PREDICTION MODELLING FOR THE REMAINING USEFUL LIFE OF WORN TURNING OF EN24 STEEL USING REGRESSION AND ANN

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 8, August 2017, pp , Article ID: IJMET_08_08_034 Available online at aeme.com/ijmet/issues.asp?jtype=ijmet&vtyp pe=8&itype=8 ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed PREDICTION MODELLING FOR THE REMAINING USEFUL LIFE OF WORN TOOL IN TURNING OF EN24 STEEL USING REGRESSION AND ANN Murugesan R and Krishnan R Department of Mechanical Engineering, SRM University, Kattankulathur, Kancheepuram, Tamil Nadu, India ABSTRACT Surface roughness is one of the factors that influence the quality of the machined product. In industries, the cutting tools are replaced due to poor surface finish. The roughness of the machined products depends on the tool wear and cutting parameters. Most of the tools are subjected to sub-optimal replacement and are not utilized to their maximum capacity. Such underutilization of cutting tools is having a significant impact on the machining cost. Hence, prediction of the remaining useful life of a used tool has a greater influence in reducing the machining cost and increasing the productivity. The present work focuses on predicting the remaining useful life of the carbide tool used in the turning of EN24 steel using regression and Artificial Neural Network (ANN) models. Turning experiments were conducted for the selected work- The tool wear tool combination at three levels of cutting speed, feed and depth of cut. and the surface roughnesss are considered as response variables. The results obtained from the prediction models have been validated against the set of validation experiments to check the reliability and accuracy of the models. Keywords: Artificial neural network; Regression; Remaining useful life; Surface roughness; Turning. Cite this Article: Murugesan R and Krishnan R, Prediction Modelling for the Remaining useful Life of Worn Tool in Turning of En24 Steel using Regression And Ann, International Journal of Mechanical Engineering and Technology 8(8), 2017, pp MET/issues.asp?JType=IJMET&VType=8&ITy ype=8 1. INTRODUCTION Machining operations form the core of the manufacturing industry and the demand for good quality product increases in the market. So the industries are left with the challenge of increasing the productivity as well as to maintain the quality of the manufactured products. During machining processes, cutting tools are subjected to wear, the friction between the editor@iaeme.com

2 Murugesan R and Krishnan R workpiece and the cutting tool materials results in progressive loss of materials in cutting tool. Thus tool wear can be defined as change in the tool s shape as a result of the gradual loss of tool material during machining processes. Tool wear is one of the important factors in metal cutting process and they greatly influence the quality of the manufactured product [1]. Tool wear depends on various parameters such as cutting tool material, workpiece material, cutting speed, depth of cut, feed rate, lubrication, temperature, etc. Optimal process parameters should be chosen to minimize the tool wear and to improve the quality of the machined product. The consequences of the tool wear are poor quality of the component, poor surface finish, increase in cutting force while machining, increased tool vibration, increase in temperature during machining, low dimensional accuracy of the product and increase in production cost [2]. Turning is one of the widely used material removal processes. This process can be done with the single point cutting tool as well as the cutting inserts. In industries, the tools are replaced when the required surface finish is not obtained. Non-optimal utilization of the tool has a great cost impact on the industries. Predicting the tool life helps in utilising the tool to the maximum. Hence developing a reliable model for the tool life estimation contributes to the safe and reliable reuse of the worn tool. This in turn contributes significantly to the cost reduction and increased productivity [3]. M. Aramesh et al. [3] estimated the remaining useful life of worn cutting tools in turning of Ti-MMCs, using worn tool flank wear value as input data. They developed a proportional hazardous model (PHM) with a Weibull baseline, to find the remaining life of inserts for different cutting parameters. Jaydeep M. Karandikar et al. [4] characterized tool wear by remaining useful tool life in a turning operation and predicted it using spindle power and a random sample path method of Bayesian inference. Amit Kumar Jain and Bhupesh Kumar Lad [5] proposed Artificial Neural Network (ANN) model to predict the remaining life of high speed milling cutters. The objective of the present work is to develop a reliable and valid model to predict the remaining useful life of carbide inserts during the turning of EN24 steel under different cutting parameters which helps in utilising the tool to its maximum capacity and reduce the machining cost. Maximum surface roughness is considered as tool life criterion. Regression and ANN models are developed for predicting the remaining useful life (RUL) and validated against the experimental results. 2. EXPERIMENTAL PROCEDURE Depending on the purpose of study, the controllable variables, monitoring variables and the failure criteria are to be defined. In this study, cutting speed (v), feed (f) and depth of cut (d) are the controllable parameters. The surface roughness (Ra) of the machined component and the tool wear (w) are the variables to be monitored. The surface roughness value is considered as the failure criterion. When the roughness of the machined component is above the desired roughness value, the tool is considered as failed. The various levels of the controllable variables should be fixed and the experiments are carried out based on design of experiments. All the experiment should be continued till the tool reaches the failure criterion. The surface roughness (Ra) and tool wear (w) should be measured at regular intervals. Each experiment should be started with a new cutting edge and continued till the roughness of the machined surface is above the desired value. Data layout based on the collected experimental data is used for the selection of factors and factor levels Cutting speed, feed and depth of cut are the controllable parameter. Trial experiments were conducted to finalise the levels of parameters. Based on the experimental results, selected levels of each factor are given in Table editor@iaeme.com

3 Prediction Modelling for the Remaining useful Life of Worn Tool in Turning of En24 Steel using Regression And Ann Table 1 Factors and Factor levels Factors Low Medium High Cutting Speed(m/min) Feed (mm/rev) Depth of cut (mm) The Standard L 9 (3 3 ) orthogonal array shown in Table 2 is selected for designing the experiments. This OA gives 9 different combinations of the 3 selected factors, cutting speed (v), feed (f) and depth of cut (d). Table 2 L 9 (3 3 ) Orthogonal Array Exp No. Cutting Speed v (m/min) Feed f (mm/rev) Depth of cut d (mm) Experimental setup Experiments were conducted in a CNC Lathe machine (JOYTI DX150) having a spindle speed range of 50 to 4500 rpm. The maximum cutting length and turning diameter of the machine is 350mm and 50mm respectively. Machining is done on EN24 steel rod with CVD coated carbide insert. The dimension of work piece used for the current study is Ø mm. The Insert used for turning, CJ225P (KYOCERA) having 6 cutting edges with a nose radius of 0.8mm is shown in Fig. 1.Tool holder used to hold the insert is TNMG Figure 1 CVD Coated Carbide Insert A workpiece specimen of dimension Ø70 20 mm is used to test the hardness and it is found to be 210BHN. The same specimen is used to analyse the chemical composition and the result is given in Table 3. Table 3 Chemical Composition of EN24 Steel Material Fe C Cr Ni Mo Si Mn S P Composition(%) editor@iaeme.com

4 Murugesan R and Krishnan R A Mitutoyo TM510 tool maker s microscope shown in Fig.2 is used to measure the flank wear of the tool. SURFCOM 1400G is used to check the surface roughness (Ra) of the machined component. Figure 2 Mitutoyo TM510 Toolmaker s Microscope In this study, surface roughness (Ra) of 0.6µm is considered as failure criterion. When the machined part has roughness Ra greater than 0.6µm, the tool is considered as failed. Fig. 3 shows the turning of EN24 steel rod using coated carbide insert. The tool wear (w) and surface roughness (Ra) were recorded at regular intervals for each experiment. Table 4 shows the cutting length, tool wear and machining time up to failure criterion. Figure 3 Turning of EN24 Steel rod editor@iaeme.com

5 Exp No. Prediction Modelling for the Remaining useful Life of Worn Tool in Turning of En24 Steel using Regression And Ann 3. PREDICTION MODELS Table 4 Measured cutting length, tool wear and machining time up to failure criterion (R a =0.6µm) Cutting Speed v (m/min) Feed f (mm/rev) Depth of cut d (mm) Wear w (mm) Cutting Length upto corresponding wear (mm) 3.1 Regression Model A multiple regression model is developed to predict the remaining useful life of a worn insert for different cutting parameters. The function to determine the remaining useful life can be expressed as: RUL = α 0 + α 1.v + α 2.f + α 3.d + α 4.w (1) where RUL is the remaining useful life, v, f, d and w are the cutting speed, feed, depth of cut and tool wear respectively. α 0, α 1, α 2, α 3 and α 4 are the regression coefficients to be determined. Minitab 15 is used to determine the coefficients of the regression model. The final model obtained after substituting the regression coefficients is given in Equation 2 RUL = v f d w (2) The results obtained from the Minitab are shown in Table 5 and Table 6. The Residual plot obtained is shown in Figure 4. Table 5 Results From Minitab Surface Roughness Ra ( µm) Predictor Coefficient SE Coefficient T P Constant Speed Feed Depth Wear S = R 2 = 83.1% Adj. R 2 = 77.9% Machining time (secs) editor@iaeme.com

6 Murugesan R and Krishnan R Source Table 6 ANOVA of the Regression Model Degree of freedom Sum of Squares Mean Square Regression Residual Error Total F Figure 4 Residual plot for RUL 3.2 ANN Model Artificial Neural Network (ANN) is a computational method. It works the same way as the brain does to solve the problem. An artificial neural network is an interconnected group of nodes, similar to the network of neurons in a brain. It has a large collection of neural units similar to that of the biological neurons and there are lot of connections established between them. Feed forward neural network, Radial Basis Function (RBF), Associative Neural Network (ASNN), Neuro-Fuzzy network are some of the types of networks. Artificial neural network have properties of learning capability, adaptation and working with a few data with high speed. The selection of the neuron number, hidden layers, activation function and training algorithm are very important to obtain the best results. MATLAB is used for the development of the ANN model. In this study, a Feed forward back propagation neural network model is developed to predict the remaining useful life of the tools. The Structure of Neural Network model consists of input, hidden and output layers shown in Figure editor@iaeme.com

7 Prediction Modelling for the Remaining useful Life of Worn Tool in Turning of En24 Steel using Regression And Ann Figure 5 Proposed ANN Model A logsig transfer function is used in the hidden as well as output layer. The cutting speed, feed, depth of cut and tool wear were fed into the model as input. The output from this model is the RUL of the tool MATLAB neural network training (nntraintool) tool is used for training the model. Trial and Error search method is used to select the best configuration for the model [5]. Gradient descent back propagation with adaptive learning rate (traingdx) method is used to train the model and the hidden layers have eight neurons. The ANN model designed in MATLAB is shown in Figure 6. Figure 6 ANN Modelling in MATLAB 4. RESULTS AND DISCUSSION In this study, the experimental results shows that the life of the tool depends on selection of optimum machining parameters like cutting speed, feed and depth of cut. In current scenario, the worn tools were replaced when the surface roughness (Ra) of the machined product is more than the desired level. Figure 7 shows that the surface roughness against machined length for the conducted experiments editor@iaeme.com

8 Murugesan R and Krishnan R Surface Roughness vs Machined Length Surface Roughness (µm) EXP 1 EXP 2 EXP 3 EXP 4 EXP 5 EXP 6 EXP 7 EXP 8 EXP 9 Machined Length (mm) Figure 7 Surface Roughness curves of the conducted experiment It is inferred from the experimental results that the depth of cut (d) is the most influencing factor for surface finish. When the depth of cut is high, the surface roughness (Ra) is high even during the early stages of tool wear. Optimum level of depth of cut results in better surface finish for a long cutting period. Next to depth of cut, feed rate (f) has influence on the surface finish. When the feed rate is high, it leads to high surface roughness. Performing the machining in the range of constant cutting speed (s) specified for the tool has the least affect on the surface finish. A Regression and ANN model was developed to predict the remaining useful life (RUL). RUL refers to the distance a worn tool can machine more with particular cutting parameters until the failure criterion (R a = 0.6µm). The reliability of the obtained regression model is tested using statistical analysis of variances (ANOVA). The R 2 value shows the statistical measure of closeness of the data to the fitted regression line. Figure 7 Predicted RULs VS Actual RUL (Exp. 1-5) Figure-8. Predicted RULs VS Actual RUL (Exp. 6-10) editor@iaeme.com

9 S. No Prediction Modelling for the Remaining useful Life of Worn Tool in Turning of En24 Steel using Regression And Ann Cutting Speed v (m/min) Feed f (mm/rev ) Table 7 Actual and Predicted RUL values Depth d(mm) Wearw (mm) Obser ved RUL (mm) Regression Predicted RUL (mm) Regres sion Error % ANN Predicted RUL (mm) ANN Error % Figure 9 Predicted RULs VS Actual RUL (Exp ) Figure 10 Prediction Error Plot 5. CONCLUSION In this study, a regression and ANN model for the prediction of the remaining useful tool life of used tools in the turning of EN24 steel was developed. L9 (3 3 ) orthogonal array was used to design the experiments. The experimental results were used to develop the regression model. The remaining useful life of tool can be estimated for different cutting conditions by using the tool wear and cutting parameters such as cutting speed, feed and depth of cut as input data. Therefore by giving input as tool wear, the remaining useful life under any desired cutting parameters could be estimated. The RUL values predicted by the two models had a good agreement with the observed RUL values. Comparison of the models revealed that the ANN model is more accurate and reliable than the regression model. ACKNOWLEDGMENT The authors would like to thank the Department of Mechanical Engineering, SRM University, Kattankulathur, Kancheepuram, India for their support editor@iaeme.com

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