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CUTTING VALUES PREDICTION WITH NEURAL NETWORKS A. Nestler, G. Schulz Dresden University of Technology, Institute of Production Engineering, Mommsenstr. 13, D-01062 Dresden, Germany Abstract Flexibility and intelligence are indispensable factors in automation and manufacturing. For such demands, intelligent information systems are required, especially considering the technological data type. The process setting parameters have to be determined in the best suitable way. A central point of technological data are the cutting values feed and speed, especially because of their influence on cost, time, quality und security of the manufacturing process. The rapid development of new advanced materials, tools etc. cause the lack of qualified, actual machining values. A creative approach to make available technological data are artifical neural networks (ANN). Neural networks offer a possibility to solve these problems by training networks using known cutting values and the generalizing ability in a later utilization phase. Considering different data sources for cutting processes are possible. The authors demonstrate results of a project based on neural network and technological database system. Keywords: Milling Modeling, Cutting Values, Technological Database, Neural Network 1 Introduction The planning and manufacturing processes need an easy access to reliable technological information. The usually existing conventional methods for technological databases are insufficient. Selection and prediction of appropriate machining process parameters is critical. Tables from tool manufacturer or machining handbooks give only a wide range of cutting values for special cutting conditions. Real good cutting values for specific cutting operations are usually gained through experience in long periods. It is still very common that personal experience is used to determine cutting values because the complexity of machining conditions cannot fully be described with calculation methods. The handling of the given planning data before manufacturing, the estimation of data during phases of testing and the retrieval of realized cutting values in connection with a database near the process are not practically solved. General characterisitcs for technological data are shown in the figure 1. Incomplete, uncertain, heterogeneous information Flexible solution fields, environments, constraints Improved part and cutting materials Distributed and/or cooperative problem solving Alternative possibilities, strategies Global informations for local users Intelligent usage of resources Machine tool - clamp workpiece material cutting material - adapter tool - manufacturing operation manufacturing condition Complexity of machining conditions Cutting values (feed, speed, ...) Figure 1. General aspects to handle technological data Technological information play an important role in computer integrated manufacturing. Computerized techniques influenced the development stages of technological data dissemination drastically - from simple ‘data collection’, over the level ‘integration of applications’to the ‘global communication’(Fig. 2) Cutting values dissemination Best value collection Tool management Electronic catalog Database Internet Applicable methods for the main functionality of databases has to be extended. Different methods for data handling are available for this purpose, esp. for the treatment of numerical data (Tab. 1). 3 Determination of cutting values Table Handbook Figure 2. Levels of cutting data dissemination 2 The determination of cutting values includes different methods parallel (Fig. 3). Technological database The planning and manufacturing processes need an easy access to reliable technological information. Such a database is an decision supporting system for planning and manufacturing task. Relevant data ranges for technological data are: • Tools, measuring - and testing instruments • Clamping devices, fixtures, chucks, stock • Workpiece and cutting materials, cutting values • Machine tools, attachments • Manufacturing features, operations and cycles. Almost of all these data ranges are the research and development dominance of tool-, resource management- or machining recommendation systems. A new technological database quality can be gained if there can be considered various data sources by means of different data processing methods alternatively. In practice can be found relevant data sources of the cutting data area: • Firm-independent recommended values • Firm-dependent best values • Personal experience and intuition • Results of calculation models. The crucial point of such database are the cutting data. The cutting values depend on a lot of influencing parameters and require complex connections. Therefore different possibilities exist for the availability of cutting values. Table 1. Applicable methods for extended data management Method for data handling Example empirical model constraints, optimization statistical data analysis detection of statistic dependencies explorative data analysis detection of internal data structure fuzzy analysis handling of unsharp, uncertain information artifical neural networks knowledge acquisition, learning data, generalising decision trees stuctureing knowledge genetic algorithmus optimization Information System Handbook Table MEMO Intuition Calculation Experience Programm 1 - Dokument 2 Datei Bearbeiten Optionen Ansicht Ändern Hilfe Constraints (%) Mathematical model Neural network model Fuzzy logic model Stand-alone or hybrid intelligent structures Figure 3. Possibilities for cutting data determination and calculation methods Conventional based methods for different application situations are required: the generation of results from intuitive actual cutting, personal experience or calculation results. The calculation contains mathematical models and knowledge based methods (neural net, fuzzy logic) in connection with database. Applications of mathematical models for technological data determination give useful start parameters for using in different technological software systems [NES-92]. Neural networks are applied for knowledge acquisition and processing in intelligent machining [MON-93] and process modelling [KLO-97]. The computerised model neural network is directed to automated knowledge processing. At present, neural networks are especially used to solve sophisticated problems. Unknown correlations between input and output parameters can be learned and reproduced by neural networks. Another possibility exist with fuzzy logic models for metal cutting [BAR-96]. The knowledge can extracted from a catalog or handbook. With the help of linguistic variables (parameter - very low, low, medium, high, very high) can described fuzzy expressions. 4 Neural networks for cutting values The determination of cutting values with the help of neural networks is a new alternative method. Examples, demonstrated for milling manufacturing, proves that neural networks are capable of learning nonlinear functions from data sources [FIC-97]. The procedure for establishing an applicable network consits of the following main steps: • description of the problem • data selection • design of neural network • training and test of neural network • practical performance test • integration of net in a framework of an application The first step includes considerations about the practical target and the purpose of the later application. In preparation the available data sources will be analysed. 5 Strategies for data selection A good quality of the net output depends to a high degree on a carefull selection and preprocessing of training data [SCH-98]. The selection of relevant data out of all available data sources and the combination of different data is a task that cannot be done automatically. Intelligent software can only support the manual work of an experienced person (Fig 4). Cutting parameters, different data sources do have a different reliability. Data that was gained through experience is the most valuable one followed by firm-independent data collections and standard values of tool and machine suppliers. internal data external data selection, evaluation, adaption, ... data analysis, data mining, tools, add-ins data set relevant net data division training data test data Figure 4. General preprocessing steps In general, the structure, parameters and used classificators out of different data sources are quite different. They have to be transformed and made compatible. The data structure of the most relevant in-house data sets are in most cases the standard for the structure. All other interesting external data sources have to be transformed into this structure. One major problem are missing parameters. Special neural networks, self organising maps (Fig. 5), are capable of estimating single missing parameters and completing data sets. With a special network structure it is possible to handle data with different, incomplete input pattern. Figure 5. Self organising maps (SOM) for input data with gaps Now, the best and most reliable data sets have to be selected. For a selection of training data sets, only data sets which are very close neighbours within the field of input parameters can be compared and selected. Data sets of minor reliability should be only selected when no better data is available within this distinct area of the problem space. Therefore, the different data sources are weighted and the multidimensional space of all interesting cutting problems is divided into numerous blocks. Within these blocks, only the most reliable data sets are selected, the other ones are ignored (Fig. 6). pa ra m ete r3 parameter 2 Machine Tool training pattern in competition, selection according to reliability data Workpiece Material Cutting Material Feed Speed Tool parameter 2 Manufacturing Parameters training pattern without competition, always selected Cutting conditions Figure 8. Input / output parameter of a net ‘cutting values’ parameter 1 Figure 6. Reference grid Many other methods and software tools are available. Their usage strongly depends on current problem. After determinating the relevant parameters, the selected data sets can be divided into the training - and test data sets (two-third and one-third). 6 Neural network model 6.1 Steps for implementation After making available the cutting database with the relevant net data follows the definition of the network structure and the training of the network with the training database. The test database is needed for a detailed test of the quality of a trained network. The result is a trained network and can integrated in a technical environment. (Fig.7). The architecture of the neurons and connections in the hidden layers are subject of adapting to the problem. In general, numerous experiments have shown, that a feed-forward, multilayer backprobagation net [ZEL-95] with two hidden layers of 10 to 30 neurons each is appropriate (Fig. 8). If there exists partial knowledge (empirical) about the problem, it’s worth it to integrate it. Is is very useful to integrate calculations of existing mathematical models into a network [SCH-97]. Because of the high flexibility and adaptability of neural networks it is possible, to create a special system for any special purpose. Because of the high complexity of cutting processes and the high number of influencing parameters, smaller networks for special tasks do work more efficient than networks that try to deal with a large variety of conditions. relevant net data input neural net calculation output application Figure 7. General processing steps Figure 9. Network structure of firm-dependent cutting values 6.2 Neural network design After classification of the relevant parameters, the training database for the neural network will be extracted. For the training of the neural network approximate or discrete cutting value tables and results of computations modules can be used. For example, Fig. 9 shows a net which is trained with cutting values from a company, that mainly machines cast iron on milling machines of a similar type with many different tools. In this case, it is not useful to train the net with any information about the machine or the material, because there will not be any significant change in these parameters. 6.3 Training and test The training and test show an example ‘Sandvik catalog’. With a extract of the selected catalog data sets, the net will be trained. The training is done when the average fault of the output is lower than 10% while reproducing the training data (Fig. 10). calculate correct cutting values for different types of tools, materials, cutting conditions. Fig. 12 shows the netoutput and expected feed value when varying the tool diameter (all other parameters remain constant). Feed 1 net output 0,20 Real Value Calculated Value 0,10 0,15 0,05 0 12 15 20 25 30 35 40 45 50 Tool Diameter Figure 12. Net output for unknown tool diameter It follows the test phase. For the testing data sets the network has to calculate a correct output. The amount of data sets in a 2% output-error-class for 1000 data sets shows (Fig.11). A method for pediction test is validation of the output values by practical tests The machining experiments were carried out with cutting parameters suggested by a neural net for a tool and a material which was not subject of the training database (Experiment 1, Fig. 13). To get a better feeling about the quality of these parameters, additional cuts with a modified parameter environment were done. The setting limits are results for instance of optimization models (constraints) or from experince. In the example the prediction area for feed and speed were altered +/10 % (Experiment 2, 3, 4, 5 Fig. 13) number of data sets per error class Figure 10. Output error of training with successfull progress 700 600 training data sets: 2000 test data sets: 999 vc cutting speed vf cutting feed 500 400 300 200 100 0 -22 -18 -14 -10 -6 -2 2 6 10 14 18 22 error classes (%) vc [m/min] Figure 11. Output error frequency of the trained network 7 Practical performance test Before implementing a trained neural net into a technical environment, the performance of the netoutput and the generalising ability has to be inspected. It is advantageous, to combine different test methods. Usually, this is done through a validation data set. In addition, practical machining tests were done. The experiment based on cutting values feed and speed from a manufactureres tool catalog for milling tools. A method for testing the generalisation ability is the variation of single input parameters within a wide range - before machining. An expert is able to see if the net is doing the right thing. Varying tool, material, and process parameters is of special interest. It can be shown, that neural nets are able to limit settings 3 net output 4 1 2 5 f z [mm/U] Figure 13. Cutting values area for testing saefty of the net output First machining experiments were done on the shop floor for testing the output of a trained network. It could be proven that cutting values generated via the generalising ability of a neural net are precise enough for practical requirements. Network source: Sandvik-Catalog Experimental conditions: 35 30 25 20 15 10 not acceptable acceptable good 5 0 very good number of experiments (40 in total) milling machine: Deckel FP4A, 4kW tools: diameter 20, 25, 40 (Fa. Walter) cutting material: WTL-71 cutting depth, overlapping: 50%, 100 % workpiece material: AlCuMgPb, 210Cr46, St60, GG25 Figure 14. Experimental results An example shows the practice: The training data were taken from a Sandvik catalog. The output was generated for Walter-tools which differ in few details. The maching experiments were done with 3 different tools, different cutting depths and 4 different workpiece materials. The opinion of the machining worker about the quality of the cutting values is shown in Fig. 14., most of the values are good. 8 networks. The generation of the input data has to be as easy as possible with an intelligent support. Most of the values for the input can be extracted by the operator from a technological database (Fig. 16). Application for usage of the neural networks For a comfortable handling of the trained network, a proper user interface is to make available. Figure 15 shows the integration of trained networks into the framework of a technological database system. application technological database - ... network specific user interface C- source code trained network extended application extended data management data management technological database - CUTTING VALUES - single value ... - calculation methods - optimization - neural network technological data (training-, test data) RDBS SQL Windows Figure 15. Extended data management The user must be able to work with the net without any detailed knowledge about neural Figure 16. User interface for ANN in the framework of a database system This software and the trained network can be installed on a PC, Notebook or within the CNC. The general advantage of a neural network is, that once it is trained, it does need only a small amount of memory and computation time. A trained network can be transformed into a short C subroutine which means an enormous compression of data, compared to the training database. 9 Conclusions Artificial neural networks are able to learn from examples and, through good generalisation abilities, to apply the trained knowledge to new situations. Trained networks can integrated into an existing database system and do extended its functionality into the direction of future intelligent database assistants. Applications of neural networks can be integrated into the manufacturing process for the acquisition and handling of cutting values. Neural techniques offer new possibilities to store, handle and retrieve technological informations on shop floor, for NC-programming and for planning tasks. 10 Acknowledgement This research was a part of the project " Knowledge based cutting value determination with the help of neural networks" funded by the German Association for Research (DFG). 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