外文翻譯--數(shù)控切割機(jī)的進(jìn)化規(guī)劃(英文）

1、ORIGINAL ARTICLEEvolutionary programming of a CNC cutting machineReceived: 26 March 2002 / Accepted: 17 June 2002 / Published online: 19 March 2003 ? Springer-Verlag London Limited 2003Abstract The scope of the paper is

2、to construct an auto- nomous, intelligent CAD/CAM programming system for the cutting device controller (for instance a CNC laser cutting machine tool) based on evolutionary methods. The CNC cutting device should be able

3、to optimise paths autonomously between cutting trajectories, determined by the product’s CAD model. An evolutionary GA was used for this purpose.Keywords Genetic algorith Æ CNC laser cutting Æ Path planningRela

4、ted work on automatic programming of CNC machinesConventionally CAM systems for programming of CNC machine tools are well known and commercially avail- able. In recent years automatic CNC programming systems have been ap

5、plied more and more in tool shops. There are several systems reported in the literature. The literature [1] describes a numerical controller which contains a programming device to construct a learning-processing program

6、which is combined with the entered program in order to produce the resulting CNC program of the workpiece. This system uses a new method for learning a processing program and enabling the machine operator to select the l

7、earning mode and change the CNC program. The literature [2] describes a tool path data genera- tion apparatus which can speedily and securely generate the tool path data on the basis of CAD data. The tool path data gener

8、ation includes a feature data extractor toextract features in relation to a CAD model of a work- piece, the tool-cutting data for selecting a cutting mode, the cutting method to set an optimal cutting method and a tool p

9、ath data generator. An operator does not have to input any data for generating tool path data. Tool path data can therefore be generated automatically and speedily. The dialog-oriented programming system described in [3]

10、 is used for program generation of a CNC machine. All the actions are initiated by the dialog between op- erator and CNC unit, which has the ability to process program input and download it to the CNC unit. The concept o

11、f the distributed network manufactur- ing mode is outlined in [4]. This research is concentrated on enhancing the intelligence of conventional NC ma- chine tools and their ability to communicate with the outside world an

12、d coordinate the work. The experi- mental results of the distributed network manufacturing prototype system shows that the system is intelligent and it enhances the ability of a conventional NC milling machine to improve

13、 its efficiency and quality and protect the cutting tool. A system and method for creating varying charac- teristic products from an automated production line is presented in [5]. The present system includes an auto- mat

14、ed laser-cutting device. The method comprises pro- gramming the laser cutter with a parametric computer program to receive the data file. The method may also involve an automatic power source for the laser cutter upon re

15、ceipt of the data file. In [6] an NC feature unit is proposed in order to generate tool path data in real time and is implemented in 2.5 D profile/pocket and 3D surface milling opera- tions. Aspects all systems have in c

16、ommon are that they are not intelligent and it is not possible to generate a CNC program for unknown parts workpieces. The learning ability of the systems is not presented. A concept of biological manufacturing systems (

17、BMS) that is based on biologically inspired ideas of self- organization, evolution and learning is discussed in [7].Int J Adv Manuf Technol (2003) 22: 118–124 DOI 10.1007/s00170-002-1450-8M. Kovacic Æ J. BalicM. Kov

18、acic Æ J. Balic (&) Faculty of Mechanical Engineering, Laboratory for Intelligent Manufacturing Systems, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia E-mail: joze.balic@uni-mb.sii.e. they ar

19、e better adapted to the environment. A selective operation assures the survival of the fitter individuals in the population and they advance unchanged into the next iteration, also called the next generation. The variabl

20、e operation affects one or more parental organisms, which create their offspring. After completion of selection and variation a new generation is obtained and evaluated. The process is repeated until the termination crit

21、erion of the process is fulfilled. This can be a prescribed number of generations or a sufficient quality of solutions.Genetic algorithmsSince their introduction by J. Holland [10] the use of genetic algorithms has sprea

22、d to almost all areas of research [11, 13]. A population consists of organisms. Organisms are points in the space of the solutions. An organism has co- ordinates that are called genes (Fig. 4). It is a character- istic o

23、f a conventional GA that organisms represent coded values of variables (parameters) of the mathematical model (cost function). The most widespread method is to code variables into fixed-length binary strings. The length

24、of organisms is determined with the respect to the size of the interval with which the solution is searched for andwith respect to the desired resolution of the variables. The binary representation of the organism is cal

25、led genotype and the actual value of the organism is called a phenotype (Fig. 4). The evaluation of organisms is the driving force in the evolutionary process. The quality of the individual organism is determined on the

26、basis of its ability to solve the problem. A higher probability of cooperating in basic operations of the conventional GA (e.g. reproduction, crossover, and mutation) is prescribed to organisms (solutions) of higher qual

27、ity. Thus, the fitter organisms transfer their genetic material more frequently into the next generation, whereas less fit organisms slowly die away from the population. Let us look at the basic genetic operations. The r

28、eproduction operation (Fig. 5) gives a higher probability of selection to organisms of higher quality. They are copied unchanged into the next generation. The crossover operation (Fig. 6) ensures the exchange of genetic

29、material between organisms. From two parental organisms, two offspring organisms result at the level of a genotype. Mutation (Fig. 7) at the level of the genotype ran- domly introduces new genetic material into organisms

30、. The evolutionary development of organisms usually leads to better and better solutions. In the literature it is possible to find many variants of the conventional GA that are adapted to the specific characteristics of

31、the optimisation problem dealt with. The variants differ particularly in the representation of the organisms and the use of additional or modified genetic operations [9, 12, 13]. Evolution is terminated when a terminatio

32、n criterion is fulfilled. This can be a prescribed number of generations or a sufficient quality of the solution. Since evolution is a nondeterministic process, it does not end with a successful solution in each run. In

33、order to obtain a successful solution, the problem must be processed in several independent runs. The number of runs required for the satisfactory solution depends on the difficulty of the problem.Evolutionary-programmed

34、 laser cuttingProblem statementThe evolutionary-supported laser cutting programming problem is to obtain an optimal cutting path between determined cutting trajectories.Fig. 3 Evolutionary algorithm in pseudo codeFig. 4