Using of Artificial Neural Networks to Predict Drill Wear in machining processes

Transcription

1 Australian Journal of Basic and Applied Sciences, 5(12): , 2011 ISSN Using of Artificial Neural Networks to Predict Drill Wear in machining processes 1 Aydin Salimi, 2 Samira Abbasgholizadeh, 3 Samad Taghizadeh, 4 Asghar Safarian 1 M.Sc of Mechanic, Lecture of Mechanic, Faculty of Engineering, Department of Mechanic Engineering, University of Payam Noor. Tehran, Iran 2 Mechanics and Industrial Engineering Department, Faculty of Engineering, Payam Noor University of Tabriz-Iran. 3 Dept. of Manufacturing Engineering, Islamic Azad University- Maragheh Branch. Maragheh, Iran. 4 Dept. of Manufacturing Engineering, Islamic Azad University- Maragheh Branch, Iran. Abstract: In machining operations a hard tool is engaged with work piece along with process. Tool is harder than work piece. However, tool wear occurrence in machining processes is inevitable. Tool wear will results in scraped parts and also it makes tool to weaken and then a tool failer will happen in the end. Therefore, an operator is needed to follow the process and change the tool when it is going to break. But this is a serious problem against automation. To create an automation system, we need to develop a monitoring system to predict tool wear rate by on-line and substitute it with an operator. In this paper by using of a wear model and experimental data and also motor current block diagram,tool wear rate in drilling process will be predicted.to investigate the results, neural network method is used.the results compared with the real data show that the neural network results have a close fitness with the real data. Key words: Monitoring, Current signal, Drill wear, Neural Network, Cutting force. INTRODUCTION Tool wear detection is one of the most important problems found during manufacturing in automated C.N.C machine tools. The reason for acquiring the drill wear information and its monitoring is to enhance the predictive capability to allow the machine operator to schedule tool change or regrind just in time to avoid underuse or overuse of tools,avoid shutdown of machines due to damage, and to minimize scrap or rework. On the other hand, drill wear affects the ability of the hole cutting system to satisfy specified performance characteristics, such as hole roundness, centering, burr formation at drill exit, and surface finish. Although, many researchers have been performed for tool condition monitoring, there is no practical method to predict wear rate in drilling process without some problems and limitations (Zhang, M.Z., Liu, Y.B., Zhou, H, 2001). Researchers use two methods for monitoring tool condition.the first is direct methods. In these methods, tool and work piece are in contact with each other. Optical methods are used in direct tool condition monitoring but, because it cannot be used in Real-time tool condition monitoring system, researchers prefer to use indirect methods to monitor tool condition along with the machining processes. Indirect methods that rely on the relationship between tool condition and measurable signals (such as force, acoustic emission, vibration, current, etc.) for detecting tool conditions have been extensively studied. Among these methods,cutting force has more accuracy because cutting force signals are measured directly on the tool, therefore the structure of machine does not affects the measured data and extracted data accuracy is higher. But on the other hand, dynamometer is needed to measure cutting force signals,therefore the measurement process cost goes high too. Also, the method based on sensing motor current is considered as one of the major methods (Xiaoli, Li. and Tso, S.K, 1999). Discussed the feasibility of the motor current sensing for adaptive control and tool condition monitoring (Jantunen, Erkki, 2002). Described the use of current signals measured from spindle motor and feed motor to estimate static torque and thrust for monitoring tool conditions. The major advantage of using the measurement of motor current to monitor tool condition in cutting processes is that fixed current sensors do not disturb machining processes, and the cost of the sensors is very low (Xiaoli Li, Shiu Kit Tso, 2000). In this paper, both force and motor current signals are used for monitoring drill wear in drilling process. Using of two methods, force and current data, would result in accurate prediction of tool wear rates. For obtaining the data, empirical models are used. First experimental data of force for a new tool are given to wear model and tool wear rates are estimated for a certain machining parameters. Then machine tool drive block diagram is used to measure motor current. Finally, all of the data are investigated by neural network method to predict tool wear rate. In this paper, PYTIA software is used to design the neural network program. Neural network program is trained by the Corresponding Author: Aydin Salimi, M.Sc of Mechanic, Lecture of Mechanic, Faculty of Engineering, Department of Mechanic Engineering, University of Payam Noor. Tehran, Iran. E-mal: salimy@pnu.ac.ir 2752

2 measured data. Then it can estimate tool wear rate for other inputs. Also tests will be run to prove the reliability of the network. Tool Wear Model: Tool wear has a nonlinear attitude in machine tools. There are many of factors that influence on tool wear such as friction, temperature, chemical materials and etc. The effective value of the factors is not known. Therefore there is no any analytical model to predict tool wear rate in machining process. For a new drill, analytical models of force prediction are developed but for a worn drill it is not easy. We suppose that the cutting force value for a new drill is F cut.as the machining process continues; tool starts to wear and becomes worn little by little. Then the sharpness of tool decreases and tool needs more cutting force to cut the work piece. As mentioned above, this extra force depends on the wear rate, friction factor, cutting length and other parameters. Total force value is shown: F Fcut kwb (1) In the formula (12), k, is a constant value, w, is wear rate and b, is cutting length. To find an empirical model for tool wear, experimental data are needed. The block diagram and state equations of wear model has been explained by these researchers (Carrillo, F.J. and Zadshakoyan, M, 1997). In this paper the so called wear model is used to find expected drill wear rate for the certain cutting force values and machining parameters. Fig.1 shows simulated tool wear diagram in MATLAB software. Fig. 1: Simulated tool wear diagram obtained from wear model for drill diameter 15.9mm, point angle, helix angle 33, pilot whole diameter 3.2 mm, speed rpm, federate.229 mm/rev. Machine Tool Drive System: In CNC machine tools, feed motor makes tool to move in relation to work piece. The motion situation depends on the current signal magnitude that feed motor needs. When drilling process is going on, tool wear happens and tool loses its sharpness. Therefore the cutting and thrust force necessary to process continuation would increase. To overcome the force necessary for cutting, motor needs higher current value to increase the power for continuing the machining process. Therefore there is a relationship between tool wear, motor current and cutting and also thrust force. Block diagram of feed drive system of drilling machine tool was simulated in MATLAB software and feed and spindle motor current changes, are obtained along with drilling process by using of this block diagram (fig.2).the block diagram of machine tool drives have been explained and developed by some researchers (Ebrahimi, M.whalley, R, 2000). Experimental set-up: The most commonly used drill is the conventional conical point drill and in this paper The experiments are done for this kind of drill only (fig.3). Drilling experiments were conducted on an OKUMA (MC-4VAE) CNC machining center a kistler (9273 A). Four-channel dynamometer was used to measure the thrust and torque forces. Material used is gray cast iron. Data has been sampled at 100Hz and stored in a pc. Table 2 shows the thrust force and torque values for a sharp drill used in this paper (V. Chandrasekharan, S.G. Kapoor and R.E. 2753

3 Devor, 1995). The force and motor current values will be obtained by wear model and machine tool drive block diagrams. Artificial Neural Network: Artificial neural network and fuzzy logic have been one of the reliable tools to predict tool wear rate in machining process. (Xiaoli, Li., Xinping, Guan. And Hongrui, Wang, 1999) used fuzzy logic for monitoring tool wear rate.also some researchers used both of fuzzy and neural network for tool condition monitoring (Xiaoli, Li.,Yingxue, Yao. and Zhejun, Yuan,1997). Artificial Neural networks or parallel distributed processing is an alternative to sequential processing of knowledge as known from symbolic programming. In analogy to the human brain artificial neural networks consist of single units (neurons) that are interconnected by the so-called synapses. The typical network has layers of input and output units that are either connected directly or they are linked through one or several hidden layers. Each unit sends its numeric output modified by inhibitory or excitatory weights (also called transition coefficients) to another unit so that the output of a unit is the input to another or to many other units. Usually, one single unit multiplies the inputs by the individual weights and sums them up. The resulting value is the activation value of the unit which is often modified by applying an appropriate transformation function. The activation value is then preceded to other units by applying the appropriate weight. There are many useful transfer functions. Using of nonlinear transfer function makes a network capable of storing nonlinear relationships between input and output. Choice of the function depends on problem that will be solved. An important stage of a neural network is the training step, in which an input is introduced to the network together with the desired output and the weights are adjusted so that, the network attempts to produce the desired output (Adnan Sözen, Mehmet Özalp, Erol Arcaklio glu, 2004). The weights, after training, contain meaningful information whereas, before training they are random and have no meaning. If it has reached a satisfactory level, the train stops, and the network uses the weights to make decisions, to identify patterns, or to define associations in test data. Fig.4 shows the structure of an artificial neural network (ANN). Fig. 2: Simulated feed motor current diagram obtained from machine tool drive block diagram model for drill diameter 15.9mm,point angle,helix angle 33,pilot hole diameter 3.2 mm, speed rpm, federate. 229 mm/rev. Table 1: experimental results of thrust force and torque (V.Chandrasekharan, S.G.Kapoor and R.E.Devor, 1995). Speed Diameter Point Web Pilothole Cutting lips (mm/rev) (rpm) angle Thichness Diameter Thrust Torqe (nm) Total chisel Thrust torqe Entire drill Thrust Torqe (Nm) Cuttingf orce

4 Fig. 3: conical point drill. Fig. 4: structure of an artificial neural network (ANN). (Adnan Sözen, Mehmet Özalp, Erol Arcaklio glu, 2004). Modeling of Tool Wear Rate by Neural Network: As mentioned above, there is relationship between cutting and thrust forces and drill wear rate.also as the cutting and thrust force increases, motor current value increases too. For modeling this relationship, neural network is used in this paper. The transfer function used in this paper is Logistic sigmoid (logsig) transfer function: 1 f( z) z 1 e (2) Where z is the weighted sum of the input. PATIA software is used to model data. The data used in neural network obtained from tool wear and machine tool drive block diagrams. Data extracted from the simulation and used in this paper are given in table.2. Tool wear rate for sharp tool is 0 and for the worn tool it is supposed to be 0.8mm.Tool wear condition will be predicted between 0 and 0.8mm by the model. When the wear rate reaches to 0.4, the tool life reaches to half of its expected life. In neural network, there are nine inputs and one output, the output is drill wear rate and the inputs consists of machining parameters and force and motor current values. After giving data, the network is created. In the hidden layer of network, we created 12 neurons. The neurons within the hidden layer perform two tasks: they sum the weighted inputs connected to them and then pass the resulting summations through a non-linear activation function to the output neuron or adjacent neurons of the corresponding hidden layer (in case of more than one hidden neuron layer). There are 48 data to train the network. The data are given to ANN and the network is trained for the given data. The aim of ANN is to estimate interval values accurately. When the inputs are given to the ANN, the results are estimated. After a good training of ANN, the network is tested by some test data to assess the ANN accuracy. RESULT AND DISCUSSION Data are given to ANN and after training the predicted results are shown. Also, the error value for any run is obtained too. Square deviation shows the error and unfitness between neural network outputs and experimental results. R2 value of the data is obtained too. As it is given in the figure5, there is a very good fitness between neural network outputs and real data.r2 value is about which shows the accuracy of the ANN. Tool wear results obtained from the ANN and the errors for any experiment are given in table3. As it s seen in the table the maximum square deviation is about that shows the accuracy is very high. Also, variation diagram of the errors has been given in figure 6. It shows that the maximum square deviation value occurs in 45 th set and confirms the above given data. From the error diagram it is found that the error value between the wear rate of 0.2 and 0.5 is higher than others and in this part most of the data have a deviation from the zero line. In fig.7 the diagram of real wear data in relation to data number is given and in fig.8 the diagram of predicted drill wear data is created. From these two diagrams, the accuracy of ANN for prediction of the wear rate is obvious too. By attention to the diagram we find that the maximum unfitness happens in drill wear rate of 0.4 and 0.6 that confirms the results of square deviation diagram. For assessment of the accuracy of the designed neural network to predict drill wear, a test run compared with real value of drill wear. In this test the cutting force was 300N and the diagram is shown in figure5 on the right hand of the main diagram. 2755

5 Table 2: Data used for training ANN. Data rate (m/min) Cutting speed (m/min) Drill Diameter point angle (Degree) Webthicknes s pilot hole Diameter Thrust force motor Current (v) Cuttin g force motor Current (v) Flank wear Table 3: ANNs predicted data and errors Data rate(m/ min) Cutting speed (m/min) Drill Diameter point angle (Degree) Web thickness pilot hole Diameter Thrust force motor Current (v) Cutting force motor Current (v) Flank wear ANNs Predicted flank wear Square deviation (mm^2)

6 Fig. 5: Comparison of real wear and predicted drill wear rate by ANN. 2757

7 Fig. 6: Square deviation of the data from the zero line. Fig. 7: Diagram of real drill wear data. 2758

8 Fig. 8: Diagram of predicted drill wear data by ANN. Conclusion: In this paper artificial neural network was used to predict drill wear rate by using of cutting and thrust force and motor current value. We conclude that by measuring of cutting force and motor current values, estimating the drill wear rate is possible because there is a relationship between them. Also Artificial Neural Network is a reliable method to predict nonlinear incidents in machining process such as drill wear and etc. By using of ANN we can process and investigate many of the data that without using of this kind of tools accurately prediction of tool wear in machining will be impossible. REFERENCES Adnan Sözen, Mehmet Özalp, Erol Arcaklio glu, 2004." Investigation of thermodynamic properties of refrigerant absorbent couples using artificial neural networks" Chemical Engineering and Processing, 43: Carrillo, F.J. and M. Zadshakoyan, 1997." Adaptive observers for on-line tool wear estimation and monitoring in turning, using hybrid identification approach" ECC 97 European Control Conference. Bruxelles, Belgique, 1: 1-4. Chandrasekharan, V., S.G. Kapoor and R.E. Devor., 1995." A mechanistic approach to predicting the cutting forces in drilling:with application to fiber-reinforced composite materials" Journal of Engineering for Industry, l,117: Ebrahimi,M.whalley, R., "Analyses, modeling and simulation of stiffness in machine tool drives" Computer & Industrial Engineering, 38: Jantunen, Erkki., " A summary of methods applied to tool condition monitoring in drilling", International Journal of Machine Tools & Manufacture, 42: Xiaoli Li, Shiu Kit Tso, 2000." Real-Time Tool Condition Monitoring Using Wavelet Transforms and Fuzzy Techniques, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART C: APPLICATIONS AND REVIEWS, 30(3). Xiaoli, Li. and S.K. Tso, "Drill wear monitoring based on current signals", Elsevier Wear, 231: Xiaoli, Li., Xinping, Guan. And Hongrui, Wang " Identification of tool wear states with fuzzy classification", International Journal of Computer Integrated Manufacturing, 12:

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