Materials Science Forum Online: 2004-12-15 ISSN: 1662-9752, Vols. 471-472, pp 687-691 doi:10.4028/www.scientific.net/msf.471-472.687 Materials Science Forum Vols. *** (2004) pp.687-691 2004 Trans Tech Publications, Switzerland online at http://scientific.net 2004 Trans Tech Publications, Switzerland Development of Parameters Selection System for Ultrasonic Vibration Assisted Electro-discharge Machining Y.J. Hu 1,a,J.H. Zhang 1,b, X.F. Wang 1,c and S.F. Ren 1,d 1 School of mechanical engineering, Shandong University, 250061, Jinan, P.R. China a yujing_hu@163.com, b jhzhang@sdu.edu.cn, c yj_xf@163.com, d shengfengren@hotmail.com Keywords: Electro-discharge machining, Ultrasonic vibration machining, Machining parameters, Artificial neural network Abstract. Because the machining of ultrasonic vibration assisted electro-discharge machining (UEDM) is a very complex process and it is too difficult to describe precisely every influencing factor with an accurate mathematics model, the study of parameters selection system is necessary and important for the practical application of machining method, the improvement of machining efficiency and minimizing the tool wear ratio (TWR). In this paper, the model and the corresponding database are built for UEDM based on the back propagation (BP) algorithm artificial neural network (ANN) to optimize machining parameters. Through learning and training, this system realizes the intelligent selection of machining parameters. As shown by the experiment results, the predictions accord with the test results, which shows that the reasonable and reliable project of UEDM can be provided by the system. With the increase of the machining sample, the machining database can be enriched and the application range will be expanded, so this system has the excellent fault-tolerance and extensible quality. Introduction The process of UEDM has more advantages compared with other machining processes, for instance there exist no mechanical machining forces in the machining, so it has become an indispensable machining process in modern manufacturing. However, the influencing factors are numerous and complicated, so it is too difficult to quantify the influence expressly with an accurate mathematics model for UEDM. The configuration of the electrical parameters affects machining results greatly, and the influencing factors also inter-conflict and inter-restrict [1]. Reasonable parameters selection is not only the foundation of stable, high-efficiency machining, but also the precondition of making full use of the machine tool. Generally, manufactory provides many better projects that include certain electrical parameters and machining results. Because of the experiences and the diversity of parameter selection and the complexity and real-time of logical reasoning, the selection of machining parameters depends on the operators experience and training degree. So it is very valuable to predict new machining projects from original data, and the corresponding study and development are necessary to the practical application [2,3]. The Main Influencing Factors The machining mechanism of UEDM is too complicated to find it out completely. For application and improving machining efficiency and minimizing tool wear ratio, it is necessary to investigate the influencing factors [4]. According to experimental results, the influencing factors to machining velocity are shown as [5,6] V = KW fφ (1) M All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications, www.ttp.net. (ID: 130.203.136.75, Pennsylvania State University, University Park, USA-04/03/16,01:55:25)
688 Advances in Materials Manufacturing Science and Technology 688 Advances in Materials Manufacturing Science and Technology where, K is a process coefficient related to pulse parameters and dielectric fluid, etc.; f is pulse frequency, φ is the utilization ratio of valid pulse; W M is the energy of a single pulse, related to discharge voltage, discharge current and discharge duration. W M can be expressed as follows t = e W u( i( dt (2) M 0 where, t e is discharge duration; u( and i( is discharge voltage and current that vary with time. Equation (1) and (2) can be synthesized to V t e = Kfφ u( i( dt (3) 0 In the machining process, W M is the most important influencing factor to surface quality; According to experimental results, the empirical formula is given as R μ (4) 0.3 0.4 0.2 0.2 max = K rte i pe ti i where, R max is actual surface roughness; K r is a constant; t e is the discharge duration; i pe is the peak discharge current; t i is pulse interval; µ i is no-load voltage. In UEDM process, except for the influencing factors mentioned above, the machining results are also related to ultrasonic vibration frequency and ultrasonic vibration amplitude closely [7]. Principle of ANN The ANN technique is a kind of intelligence technique applied widely that has self-learning, self-organizing and association functions by simulating the micro-architecture of human brain. The network can deal with fuzzy, indeterminate, sequential, un-accurate information [8]. The BP network architecture includes input neurons, output neurons and hidden neurons. Generally, multi-layer network contains one or more hidden layers and a nonlinear activation function, which is able to solve more complicated problems than the single layer network. The BP network converts the relation of input and output information to nonlinear optimal problem. Parameters Selection System for UEDM Based on ANN Structure of the Parameters Selection System. By introducing ANN technique, the parameters selection system of UEDM based on ANN is put forward. Meanwhile, the self-learning sample database of ANN is developed based on original data provided by manufactory and experimental Machining database Learn Train New project Selecting machining parameter network Man-machine interface of database Man-machine COM port, extended COM port Re-learn Learning results Fig.1 Structure of the parameters selection system data. Through training the BP network by original parameters, the system can acquire comprehensive machining knowledge and optimize the machining parameters. The system structure can be shown as Fig.1.
Materials Science Forum Vols. 471-472 689 Materials Science Forum Vols. *** 689 Design Target of the System. The system can realize the following functions: 1) Giving machining parameters according to the input information of user, and creating process procedures; 2) Modifying the weights by learning continuously and providing more dependable projects; 3) Developing friendly man-machine interface based on ergonomics principle, which make this system easier for the user to operate. Topology Structure and Network Algorithm of the BP Network. The parameters selection process is that the network sets the optimal machining parameters automatically according to the machining requirement. In this process, surface quality, material removal rate (MRR) and TWR are related nearly to many machining parameters such as pulse peak current, pulse duration, pulse interval, machining duration, no-load voltage time, servo voltage, feed rate, machining area, electrode profile, electrode material, dielectric medium, ultrasonic frequency and amplitude etc. [9,10]. If all these parameters are put into the network as output parameters, the network will become too complicated and difficult to be trained. Therefore, it is necessary to use the most important electrical parameters as the network output. In this system, the input and output parameters are shown in table 1 and 2. This BP network is composed of three layers, which includes 5 input neurons and 5 output neurons. According to the computing ability and quantity of training samples, the BP network has a lower allowable error and runs stably when the hidden layer include 16 hidden neurons. The developed ANN model is shown in Fig.2. Table 1 The input information Input parameter Surface quality MRR TWR Machining area Material couple Input code X1 X2 X3 X4 X5 Table 2 The output information Output parameters Peak current (I p ) Pulse duration (t i ) Pulse interval (t o ) Ultrasonic frequency (U f ) Ultrasonic amplitude (U a ) Output code Y1 Y2 Y3 Y4 Y5 X1 X2 X3 X4 Y1 Y2 Y3 Y4 X5 Input neurons Hidden neurons Output neurons Y5 Fig.2 The artificial neural network model of the system The learning process of BP network includes positive transmitting and negative transmitting. If the error could not meet the required accuracy or astringency after the positive transmitting, the network need negative transmitting to modify the weights of each layer till getting allowable error. With the increase of sample quantity, the BP network modifies the weights continuously. The longer the training time, the closer the training results to actual accuracy. Program Design. Based on Win2000 operating system, the system is programmed by VC++6.0 and the Microsoft Access database is adopted. The link with Access is realized by ADO DLL technique, which creates friendly man-machine interface and the CperDlg class to manage database.
690 Advances in Materials Manufacturing Science and Technology 690 Advances in Materials Manufacturing Science and Technology The prediction will be shown on the interface after the information is send to the system, and the output can be saved, modified and deleted in database. The user can use the machining data in this database directly by inputting the project number. The man-machine interface is shown as Fig.3. Fig.3 The man-machine interface of parameters setting Model Verification. The system is validated on special EDM tool. The results are shown on the table 3 after 50 samples trained the network. The surface roughness difference is 7.60% and the MRR difference is 9.03% between machining results and process request. The experimental results show that the BP network can reflect the relation between process parameters and machining results, and can improve machining efficiency of UEDM. Table.3 the validated results The input information The output information Machining result R amax [μm] MRR [mm 3 /min] TWR [%] Area [mm 2 ] Material couple I p [A] t i [μs] t o [μs] U f [KMZ] U a [μm] R amax [μm] MRR [mm 3 /min] 3.4 5.9 0.5 100 C u -S t 9.5 30 40 43 1.0 3.04 5.13 6.3 20.0 2.5 100 C u -S t 24.0 53 40 43 1.2 6.01 19.01 Conclusions The system developed in this paper can optimize main machining parameters to improve machining quality and efficiency. In this system, the key of development is to determine the network topology structure and the weights of BP network. Based on plenty of practical machining data, the sample data train BP network to achieve reasonable weights, Meanwhile, in practical machining process, the weights could be modified and adjusted continuously to improve the selection accuracy and extend the machining knowledge of UEDM. Because of the intelligentized design, this system has excellent fault-tolerance performance, flexibility, expansibility and maneuverability. Acknowledgements The work described in this paper was supported by the National Natural Science Foundation of China (NSFC), project number: 50275087.
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Advances in Materials Manufacturing Science and Technology 10.4028/www.scientific.net/MSF.471-472 Development of Parameters Selection System for Ultrasonic Vibration Assisted Electro-Discharge Machining 10.4028/www.scientific.net/MSF.471-472.687 DOI References [1] M.B. Gorzalczany: Information Sciences Vol. 120 (1999), p. 69 10.1016/S0020-0255(99)00069-9