Metal cutting and high speed machining

Tai ngay!!! Ban co the xoa dong chu nay!!! METAL CUTTING AND HIGH SPEED MACHINING Edited by D Dudzinski Universite de Metz Metz, France A Molinari Universite de Metz Enim Metz, France and H Schulz Technical University of Darmstadt Darmstadt, Germany Kluwer Academic I Plenum Publishers New York, Boston, Dordrecht, London, Moscow Library of Congress Cataloging-in-Publication Data Metal cutting and high speed machining/edited by D Dudzinski, A Molinari, and H Schulz p cm Papers presented at the Third International Conference on Metal Cutting and High Speed Machining, June 2001, Metz, France Includes bibliographical references and index ISBN 0-306-46725-9 I Metal-cutting tools-Congresses Metal-work-Congresses High-speed machining-Congresses I Dudzinski, D., 1952- II Molinari, A., 1948- Ill Schulz, Herbert, 1936- IV International Conference on Metal Cutting and High Speed Machining (3rd: 2001: Metz, France) TJll86 M378 2002 671.5 '3-dc21 2001057982 Proceedings of the Third International Conference on Metal Cutting and High Speed Machining, held June 27-29, 2001, in Metz, France ISBN 0-306-46725-9 ©2002 Kluwer Academic I Plenum Publishers, New York 233 Spring Street, New York, New York 10013 http://www.wkap.nl/ 1098765432 A C.l.P record for this book is available from the Library of Congress All rights reserved No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording, or otherwise, without written permission from the Publisher Printed in the United States of America PREFACE This book gives a coherent overview of recent developments in Metal Cutting and High Speed Machining, presenting the latest research of international groups in theoretical and experimental approaches in this field Topics covered include: mechanics of cutting, numerical models, chatter vibrations, machining processes (drilling, high speed milling, grinding, hard turning), cutting tools and coatings, dry cutting, computer aided manufacturing, numerical control and command, process monitoring and adaptive control, machine tool (in particular the Parallel Kinematic Machines) and components (spindles and linear motor feed drive) Special attention is made to industrial applications, to aeronautical materials, for example Various facets of metal cutting are developed to stimulate interdisciplinary approach The book is constituted by a selection of papers presented at the Third International Conference on Metal Cutting and High Speed Machining which was held in Metz, France, on June 27-29, 2001 This conference brought together 360 scientists, researchers and engineers from 31 countries; it promoted fertile discussions and exchange of ideas The Conference is co-organized by the Universite de Metz, Ecole Nationale d'Ingenieurs de Metz and the Darmstadt Technische Universitat with a two years interval Progress in metal cutting needs a synergy between many disciplines among which mechanics, of course, for the analysis and the design of the whole process, but in combination with material science and physico-chemistry for elaborating new tools, coatings and new work materials, tribology for the modelling of dynamic friction at the tool-chip interface, computing for the development of efficient software simulating and optimizing the cutting processes, applied mathematics for process monitoring and control Interactions between these disciplines are illustrated in this book The editors would like to express their appreciation to all the authors for their contributions to this book Special thanks are due to the members of the scientific committee of the conference It is hoped that this book will provide to manufacturing engineers, researchers, and students, information, help and a necessary interdisciplinary view to solve problems encountered in machining processes and to-propose new ideas and applications in this field D Dudzinski, A Molinari and H Schulz v CONTENTS MECHANICS OF CUTTING I ON THE SIMULATION OF MACHINING AT THE ATOM JC SCALE R Komanduri and M.L Raff DYNAMICS IN HIGH SPEED MACHINING G Warnecke and S Siems INFLUENCE OF MATERIAL PROPERTIES ON SURFACE INTEGRITY AND CHIP FORMATION IN HIGH SPEED TURNING E Brinksmeier, P Mayr, T Lubben, P Pouteau, and P Diersen DETERMINATION OF FORCES IN HIGH SPEED MACHINING (HSM) FROM MACHINING TESTS AND AV ARIABLE FLOW STRESS MACHINING THEORY B Kristyanto, P Mathew, and J A Arsecularatne 21 31 41 THERMOMECHANICAL MODELLING OF CUTTING AND EXPERIMENT AL VALIDATION A Moufki, A Devillez, D Dudzinski, and A Molinari 51 INFLUENCE OF HEAT TREATMENT AND CUTTING PARAMETERS ON CHIP FORMATION AND CUTTING FORCES H Schulz and A Sahm 69 MEASUREMENT AND SIMULATION OF TEMPERATURE AND STRAIN FIELDS IN ORTHOGONAL METAL CUTTING Y.K Potdar and A.T Zehnder 79 NUMERICAL APPROACH OF CUTTING AND MACHINING A PARAMETRIC STUDY OF THE EFFECTS OF CUTTING PARAMETERS ON CHIP FORMATION PROCESS M.R Movahhedy, M.S Gadala, and Y Altintas 91 vii CONTENTS viii THREE-DIMENSIONAL FINITE-ELEMENT ANALYSIS OF HIGH-SPEED MACHINING J.F Molinari IO PR EDICTION OF CHIP MORPHOLOGY IN ORTHOGONAL CUTTING BY MEANS OF A CUSTOMIZED FINITE ELEMENT CODE E Ceretti , L Filice, and F Micari 107 119 CHATTER VIBRATIONS 11 KTN EMATICS AND DYNAMICS OF MILLING WITH ROUGHING END MILLS M.L Campomanes 129 12 STUDY ON CHATTER VIBRATION IN RAMPING OF SCULPTURED SURFACES B.W Ikua, H Tanaka, F Obata, and S Sakamoto 141 13 REGENERATIVE STABILITY ANALYSIS OF HIGHLY INTERRUPTED MACHINTNG M.A Davies, J.R Pratt, B Dutterer, and T.J Bums 151 14 DETECTING CHATTER IN GRINDING J Gradisek, E Govekar, I Grabec, A Baus, and F Klocke 161 MACHINING PROCESSES 15 TOOL WEAR AND WORKPIECE SURFACE INTEGRITY WHEN HIGH SPEED BALL NOSE END MILLING HARDENED AISI Hl3 D.A Axinte and R.C Dewes 16 THE EFFECT OF CUTTING ENVIRONMENT AND TOOL COATTNG WHEN HIGH SPEED BALL NOSE END MILLING TITANIUM ALLOY H Niemann, E.G Ng, H Loftus, A Sharman, R Dewes, and D Aspinwall 17 HIGH SPEED BALL NOSE END MILLING OF INCONEL 718 WITH VARIABLE TOOL GEOMETRY- EXPERIMENTAL AND FTNITE ELEMENT ANALYSIS E.G Ng, S.L Soo, C Sage, R Dewes, and D Aspinwall 171 18 191 CONTENTS ix 18 INFLUENCE OF MACHINING CONDITIONS ON RESIDUAL STRESSES: SOME EXAMPLES ON AERONAUTIC MATERIALS L Guerville and J Vigneau 20 I 19 SURFACE INTEGRITY IN FINISH HARD TURNING OF GEARS J Rech, M Lech, and J Richon 211 20 WEAR TRENDS OF PCBN CUTTING TOOLS IN HARD TURNING T.G Dawson and T.R Kurfess 221 21 AN ANALYTICAL STUDY ON THE ST ABILITY OF DRILLING AND REAMING J.A Yang, V Jaganathan, and R Du 233 22 HIGH SPEED GRINDING: AN INDUSTRIAL STUDY OF LUBRICATION PARAMETERS A Devillez., Sinot, P Chevrier, and D Dudzinski 251 23 USE OF A HIGH SPEED MACHINING CENTRE FOR THE CBN AND DIAMOND GRINDING OF NICKEL-BASED SUPERALLOYS J Burrows, R Dewes, and D Aspinwall 267 CUTTING TOOLS AND COATINGS, DRY CUTTING 24 SHEAR LOCALISATION AND ITS CONSEQUENCE ON TOOL WEAR IN HIGH SPEED MACHINING S.V Subramanian, H.O Gekonde, G Zhu, and X Zhang 277 25 HSC-CUTTING OF LIGHTWEIGHT ALLOYS WITH CVDDIAMOND COATED TOOLS F Klocke, R Fritsch, and J Grams 289 26 ENHANCED WEAR RESISTANCE AND TOOL DURABILITY USING MAGNETIZATION M El Mansori, K Lafdi, and D Paulmier 301 27 FUNCTIONALLY GRADED HARDMETAL SUBSTRATES FOR COATED CUTTING TOOLS J Garcia, W Lengauer, J Vivas, K Dreyer, H van den Berg, H.-W Daub, and D Kassel 28 INNER COOLING SYSTEMS-WEAR REDUCTION FOR DRY CUTTING E Uhlmann and T Frost 311 319 x CONTENTS 29 MIST COOLANT APPLICATIONS IN HIGH SPEED MACHINING OF ADVANCED MATERIALS M Dumitrescu, M.A Elbestawi, and T.I El-Wardany 329 CAD/CAM/NC 30 DEVELOPMENT OF CAM SYSTEM FOR HIGH SPEED MILLING K Morishige, T Sakamoto, Y Takeuchi, I Takahashi, K Kase, and M Anzai 31 AB-CAM: AN AGENT-BASED METHODOLOGY FOR THE MANUFACTURE OF STEP COMPLIANT FEATURE BASED COMPONENTS R.D Allen, R.S.U Rosso, Jr., and S.T Newman 32 ASSESSMENT OF THE DESCRIPTION FORMAT OF TOOL TRAJECTORIES IN 3-AXIS HSM: OF SCULPTURED SURFACES E Due, C Lartigue, and S Laporte 341 351 363 PROCESS MONITORING AND ADAPTIVE CONTROL 33 TOOL CONDITION MONITORING USING TRANSITION FUZZY PROBABILITY R Du, Y Liu, Y Xu, X Li , Y.S Wong, and G.S Hong 375 34 TOOL WEAR MONITORING BY ON-LINE VIBRATION ANALYSIS WITH WAVELET ALGORITHM G Luo, D Osypiw, and M Irle 393 35 ADAPTIVE POWER FEEDBACK CONTROL IN CYLINDRICAL TRAVERSE GRINDING K.A Hekman, R.L Hecker, and S.Y Liang 407 MACHINE TOOL 36 A NEW MACHINE TOOL CONCEPT FOR ON SITE MAINTENANCE OF LARGE METAL FORMING TOOLS: TRANSPORTABLE MACHINING UNIT WITH HYBRID KINEMATIC STRUCTURE H.K TOnshoff, H.-C Mohring, G Gunther, E Lubbers, and A Schmidt 37 THE DESIGN OF PARALLEL KINEMATIC MACHINE TOOLS USING KINE TO ST A TIC PERFORMANCE CRITERIA F Majou, P Wenger, and D Chablat 417 425 CONTENTS xi 38 PARALLEL KINEMATIC MACHINES-DEVELOPMENT, SOFTWARE METHODS AND EXPERIENCES V Maier 435 MACHINE TOOL COMPONENTS 39 HIGH VOLUME CUTTING OF ALUMINIUM H Voll 40 EXPERIMENT AL STUDIES OF HIGH SPEED THERMOMECHANICAL-DYNAMIC BEHAVIORS OF MOTORIZED MACHINE TOOL SPINDLES C.-W Lin, J.F Tu, and J Kamman 41 ADVANTAGES IN APPLICATION OF LINEAR MOTOR MACHINES IN DIE AND MOULD MANUFACTURING E Abele, H Schulz, and B Bork 445 455 465 42 ROBUST MOTION CONTROL FOR LINEAR MOTOR DRIVES D Tong, A Elfizy, and M.A Elbestawi 475 AUTHOR INDEX 487 KEYWORDS INDEX 489 ON THE SIMULATION OF MACHINING AT THE ATOMIC SCALE Ranga Komanduri and Lionel M Raff ABSTRACT Molecular dynamics (MD) simulation is an extremely powerful technique for investigating atomistic phenomenon Almost all physical phenomena when considered at the fundamental level can be attributed, directly or indirectly, to the forces acting between the atoms that constitute the material Atomic or molecular dynamics (MD) simulations are playing an increasingly important role in the fields of materials science, physics, chemistry, tribology, and engineering This is because there is really no alternate approach to MD simulation capable of handling such broad ranging problems at the required level of details, namely, atomistic level MD simulations are providing new data and exciting insights into ultraprecision machining that cannot be obtained readily in any other way - theory or experiment In this paper, the principles of MD simulation, relative advantages and current limitations of this technique, and the application of MD simulations in addressing a wide range of machining problems will be presented l INTRODUCTION For a long time, miniaturization of products was limited essentially to one industry, namely, the watch industry Various components of a watch were fabricated mainly by mechanical methods using minilathes, minidrilling machines, minimilling machines, and Reg~ts Professor, Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK 74Q78, U S A, Phone: (405) 744-5900, Fax: (405) 744-7873, e-mail:ranga@ceat.okstate.edu Regents Professor, Chemist!)' Department, Oklahoma State University, Stillwater, OK 74078, U S A Metal Cutting and High Speed Machining, edited by D Dudzinski et al., Kluwer Academic/Plenum Publishers, 2002 ROBUST MOTION CONTROL FOR LINEAR MOTOR DRIVES 477 2.1 The dynamics along the direction of motion A general block diagram for the linear motor system is given in Figure White Noise Considered as Open Loop Disturbance Model System~ r - I Sk + Brushless Amplifier A Set point u Linear Motor G Yt Output Feedback Encoder Figure General block diagram for the linear motor system The system dynamics can be described by the transfer function relating the input sk , given by the position command, to the output Yk, which is the actual position of the moving part For open loop identification, a PRBS signal is used as input The PRBS is designed with switching time 0.05 second and magnitudes varying from 0.25 mm to 2.5 mm The noise disturbance is modeled as white noise The sampling time for the data is O.OOlsecond The data collected are divided into two sections: one for the identification and the second for cross validation Using time series analysis, the model structure can be represented as Eq (1) (1) where Bis the backward shift operator, (1)(8)11' 18(8) is the plant model, and 9(8)1;(8) is the disturbance model Based on correlation analysis, the plant transfer function can be considered as a second order system with delay equal to one sampling interval In addition, the disturbance model has been found to be also of second order The identification of the different parameters of the structure results in, n= 0.936lxl0- 38 1-1.97418 + 0.97538 ·~+ l l- l.99878 + l.00038 ·~ (2) The bode plot of the plant transfer function is presented in Figure The plant acts as a low pass filter with a cut off frequency of 70 Hz 478 DAYONG TONG ET AL _ Bode Diagrams 50~ I I I I I I i·: ,·,·~; ~ i•I~ I I I I 1111 I Ill I I I I I I I I I I I I 111 II I I I I I I I I I I I I I I I -f -5: ::: _ ,: ,: Li-li.i1tJ:::::::t::t:t:j:!tt::::::t:::t::l:: :t:.t:i.i ,· C I I I I I II I I I I I I I I g> -100 : I : I : : : :::' I I I t I Ill I I I I I I 11 I : I : I : : : : ::' I I It 11 I I : : : ::: I : I I ~ f : : l· · EIJ·······1· · ·t· · · · · t· ,·l· · ~ 10' 1o~ Frequency (Hz) Figure Bode plot of the plant model The results of the validation of the model, depicted in Figure 4, show a good agreement between predicted and experimental data In addition, to examine the performance of the plant model obtained in the high frequency range, three input signals are designed Two of them have the same range of varying magnitudes (from 0.25 mm to 2.5 mm) but with faster switching times, 0.01 second and 0.005 second The third one has magnitude of 0.25 mm and switching time of 0.001 second The results show that the maximum error obtained is about 10 µm In all cases, the results show that model errors can be neglected x o·• Measured and step predicted output 1.2 1.15 :§: 1.1 ~ g_ :; 1.05 a: 0.95 40.56 40 59 40.6 40.61 40.62 40.63 40.64 40.65 40.66 40.67 40.66 Time (second) Figure Comparison between the model output (solid line) and the actual output (dashed line) over a portion of simulation time ONG ET AL ROBUST MOTION CONTROL FOR LINEAR MOTOR DRIVES 479 2.2 Structural dynamics perpendicular to the direction of motion agreement ce of the Two of "th faster mm and is about In case of high speed machining, higher frequency modes of vibration can be excited and this affects the manufacturing process Vibrations of the moving part normal to the direction of motion affect the motor performance The sources of vibrations could include cutting and friction forces as well as mass variations For a double-sided linear motor, the air gap between coils and permanent magnets changes under vibrations This affects the magnetic force normal to the direction of motion This type of vibration also affects the measuring device Since a reflected light measuring encoder is used for position feedback, inaccurate readings can result8 Modal analysis is performed to identify the structural dynamics (natural frequencies and mode shapes) The results show two dominant natural frequencies in the frequency range of interest Figure shows the accelerance function for different position of the moving part along the travel distance As mentioned, the two natural frequencies are identified at 400 Hz and 638 Hz Figure also shows that the response of the motor structure varies with the position of the moving part along the travel distance Mode shapes are shown in Figure for the two dominant natural frequencies These results can obviously be used to determine the location of the feed back sensor (i.e on the side showing smaller deformation) The natural :frequencies are used further to select the most appropriate controller oarameters: this will be explained later in section · - ·: " .~ 0.4 0 200 Position of moving Part (m) 3lJ 400 500 600 700 000 Frequency (Hz) portion of Figure The response function of the moving part DA YONG TONG ET AL 480 Frequency 638 Hz Frequency 400 Hz Figure The mode shapes of the moving part CONTROL LAW DESIGN Three main strategies were chosen for control of the feed drive They are: Generalized Predictive Control (GPC), developed in , Minimum Time Tracking Control (MTTC)7, and Sliding Mode Control (SMC) which is developed for the linear motor in this study Sliding mode control is attractive due to its simplicity and ability ofresponding to model uncertainties The model for the motor in section is treated as the nominal part of the sliding mode controller The modal testing result is employed to select the value of / in the sliding mode control law A double linear transformation of (Eq 2) determines the second order transfer function of the linear motor plant in the s domain Eq (3), where d(:i,x) are the dynamics of the linear motor 0.001 li + 0.0264.i + 1.2819x = u or mi=d(.i,x)+u (3) The model represented by (Eq 3) is a small signal model, therefore, in analyzing larger displacement ranges, disturbance observers and pre-compensation are necessary to obtain improved control performance The sliding surface used is defined in Eq (4), where x= x - x r is the tracking error in the variable x s(t)=x+ U (4) A control law, necessary to determine a negative definite derivative of the Lyapunov function s2 has to be determined To maintain the system on the sliding surface "s", the conditions s = o and s =0 should be applied If s =0 an ideal control input u = d (x,x)+m(xr - S) is obtained However, due to model imprecision, the dynamics d(x,x) and the parameter m are not clearly defined The most appropriate control input ROBUST MOTION CONTROL FOR LINEAR MOTOR DRIVES 481 approximation u=d(x,x)+m(ir-E) is obtained from d and m estimation The condition s=O will be satisfied if u=u-ksgn(s), where sgn(s) is a signal function acting as on-off switch, or" equivalent control"9 Thus, the control law can be obtained from Eq (5) and Eq (6) u = m(ir -Ai)+ d(i,x)-k sgn(s) (5) k = (&d + 71)+ {&m,Xr -Ej+q} (6) I Where: the bound of mass variation &m is defined as jm - ,;, ~ c m , m 1s the geometric average value of mass uncertainty, &d Id - d I!> c d , and , the bound of d(x ,x) , is defined as 11,q are small positive numbers Equations (7) and (8) demonstrate that the negative definite derivative of the Lyapunov function s2 can be obtained from the control law given by (Eq 5) and (Eq 6) !!._L(t) =!_!!._s2 = SS = {d dt dt m(x, - E)-d + m(x, - E) - ksgn(s)}s d Id = ss !> -(71+q) 11 -L(t)= S dt dt where: m'&m' andi,x are determined experimentally and (7) (8) XnXr , Xr are obtained from the interpolator command d(x,x) is estimated using a periodic observer, a force ripple feedforward compensation, and a friction feed-forward compensation Also, a periodic observer for the cutting force and a mass observer for slow varying disturbances are employed After the force ripple and friction were pre-compensated, the steady state error caused by mass variation becomes dominant The state space model for the disturbance observer is given as Eq (9), ( w is slow varying or constant disturbance) by augmenting the system model 11 • The control law is shown in Figure (9) The frequencies and magnitudes of the sine waves used to represent the force ripple of linear motors can be determined experimentally 12' 13 • In addition, the static friction force leads to significant errors in high speed contouring applications, particularly at sharp changes in the reference velocity direction To avoid the contouring errors at the critical points, the friction force is identified off-line and compensated for using Eq (10), ( c value is detennined experimentally to ensure smooth direction changes) 482 DA YONG TONG ET AL l F,+ F,- Ffriction(x) = if(x > E) ) if(E > X > -&) (10) if(-&>x) d Sliding Mode Controller +V- Friction Compensation • U- Jv'\_ -~~~~ _, ,,.~1 x~ -1 - . o_bs_~e~~er~J •~1 - with x,X,i linear Motor - Force Ripple Compensation - F;,uttingforce Periodic Obse~er Spindle Speed Figure Block diagram of sliding mode control scheme JMPLEMENT ATION AND EXPERIMENTS The Sliding Mode Control (SMC) is implemented and evaluated for the linear motor described in Section The controller is evaluated in conjunction with the Minimwn Time Path Optimization approach described in 7, using a sampling frequency of IOKHZ For comparison purpose, the Generalized Predictive Control (GPC), and the Minimum Time Tracking Control (MTTC) strategies are also evaluated, under the same condition The Plant model used for the GPC controller is defined by (Eq 2) and (Eq 3) In this case, the sampling frequency is KHZ, and the control horizon is chosen to be 30 steps In general, the controller parameters are selected to avoid saturation The MTTC algorithm is given in and is adopted for this study 483 ROBUST MOTION CONTROL FOR LINEAR MOTOR DRIVES For the Sliding Mode Control (SMC), the choice of the value of the parameter (see Eq (4)) is critical A value of A.= 350 is determined experimentally, while taking into consideration three limiting fuctors : (ii) Structural resonant frequencies: As 400 Hz Unmodeled time delay: A 113Td, where r d (iii) Control sampling frequency: A ~ 0.2fs , where fs (i) = 0.9 ms I0,000 Hz = The transient performance of the three controllers is first considered for the motor with and without added mass: The responses to a position step input of 500µm are shown in Figure It can be seen that the sliding mode controller results in improved transient performance over the other two controllers, in the presence of the added mass Tracking errors are next evaluated for two controllers; namely a circle of 60mm radius and a sharp comer (90°) The contouring velocity for the circular path is set at 188.5 mm/s, and 213 mm/s for the sharp comer In the later case, the maximum acceleration is 2,500 mm/s and the maximwn jerk is 50,000 mm/s3· G~C 600 : stop tnt with ono ' MiTC step tett with ond without load l ~ MlllM&I li! ~ -0.5 I-1 ' ~ -'' ~ '~~ -'~~ -'-~~ ' ~~ -'-~~ ' "' ' -~ _._~~~ 500 1000 1500 2000 2500 3000 3500 4000 4500 x 10· ~ ~ gi 0.5 li! ~ -0.5 I- ~~~~~~~~~~~~~~~~~~~~~~~~~~~ 500 1000 1500 2000 2500 3000 3500 4000 4500 Time ( ms ) x -5 10 ~- ~-.~~~S~M~C~T~ra=c~kl~n~ar~o=undr -'a~c=o=m=err~~-. ~~-.-~~ , ~ 0.5 G> g g> tiCll -0_5 ~ 500 1000 1500 2000 2500 Time (ms) 3000 3500 4000 4500 Figure Tracking error for sharp comer CONCLUSIONS In this paper several control strategies were developed for motion control of linear motors The results show that Sliding Mode Control (SMC) in combination with Minimum 485 ROBUST MOTION CONTROL FOR LINEAR MOTOR DRIVES Time Path Optimization (MTPO) results in good control performance particularly in the presence of load disturbances The structural dynamics of the linear motor represent important constraints for the speed and acceleration of the motor and for appropriate selection of controllers' parameters These dynamics must also be considered when locating the position of the feedback device d) E E g., !? a) x 10- GPC-Tracl -1 Tim (second) Figure 10 Tracking error for circular path Time (secorll) 486 DA YONG TONG ET Al REFERENCES I D Renton and M A Elbestawi, Motion control for linear motor feed drives in advanced machine tools, International Journal of Machine Tools and Manufacture, 41, 2001, pp 479-507 D M Alter and T.C Tsao, Stability of turning processes with actively controller linear motor feed drives, ASME Journal/or Engineering/or Industry, 116, 1996, pp 298-307 D M Alter and T C Tsao, Control of linear motors for machine tool feed drives: design and implementation of H" optimal feedback control, ASME Journal of Dynamic System, Measurement, and Control, 118 1996, pp 649-656 D M Alter and T C Tsao, Control of linear motors for machine tool feed drives: experimental investigation of optimal feed forward tracking control, ASME Journal of Dynamic System, Measurement, and Control, Vol.120, 1998, No.I, pp.137 G Pritschow and W Phillip, Research on the efficiency of feed forward controllers in direct drives, Annals of the CIRP, Vol 41/1/1992, pp 411-415 P Boucher, D Dumur, and K Faissal Rahmani, Generalized predictive control (GPCC) for machine tools drives, Annals of the CIRP, Vol 39/1/1990, pp 357-360 D Renton, High Speed Servo Control of Multi-Ax.is Machine Tools, Ph.D thesis, Mc Master University, 2000 M Weck, P KrUger, and C Brecher, Limits for controller setting with electrical linear direct drives, Translation of German original, Antriebstechnik, 38, Feb/March 1999, Vereingte Fachverlage, D-Mainz J J.E Slotine and W Li, Applied Nonlinear Control, Prentice Hall, NJ, 1991 10 H Van Brussel and P Vanden Braembussche, Robust control of feed drives with linear motors, Annals of the CIRP, Vol 4711/1998, pp 325-328 11 G F Franklin, J D Powell, and M L Workman, Digital Control of Dynamic System, 2nd ed., Addison-Wesley Publising Co , 1990 12 G Pritschow, A Comparison of Linear and Conventional Electromechanical Drives, Annals of CIRP, Vol 47/2/1998, pp 541-548 13 M.Ostojic, Jeff Xi, Robust Control of Linear Direct-Drive Actuators, IASTED International Coeference on Control And Applications, Baff; Alberta, Canada, 1999 AUTHORS INDEX Abele, E., 465 Allen, R.D., 351 Altintas, Y., 91 Anzai, M., 341 Arsecularatne, A , 41 Aspinwall, D., 181, 191, 267 Axinte, D.A., 171 Baus, A., 161 Bork B 465 Brinksmeier, E., 31 Bums, T.J., 151 Burrows, J., 267 Campomanes, M.L., 129 Ceretti, E., 119 Chablat, D 425 Chevrier, P., 251 Daub, H-W, 311 Davies, M.A., 151 Dawson, T.G., 221 Devillez, A., 51, 251 Dewes, R.C., 171, 181, 191, 267 Diersen, P , 31 Dreyer, K., 311 Du, R., 233 375 Due, E., 363 Dudzinski, D., 51, 251 Dumitrescu, M • 329 Dutterer, B., 151 El Mansori, M., 301 Elbestawi, M.A., 329, 475 Elfizy, A., 475 El-Wardany, T.I., 329 Filice, L., 119 Fritsch R., 289 Frost, T., 319 Gadala, M.S., 91 Garcia J., 311 Gekonde, H.O., 277 Govekar, E., 161 Grabec, I., 161 Gradisek, J., 161 Grams, J., 289 Guerville, L., 201 Gilnther, G., 417 Hecker, R.L., 407 Hekman, K.A., 407 Hong, G.S., 375 lkua, B.W., 141 Irle, M., 393 Jaganathan, V., 233 Kamman, J., 455 Kase, K., 341 Kassel, D., 311 Klocke, F., 161, 289 Komanduri, R., l Kristyanto, B., 41 487 488 Kurfess, T.R., 221 Lafdi, K., 30 I Laporte, S., 363 Lartigue, C., 363 Lech, M., 211 Lengauer, W., 311 Li, X., 375 Liang, S.Y., 407 Lin C-W, 455 Liu, Y., 375 Loftus, H., 181 Uibben T., 31 Lubbers, E., 417 Luo, G., 393 Maier, V., 435 Majou, F., 425 Mathew, P., 41 Mayr, P., 31 Micari, F., 119 MOhring, H.-C, 417 Molinari A., 51 Molinari, J.F., 107 Morishige, K., 341 Moufki, A., 51 Movahhedy, M.R., 91 Newman, S.T., 351 Ng, E-G, 181, 191 Niemann, H., 181 Obata,F., 141 Osypiw, D., 393 Paulmier, D., 301 Potdar, Y.K., 79 Pouteau, P., 31 Pratt, J R., 15 I Raff, L.M., I Rech, J., 211 Richon, J., 211 Rosso, R.S.U., 351 Sage,C., 191 Sahm, A., 69 Sakamoto, S., 141 Sakamoto T 341 Schmidt A 417 Schulz, H., 69, 465 Sharman, A., 181 Siems, S., 21 Sinot, 0., 251 Soo, S.L., 191 Subramanian, S.V., 277 Takahashi, l., 341 Takeuchi, Y., 341 Tanaka, H., 141 Tong, D., 475 Tonschoff, H.K., 417 Tu, J.F., 455 Uhlmann, E., 319 Van den Berg, H., 311 Vigneau, J., 20 I Vivas, J., 311 Voll, H., 445 Warnecke, G., 21 Wenger, P., 425 Wong, Y.S.,375 Xu, Y., 375 Yang, J.A., 233 Zehnder, A T., 79 Zhang, X., 277 Zhu, G., 277 KEYWORDS INDEX Acoustic emission, 21 Adaptive control, 407 Agents, 351 Aluminium alloy, 41, 329, 445 Atomic or Molecular dynamics, MD simulation, l Ball end milling, 141, 171, 181, 191 Cam system, 341, 351, 363 Chatter vibrations, Drilling, 233 Stability analysis, 129, 141, 151,233 Chemical tool wear, 277 Chip control, 277 Chip formation, 21,31, 69 Chip morphology, 21, 31, 119, 277 Coatings, 181 Computer aided process planning, CAPP, 351 Coolant application, 329 Cooling systems, 319 Cutting environment, 181 Cutting of single crystal materials, I Hard metal substrates, 311 CVD-diamond coated tools, 289 Cylindrical traverse grinding, 407 Deformation measurements, 79 Die and mould manufacturing, 465 Difficult to cut materials, 201 Drilling, mathematical model, 233 Dry cutting, 319, 329 Features, 351 Finite element model, orthogonal cutting, 91, 119 three dimensional process, 91 Flexible maintenance, 417 Force measurement, 21 Fuzzy probability, 375 Grinding, chatter, detecting method, 161 experimental tests, 161, 251,407 process optimisation, 251, 407 roughness, 161, 251 Hard turning, 211, 221 Heat treatment, 31, 69 High speed machining, Experiments, 21, 31, 41, 69, 171, 181, 191, 289 Lightweight alloys, 289 Nickel Based Superalloys, 267 Shear localisation, 277 High speed milling, 329, 341 Hybrid kinematic structure, 417 lnconel 718, 191, 201 Linear motor drives, 465, 475 Machine tool, Control, 435 489 490 Machine tool Design,417,425,435,465 Maintenance, 417 magnetic field, 301 Material properties, l, 69 MD Simulations, Milling, Chatter vibrations, l 29, 141, l 51 Experiments, 129, 171, 181 Mathematical model, 129, 141, 151 Finite element model, l 91 Lightweight alloys, 289 Model testing, 475 Monitoring, tool wear, 393 Nickel based superalloys, 267 Numerical control, 363 Nurbs interpolation, 363 Oblique cutting model, 51 On line_vibration analysis, 393 Orthogonal cutting, experiments 51, 79 Finite element model, 79 Analytical model, 41, 51 Parallel kinematic machine tool, PKM, 425, 435 PCBN tools, 221 Point grinding, 267 Power control, 407 Principal components analysis, 251 Process planning, 351 Ramping, 141 Residual stresses, 31, l 91, 20 l, 21 l Reverse engineering, 417 Robust control, 475 Roughness, 191, 211, 289 Sculptured surfaces, 141, 363 Shear localisation, 277 Spindles, 445,455 Stability analysis, 129, 141, 151, 233 STEP, 351 System identification, 475 Tagushi method, grinding, 251 Temperature measurements, 79 Time domain simulation, 141 Titanium alloy, 181, 20 I Tool coatings 311, 329, Tool Condition monitoring, 375 Tool path, 341, 363 Tool wear monitoring, 393 Tool wear, 221, 301, 311, 319, 329 Transition fuzzy probability, 375 Transportable machining Unit, 417 Wavelet algorithm, 393