 16 teeth end mill of 10mm diameter. After each experiment, surface roughness was measured using surface finish & form measuring machine (make: Talysurf). The vibration data acquired comprised of machine related and cutting process related vibration. Prior to conducting the experiments, good conditioned milling machine was selected without any machine faults such as imbalance, misalignment, mechanical looseness, without bearing and gearbox faults, hence negligible machine related vibration data were noticed during data analysis and noise were eliminated upto 5Hz and highest peak value of remaining data were considered upto 10kHz as cutting process related vibration in this work. 2.3 Results and discussions: ANOVA Analysis: Vibration data and surface roughness values were analysed using Analysis of Variance (ANOVA) method to understand the influences of the cutting parameters on surface roughness R a and vibration. Cutting parameters such as depth of cut, feed rate, and spindle speed were considered as input, vibration and surface roughness R a were considered as output parameters during this ANOVA analysis. In the ANOVA results, F-test values were used at 95 confidence level to decide the significant factors affecting the process and percentage contribution. As per ANOVA analysis, for a particular cutting parameter the p value less than 0.05 (5) and larger F value indicates that the statistically significant effects on the performance of that parameter. The ANOVA results for full factorial design for vibration and surface roughness are as shown in Table-3: Table-3: ANOVA results for Full Factorial Vibration and Surface Roughness DoC DoC E FR FR SS E SS Error Error Results of full factorial DoE: From the above Tables-3, it is clear that cutting parameter feed rate has no significant effect on both vibration and surface roughness R a, spindle speed has more significant effect on vibration and depth of cut has more significant effect on surface roughness R a. The ANOVA results for Taguchi design for vibration and surface roughness are as shown in Table-4: 375-3

4 Investigation on the influence of cutting parameters on Machine tool Vibration & Surface finish using MEMS Accelerometer in high Table-4: ANOVA results for Taguchi DoE Vibration & Surface Roughnesss DoC FR SS Error DoC FR SS Error Results of Taguchi DoE: From the above Tables-4 it is clear that cutting parameter - feed rate has no significant effect on both vibration and surface roughness R a, spindle speed has more significant effect on vibration and depth of cut has more significant effect on surface roughness R a. Results of comparison of full factorial DoE and Taguchi DoE shows that both the cutting parameter influence results obtained for vibration and surface roughness are close to each other and there is more improvement in percentage of DoC-93 on Surface Roughness with minimum or optimal number of experiments conducted using Taguchi DoE method. Hence Taguchi DoE is best suited for conducting experiments. 2.4 Surface Roughness prediction: For improving productivity and quality, there is a need to develop a model that can precisely predict surface roughness based on cutting parameters as well as vibrations. Ideally this module should be capable of monitoring the surface finish in real time. A module was developed in Artificial Neural Network (ANN) using Matlab to predict the surface roughness. Artificial neural networks are made of arrangements of processing elements called neurons. In this work, neural network have been designed and trained to perform a particular function by adjusting the values of the connections (weights) between elements. The network is adjusted,, based on a comparison of the network output and the target, until the output matches with the target. Training of a network proceeds by making weight and bias changes based on an entire set of input vectors. Here input vectors are DoC, FR, SS and Vibration. Output vector is Surface roughness and a Multi Layer Perceptron (MLP) network is used. The artificial neuron model basically consists of a linear combiner followed by an activation function, such as purelin, logsig and tansig. Table-5: Predicted Surface roughness Figure-2: ANN model designed machine for milling In the present work, ANN model is designed as shown in Figure-2, here two hidden layers and logsig transfer function is used for both hidden layers and linear transfer function is used for output layers. The number of neurons in first and second hidden layer is 50 and 4 respectively. Initially the neural network developed is trained with few set of experimental data, i.e., by setting all the necessary parameters such as the number of input neurons, hidden layers, number of neurons in hidden layers and number of output neurons. Initial values of the weights and bias are set randomly and initial values of input vector and target output vector are loaded. The actual output vector and error terms are calculated. The above steps are repeated until the energy function is converged or specified training cycle is completely executed. Further remaining set of experimental data are used for testing purpose. ANN module is tested by setting all neural network parameters. The weight matrix and bias vectors which were set during training are read back and tested. The input vectors are loaded for testing and actual/predicted output (Surface roughness) is calculated. Table-5 gives the measured, predicted Surface roughness and its variation for the experimental setup done in milling operation. Expt Measured Predicted Expt Measured Predicted Variation Variation

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 Expt Measured Table-5: Predicted Surface roughness, Continued Predicted Variation Expt Measured Predicted Variation Figure-3: ANN model graph Results: It can be seen from the above graph in Figure-3, that in most cases, the neural network prediction is very close to the measured values i.e upto 75, to get better surface roughness prediction more number of readings need to be fed to the neural network designed during the training and testing of network. 2.5 Optimisation of cutting parameters: The selection of cutting parameters increases the product quality to a great extent by minimizing the surface roughness value. In the present experiment an effort has been made to determine the optimum values of cutting parameters to obtain the best possible surface quality within the specific range. An effective optimization method, Genetic Algorithm module is developed to get optimised cutting parameters. Genetic Algorithm (GA) is a search algorithm for optimisation, based on the mechanics of natural selection and genetics. It begins with set of bit strings called chromosomes that are randomly selected. The entire set of chromosomes creates a population. The chromosomes evolve during several iterations called generations. The new generations are generated utilizing the crossover and mutation technique. In this work Genetic Algorithm approach has been carried out in MATLAB using Optimization tool, to optimise the cutting parameters for milling operation. Following Regression equation were used as fitting function in the GA program Ra = X X X 3 Where X1= DoC, X2=FR and X3=SS Options set: Population size=20, Selection: Roulette method, Generation: 100. Range of cutting parameters were set as Upper Bound (UB) and Lower Bound (LB) in the constraint, here 3 constraints are depth of cut, feed rate and spindle speed Table-6 gives the optimized cutting parameters for the given range of cutting parameter values Table-6: Optimised Cutting parameters Exp. DoC FR Optimiz ed SS Ra

6 Investigation on the influence of cutting parameters on Machine tool Vibration & Surface finish using MEMS Accelerometer in high Table-6:Optimised Cutting parameters, Continued Exp. DoC FR SS Ra Figure-4: Fitness Curve after optimisation Results: Based on the Regression equation, the surface roughness values were fitted as shown in Figure-4, and optimised surface roughness value and cutting parameters were obtained. Upto 70 surface roughness values were closely fitted with respect to surface roughness value measured. In order to improve the surface roughness and reduce vibration which in turn improves quality of the product, the optimized cutting parameters need to be sent or communicated to the CNC control system. As the standard CNC control system works in close loop environment, the best method of sending/communicating the cutting parameters into the CNC machine is through the PLC of the CNC machine. number of experiments. Investigation on the influences of cutting parameters on machine tool vibration & surface finish in high precision CNC milling machine shows that spindle speed has more significant effect on vibration and depth of cut has more significant effect on surface roughness in milling machine. The important fact is that normal accelerometers can be replaced by MEMS accelerometers to mount on CNC machine tool their by miniaturizing it. Future focus of this work is to send/communicate optimised cutting parameters into the CNC machine to improve surface roughness and reduce vibration. 4 References: Tug rul O zel, Yig it Karpat (2005), Predictive modelling of surface roughness and tool wear in hard turning using regression and neural networks, International Journal of Machine Tools & Manufacture 45, pp Durmus Karayel (2009), Prediction and control of Surface roughness in CNC lathe using artificial neural network, Journal of Materials Processing Technology 209, pp K.A. Risbood, U.S. Dixit, A.D. Sahasrabudhe (2003), Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process, Journal of Materials Processing Technology 132, pp P.G. Benardos, G.C. Vosniakos (2002), Prediction of surface roughness in CNC face milling using neural networks and Taguchi s design of experiments, Robotics and Computer Integrated Manufacturing 18, pp P. Palanisamy. I. Rajendran. S. Shanmugasundaram (2007), Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations, International Journal of Advanced Manufacturing Technology 32, pp Conclusions: This work describes the method involved to acquire and analyse the vibration data, predict the surface roughness and optimise the cutting parameters on the CNC machine tool under cutting conditions such as depth of cut, feed rate and spindle speed in milling machine. Taguchi is the best Design of Experiment (DoE) method to achieve the optimum 375-6