Int J Adv Manuf Technol (2008) 36:908–917 DOI 10.1007/s00170-006-0915-6
ORIGINAL ARTICLE
Off-line optimization on NC machining based on virtual machining J. G. Li & H. Zhao & Y. X. Yao & C. Q. Liu
Received: 7 July 2006 / Accepted: 12 December 2006 / Published online: 10 January 2007 # Springer-Verlag London Limited 2007
Abstract Virtual machining, based-on the model of a machining system, aims to simulate, evaluate and optimize the actual machining process with high sense of reality. It provides digital off-line optimization tools for NC machining. Taking advantage of virtual machining used in machining process simulation, one can build the framework of optimization system on NC machining so that the processes of reliability verification, cutting parameter optimization and error compensation can be integrated into one system to improve machining processes comprehensively. The optimization is realized via modifying NC programs. Several key issues such as virtual machining, cutting parameters optimization, error prediction and compensation are also highlighted. Optimization systems based on virtual machining have been developed to demonstrate the effectiveness of off-line optimization for different purposes. The results show that the machining process is obviously improved. Keywords Virtual machining . NC machining . Off-line optimization
1 Introduction NC machining is a key technology in machining of complex parts and free-form surfaces with high productivity and accuracy. It has become a trend that NC machining
J. G. Li (*) : H. Zhao : Y. X. Yao : C. Q. Liu School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China e-mail:
[email protected]
is utilized not only in small and medium-scale production but also in large-scale production. Much attention has been paid to NC machining optimization so as to achieve objectives with high quality and low cost. Approaches to NC machining optimization have been investigated for about 50 years. They can be classified into two categories: adaptive control based optimization (hence on-line optimization) and computer simulation based optimization. In contrast to the former, the latter is called off-line optimization. Adaptive control-based NC machining optimization is generally realized by adjusting feed rate. But it involves the setup problem of adaptive control device [1]. In contrast to it, computer simulation-based optimization is flexible and multi-purpose, and no device is needed besides a set of computer. Via computer simulation, the primary optimization aspects of NC machining can be realized, such as reliability verification, cutting parameter optimization and error compensation. NC machining is an automatic machining technology on NC machine tools driven by NC instructions. Prior to the actual machining, verification must be conducted to guarantee the reliability of NC programs, especially for multi-axis machining. However, the reliability verification in most CAD/CAM systems is only based on the cutter location of tool-paths instead of NC programs; moreover, the actual machining environment is not taken into consideration. The reliability of NC programs cannot be fully ensured. Error compensation is one of computer applications in mechanical manufacturing. Higher machining accuracy can be achieved at a low cost by correcting NC programs according to the predicted error [2]. Its primary advantage is that no hardware amendment on a machine tool is needed.
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Meanwhile, the machining productivity and cost must be taken into consideration when a NC program is planned to generate. A NC program not only prescribes the tool-path but also specifies the cutting parameters such as spindle speed and feed rate. After the tool-path is planned, the cutting parameters are the important influencing factors on machining productivity and cost. However, CAM systems can only automatically generate tool-path, the cutting parameters are usually designed by the programmer. On consideration of the safety of NC machining process, the cutting parameters are usually determined conservatively. It will result in the low utilization factors of machine tools and cutting tools. In order to improve the machining performance with unreasonable combination of cutting parameters, adaptive control-based optimization was presented at the end of 1950s. However, this approach cannot be widely utilized for some reasons, particularly for multi-axis machining of free-form surfaces [1]. As a result, a computer simulationbased NC program optimization was presented and investigated. Taking advantage of machining process simulation in the analysis and prediction of physical parameters prior to the actual machining process, programmers are aided in toolpath planning and selection of cutting parameters [3]. Virtual manufacturing (VM) is a computer-based technology, which can provide tools to optimize the production activities and improve the production efficiency via simulation prior to the actual production. Virtual machining is one of special applications of virtual manufacturing in machining process. It focuses on geometrical simulation and physical simulation. In geometrical simulation, virtual machining system is used to verify the correctness and
reliability of NC programs in advance. And physical simulation is to predict process parameters such as cutting force, power, tool-life and surface roughness. As a result, the tool-path planning and the combination of cutting parameters can be evaluated and optimized prior to the actual production. The quality of a NC program indicates the degree of a machining task being accomplished driven by it reliably, accurately and efficiently. NC programming is a complicated task. It depends much on well-developed experience and scientific planning. Researches based on virtual machining in this paper aims to improve the quality of NC programs. A framework of optimization system for improving the quality of NC programs is constructed based on virtual machining in Sect. 2. Several key issues such as virtual machining, machining error compensation and cutting parameters optimization are highlighted in the subsequent sections. At the end of the paper, several research achievements in NC program optimization are presented.
2 Framework of optimization system The framework of a NC program optimization system based on virtual machining is established as shown in Fig. 1. It can be functionally divided into four modules and two databases.
1) Virtual machining cell: It is the kernel of an optimization system. It is developed based on the device of an actual machining system, which includes a machine tool, a
Fig. 1 Framework of NC program optimization system based on virtual machining
NC program
Virtual Machining Cell
Syntax check report Collision detection report ......
Error report
Error Prediction Module
Cutting parameters optimization report
Visual machining process
Dynamic Database Cutting Parameters Optimization Module NC Program Revision
Optimized NC program
Common Database
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Fig. 2 The module structure of virtual machining for NC program optimization
User interface
NC Program Virtual fixture
Virtual tool Raw stock model
2)
3)
4)
5)
Interpretation of NC program Virtual machine tool
Kinematical error model Dynamic cutting system
cutting tool, a set of fixture and a workpiece. By using this cell, the machining process driven by a NC program in the actual environment can be simulated; and the data produced in the process simulation are stored into Dynamic Database, from which adequate information can be provided for error compensation and cutting parameter optimization. Before the start of virtual machining, the NC program is interpreted according to the programming regulations of NC instructions and a syntax check report is given; as the virtual machining process is going on, collision detection is also carried out according to the relative position of the moving geometrical models of virtual machining Cell; finally, virtual workpiece is produced for accuracy prediction and evaluation. Error prediction module: By utilizing the incorporated error model of the machining system or by using virtual inspection [4], the error value corresponding to each sampling point is calculated so that the nominal NC program can be modified according to it to realize off-line error compensation. Cutting parameters optimization module: Aiming to the optimization objectives (e.g., the highest productivity, the lowest cost), the spindle speed and feed rate are optimized by using an optimization algorithm under the constraints of the machining system and machining quality requirements. The cutting parameters optimization report is given, which is a preparation for modifying the cutting parameters in the NC program. NC program revision module: According to the simulation reports, the nominal NC program is interactively or automatically revised to generate an optimized program. Common database: It stores and manages the data irrelevant to machining process. The data is composed of tool information, machine characteristics, material property, etc. A common database can be managed via general Database Management Systems (DBMS) such as Microsoft ACCESS.
Syntax check report Geometrical Transformation
Error Determination sub-module
Collision Detection
Tool-workpiece Relative Displacement
Machining process simulation sub-module
Virtual machining Cell
Collision detection report Visual examination Virtual Workpiece Dynamic database
6) Dynamic database: Actually, it is an internal data buffer used to store and manage the run-time data generated in the machining process. Dynamic database includes the data extracted from NC program (e.g., coordinates, spindle speed, feed rate), the data calculated during simulation (e.g., cutting depth, cutting width), etc. The data, used for error prediction and cutting parameter optimization in dynamic database, is managed by the virtual machining cell, and initialized at every start of virtual machining. To smoothly develop an off-line optimization system for NC programs based on virtual machining, key issues such as virtual machining, workpiece model and cutting parameters optimization are discussed in the following sections in detail.
3 Virtual machining In a virtual machining cell, the actual machining system driven by a NC program can be simulated after it is modeled. Reliability verification is also carried out in the process. The information generated in the process is managed by Dynamic Database, and several reports are also generated. Figure 2 shows the module structure of virtual machining for NC program optimization. 3.1 The device models of virtual machining Generally, a real machining system is composed of a machine tool, a set of fixture, a piece of workpiece and a cutting tool. Corresponding to it, a virtual machining system is constructed with four device models. They are a virtual machining tool (VMT), a virtual fixture, a virtual workpiece and a virtual tool [4]. The device models are developed according to their own geometrical structures
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and specifications. Their geometrical models can be modeled with CAD systems by measuring the real devices. For a device, its functional behaviors, constraints and kinematics should also be modeled in addition to its own geometrical model. 3.1.1 Virtual machine tool (VMT) VMT, one of the key models of virtual machining, mainly includes three sub-models. They are geometrical model, kinematical model and error model. The geometrical model is used for movement simulation and reliability verification. It can be modeled in CAD systems. The kinematical model deals with the movement control of VMT driven by NC instructions. A machine tool can be abstracted as a kinematical chain of several rigid bodies. The multi-body method [5] can be utilized to model it. The error model is to characterize the kinematical/dynamic errors of a machine tool. It is a key model for error prediction and error compensation. The error model [6] of VMT includes geometrical error model, kinematical/dynamic error model. The kinematical error is resulted from the geometrical errors of all motion axes. It depends much on the structure of the machine tool. The dynamic error is caused by load variation and thermal deformation during machining. 3.1.2 Virtual fixture A set of fixture is used to position and keep workpiece at a desired position. It is fixed to the table of a machine tool. Therefore, the virtual fixture can be attached to the table component of VMT in its working status when it is modeled in a CAD system. The geometrical model of the virtual fixture is used to detect potential collision in virtual machining. 3.1.3 Virtual cutting tool During the actual machining, a cutting tool is driven by the machine tool to remove the material from the raw stock. Its geometric/performance characteristics have a direct and significant effect on the machining accuracy and roughness of products. In virtual machining system, a virtual cutting tool mainly provides the geometrical information of the real one; information in detail such as physical properties, wear characteristics is managed by a common database. 3.1.4 Virtual workpiece A virtual workpiece is an abstract model to represent the models relevant to workpiece. Before the start of simulation process, virtual workpiece represents the model of raw stock;
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during the process, it represents the model of intermediate part; and at the end of the process, the finish part. In virtual machining, a workpiece model (hence a virtual workpiece) is one of the important models. It contributes much to simulation speed and simulation accuracy, so the representation of workpiece could be developed on consideration of its own forming characteristic. Virtual workpiece in turning The workpiece in turning usually takes a rotatory shape. From the point of a geometric modeling view, the workpiece is a swept solid part generated by revolving a closed planar curve (hence generator profile) 360° around an axis line (hence rotatory axis) in the same plane. The generator profile determines the geometrical model of the part. Therefore, the geometric model of a rotatory part can be simply represented by its generator profile in an alternative way. For the sake of computational convenience in virtual turning, the non-linear curves of profile are approximated with linear segments under a given tolerance. As a result, the generator profile is transferred into a polygon. This polygon is known as a generator polygon (GP), and using GP to represent the workpiece is known as GP_Rep (representation of a generator polygon). If the swept volume of a cutting tool is also represented as a polygon (Swept Polygon, SP), the turning process can be simulated by clipping operation between the two polygons, i.e., , GP and SP [7]. The computation efficiency can be improved significantly. Virtual workpiece in milling Due to the complexity of milling process, the representation of computerized workpiece in milling is still one of research topics of CAD/ CAM at present even it has been investigated for more than 30 years. The representation can be classified into two categories: solid modeling based representation and decomposition based representation. Solid modeling based representation such as CSG and B_Rep is with high simulation accuracy. But supporting system for geometric modeling is needed; moreover, as simulation process is going on, workpiece model is becoming more and more complex and the simulation speed is becoming more and more slow. Contrasting to it, decomposition based representation [8] such as Z_Map, Dexel and Octree) has nothing to do with the complexity of the workpiece. The representation algorithm is so simple that a machining process simulation system can be developed with less effort. This representation is widely used in many CAM systems to simulate the planned tool-path. With the development of computer technology and solid modeling technology, solid modeling based representation has been paid more and more attention for its advantages over the decomposition based representation.
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Fig. 3 Diagram of machining process simulation
Virtual workpiece
NC Instructions
Tool-workpiece relative displacement
Virtual cutting tool
Modified Geometric model of virtual workpiece Boolean Subtraction Operation
Incorporated error at sampling points
Error compensation
Cutting parameters Dynamic database
Cutting Forces
3.2 Interpretation of NC program Interpretation of NC programs is based on the NC instruction regulations of a machine tool. It is used to simulate the NC program compiling process, including syntax parsing, extraction of G and M functions, coordinates, spindle speed, feed rate, etc. According to the result of interpretation of a NC program, not only the states of VMT can be set but also the behaviors of VMT can be controlled during virtual machining. After the interpretation finished, syntax check report is given. 3.3 Geometrical transformation of components In the actual machining process, the position of every component relevant to machining is changed continuously to accomplish material cutting. In virtual machining, the change (i.e., movement) is simulated by transforming the components according to the coordinates specified by NC instructions. Meanwhile, collision is detected among the transformed components.
Optimization of cutting parameters
of virtual workpiece according to the relative position of the cutting tool to the workpiece. It is what Boolean subtraction operation of the virtual cutting tool from the virtual workpiece. The cutting depth and cutting width are also calculated and stored in Dynamic Database at each cutting step in addition to coordinates, spindle speed and feed rate. Based on the data, physical parameters can be predicted.
4 Optimization of cutting parameters The optimum cutting parameters are their maximum values that can meet the quality requirements of a desired part, and take advantage of the performance of a machine tool and a cutting tool under the constraints of the machining system. In other words, the optimum spindle speed and feed rate are determined optimally within the bounds of the given
Models of workpiece & tool
3.4 Reliability verification of NC program The consequence of collision and interference is usually very serious in NC machining, especially in multi-axis machining. Therefore, collision detection is an important aspect in NC program optimization. In virtual machining, collision is detected among the transformed components of the virtual machining system. In order to speed up the process, detection is carried out between several selected model pairs that will collide potentially, such as cutting tool - fixture, tool holder - fixture, tool holder - workpiece, etc.
Accuracy prediction
Virtual workpiece
NC program
Read NC instruction block [i] Virtual machining Extract s, vf from NC instruction block [i] and calculate v and fz Calculate cutting depth ap: ap = Z0 - Z1 Calculate cutting width ae Save ae , ap at coordinate (x, y, z)
3.5 Machining process simulation i ++
In machining process simulation, not only the material removal process is simulated, but also the physical parameters such as cutting force, vibration and machining accuracy are predicted as shown in Fig. 3. Material removal simulation is to continuously modify the geometrical model
N
End of NC program Y End
Fig. 4 Diagram of cutting parameters acquisition based on virtual machining
Int J Adv Manuf Technol (2008) 36:908–917 Fig. 5 Optimization procedure of cutting parameters
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Virtual machining Parameters of optimization algorithm
ae , ap
Constraints of machining system
Machine tools
Performance evaluation
constraints of machining quality
cutting tools
constraints. These constraints include the permitted maximum power, desired tool-life, the range of spindle speed and feed rate, the maximum cutting force, the upper bound of surface roughness, etc. In cutting parameters optimization, the prediction of cutting force is a prerequisite.
Fig. 6 Example of reliability verification of NC programs
Optimum cutting parameters
Optimization algorithm
Objective of optimization
Coefficients of cutting force
4.1 Cutting parameters acquirement Given a cutting tool and a workpiece, the amplitude of cutting force is changed as the cutting parameters are changed. The cutting force can be expressed as a function of cutting parameters.
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5 Error compensation 5.1 Error model of machining system The machining accuracy of workpiece is guaranteed by the position of the cutting tool relative to the workpiece. However, the error influencing factors are inevitable in machining process. The cutting tool is deviated from its ideal position relative to the workpiece and machining error is caused. The incorporated machining error can be expressed as following: E ðx; y; z; t Þ ¼ EP þ wMT EMT ðx; y; z; t Þ þ wF EF ðx; y; zÞ þ wT ET ðx; y; z; t Þ þ wD ED ðx; y; z; t Þ where Fig. 7 Prediction of cutting force
For turning: F=f (v, vf, ap) For milling: F=f (ap, ae, fz) where F v vf ap ae fz
The resultant cutting force Cutting speed Feed rate Cutting depth Cutting width Feed rate per tooth
wi EP EF ET ED EMT
the contribution weight of ith error component to the n P total resultant machining error, wi ¼ 1 i¼1 the tool setting error the deformation error caused by cutting force the wear of cutting tool the dynamic error the total error of a machine tool
One of the keys to predict cutting force is to acquire the cutting parameters. The spindle speed s, feed rate vf can be extracted from NC programs directly. The cutting speed can be calculated from the spindle speed s and the diameter of a cutting tool or a workpiece, and feed rate per tooth fz can be obtained from the spindle speed s and feed rate vf. But the cutting depth ap and cutting width ae must be obtained by virtual machining. The procedure to get ap, ae based on virtual machining is outlined in Fig. 4. 4.2 Optimization algorithm To determine the optimum cutting parameters, i.e., spindle speed and feed rate, a reasonable optimization algorithm must be utilized. Cutting parameters optimization is a multi-objective optimization issue with multi-constraints and multi-variables. Genetic algorithm (GA) provides a novel algorithm for resolving this complicated issue [9]. In optimizing process with GA, much effort would be paid on cutting parameters encoding, genetic operations, objective function, adaptive evaluation function and recombination with multi-variables. The selection of optimum cutting parameters using GA was discussed by authors in [10]. Figure 5 shows the optimization procedure of cutting parameters.
Fig. 8 Comparison of the predicted machining power to that of the experimental without cutting parameters optimization
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Among these error components, EP is random; EF, ET and ED are difficult to model in actual application. It is the machine tool that turns the raw stock into desired form by positioning a cutting tool in a desired position relative to the workpiece. The error EMT can be classified into two categories: kinematical error and dynamic error. The kinematical error is derived by the geometric errors of all motion axes and depends much on the structure of the machine tool while the dynamic errors are caused by load variation and/or thermal deformation during machining. The modeling of dynamic error is very difficult. Method to compensate the dynamic error is usually based on inprocess physical experiment [11]. Therefore, to develop a practical error compensation system, the kinematical error of a machine tool can be modeled and compensated for its stability, time-independence. 5.2 Error compensation Error compensation is an effective approach to improve machining accuracy at a low cost. Therefore, much attention has been paid to it in NC machining. In virtual machining, the incorporated error calculated with the error
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Fig. 10 Experiment of error compensation
determination sub-module in Fig. 2 is directly reflected on the virtual workpiece. In virtual inspection [4], machining accuracy and error distribution can be predicted by comparing the virtual workpiece to the desired workpiece model. The predicted error along with the index of NC block causing machining error provides adequate information for error compensation. Error compensation can be realized just via modifying the nominal NC program point to point according to the predicted machining error [12]. Sometimes, interpolation method may be used. A simple way of error compensation is to directly use the error calculated with the error determination sub-module.
6 Implementations In authors’ research, several virtual machining systems for off-line optimization of NC programs on different platforms have been developed for different purposes. 6.1 Reliability verification of NC programs
9.03 8.53 8.02 7.58
There are many advantages of 5-axis machining technology in three-dimensional complex surface machining. But the tool-path of 5-axis machining is so complex that a step for reliability verification of NC programs must be carried out prior to the actual production. Virtual machining provides
Fig. 9 Comparison of the predicted machining power to that of the experimental with cutting parameters optimization
A B C
A B
C
D
Fig. 11 Sections to be measured on the specimens
D
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Fig. 12 Profiles of section B-B without and with error compensation
an effective approach to realize the step on computers. When collision occurs, the NC block relevant to collision or interference will be indicated and the interference distance will also be given in the report. To eliminate or avoid the collision or interference from the machining process, the NC program may be regenerated, or a proper fixture or cutting tool is re-selected. Take the Deckel DMU50 eVolution, a high-speed machining center, as an example for reliability verification of NC programs. The structure of the machining center is quite different from that of traditional ones. The actual trajectory of a cutting tool relative to a workpiece is quite complicated and collision is prone to occur so that verification process of a NC program for 5-axis machining must be carried out prior to the actual production. Figure 6 shows the Virtual DMU50eV machining system for reliability verification of NC programs and tool-path planning, which developed on a CAD system - SolidWorks. A NC program with 375 instruction blocks for a simple pocket was verified, and the result showed that there were 470 collisions occurred between the tool holder and the part (i.e., the workpiece) (Fig. 6b,c). The reason was that the cutting tool with a short effective length was selected. 6.2 Prediction of cutting force and cutting parameters optimization The cutting parameters are composed of spindle speed, feed rate, cutting depth and cutting width (for milling). Usually, cutting depth and cutting width that determine the tool path are specified by programmers manually, and cannot be easily modified in NC programs. For off-line cutting parameters optimization of NC programs, only are spindle speed and feed rate usually considered to optimize. The cutting force is one of the direct reflections of cutting parameters. It can be expressed as a function of cutting parameters. The maximum cutting force or power is one of
the primary constraints in optimization while power is a function of cutting force and cutting speed. In other words, cutting parameters optimization must be carried out on consideration of cutting force. Cutting parameters specified in a NC block implicitly or explicitly can be acquired based on virtual machining so that the cutting force can be predicted [13]. Then, an optimization algorithm such as GA is applied to search for optimum spindle speed and feed rate. Figure 7 shows the predicted cutting force in virtual machining. And Figs. 8 and 9 demonstrates the comparison of machining power without and with cutting parameters optimization for constant power objective, respectively. 6.3 Error compensation Generally, the geometrical/kinematical error of machine tools contributes much to the machining error. It can keep relatively stable for a long period and be modeled easily. Therefore, the improvement of machining accuracy with error compensation will be effective and remarkable. On consideration of the positioning change of the workpiece or cutting tool in each setup, the coordinates of reference point are required in error prediction and compensation. According to the error model, errors calculated or predicted based on virtual inspection [14, 15] are used to modify the
Table 1 Result comparison of error compensation (Roundness) Section
Without compensation (mm)
With compensation (mm)
Improvement rate (%)
A-A B-B C-C D-D
0.0248 0.0198 0.0162 0.0287
0.0198 0.0137 0.0123 0.0204
20.16 30.81 24.07 28.92
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nominal NC program at sampling points to realize the improvement of machining accuracy. A controlled experiment was designed to machine a semi-spherical surface with diameter 80 mm. The workpiece material is LY12. The machining process took three passes, roughing milling, semi-finish milling and finish milling. A Φ16 carbide ball-nose tool was used in finish milling. The maximum cusp was specified to 0.002 mm when the tool-path was generated with a CAM system MasterCAM. In finish milling pass, the initial spindle speed and feed rate were set to 1000 rev/min and 100 mm/min, respectively. Experiments without and with error compensation were both conducted on Makino 74-A20 (a 3-axis machining center) as shown in Fig. 10, and the experimental parts were measured on a coordinate measuring machining 8068A, which was produced by Harbin Measuring and Cutting Tools Group, China. As shown in Fig. 11, four sections were selected to predict the roundness, which is a direct measure to evaluate the machining accuracy. The least square algorithm was used to process the data and predict the roundness. The profiles of section B-B without and with error compensation are shown in Fig. 12. The experiment results compared in Table 1 shows that the machining accuracy were improved remarkably.
7 Conclusions With the rapid development of CAD/CAM, NC machining technology is increasingly and widely utilized in manufacturing industries. It has become a key technology in machining of complex parts and free-form surfaces with high productivity and accuracy. To improve the reliability, productivity of machining process and the machining accuracy of machined part, much attention has been paid to optimization of NC programs. In this paper, a virtual machining system integrating reliability verification, productivity and machining accuracy improvement is outlined to optimize NC programs. Key issues relevant to develop such an optimization system are also highlighted, including virtual machining, error compensation and cutting parameters optimization. Several application systems have been developed to demonstrate the idea proposed in the paper. The application results show that off-line optimization on NC machining based on virtual machining is effective and the improvement is remarkable.
917 Acknowledgement This work was sopported by the Project of National Defense Foundation Research, the Project of 2005 Tackling Key Problems in Harbin City and the Development Program for Outstanding Young Teachers in Harbin Institute of Technology.
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