Neural Networks are widely used as tools for modeling or for decision making (including automatic control) in many areas of applications. Like many other tools ...
APPLICABILITY OF NEURAL MODELS FOR MONITORING AND CONTROL OF SELECTED FOUNDRY PROCESSES Ryszard Tadeusiewicz, Henryk Poácik AGH, al. Mickiewicza 30, Kraków, Poland {rtad, polcik} @ agh.edu.pl
Abstract Neural Networks are widely used as tools for modeling or for decision making (including automatic control) in many areas of applications. Like many other tools Neural Networks can be easily applied for one tasks where their use for other problems is connected with many difficulties and inconveniences. In the paper some selected problems related to application of neural network for foundry processes are presented and discussed. The basis for the evaluation of applicability of neural networks for problems under consideration are both information collected from the very wide bibliography as well as own research results obtained by the authors during experiments with application of neural network to modeling of selected foundry processes.
1.
INTRODUCTION
The purpose of the work was selection of optimal neural models for some foundry processes (e.g. heat and mass interchange, physical and chemical transformation during cooling and crystallization processes, casting and metal forming), as well as forecasting of properties of foundry products (e.g. its quality including prediction of many types of defects - for example forging ingots). Neural model can be also used as an element of automatic control systems used in foundry industry. The simulation of the dynamics of foundry processes can be very useful for researchers discovering metallurgical processes and for technologists developing new foundry technologies and new foundry machines, tolls, technological components and so on. Usefulness of various types and different structures of neural networks for modeling and forecasting of various systems is known yet very well. Nevertheless application of the networks to prediction of the foundry product properties, prior to 189
studying them with laboratory methods, is relatively rare discussed in bibliography. Similarly detail results of the industrial applications of neural networks as a tools for technologists are also very rare in the bibliography, maybe because of technological secrets hidden and patents prepared by many companies. It means the new research must be conducted in this area and many new discoveries are necessary. The huge number and variety of chemical, physical and technological factors, which must be taken into consideration during designing of foundry processes, development of technologies and prediction of properties of products make computer modeling a very attractive alternative to costly experimental studies. The problem of using neural networks for foundry industry and for scientific research of cast processes can be found as almost untouched by other authors. Also very detail searching in bibliographic databases do not lead to the selection and discovery of proper papers. Therefore discussion of the bibliography, given in next chapter, is rather not very comprehensive.
2.
OVERVIEW OF SELECTED RESULTS OF NEURAL NETWORK APPLICATIONS IN FOUNDRY PROBLEMS PRESENTED BY OTHER AUTHORS
Artificial Neural Networks (ANN) are necessary for foundry industry because are effective tool in modelling of non linear multi variable relationships. All other methods are less effective. In fact there are many academic methods for modelling of foundry processes, such as differential equations, Petri nets and several types of automata. All these methods did not meet great success when proposed for industrial use, primarily because they are application depended. In addition, contemporary industrial systems are hundred times more complex than typical systems found in academic papers. The dependence between application and formulation method seems to be avoided with a recently proposed new method, which is based on neural networks. Artificial Neural Networks models can be adapted to the real needs and are not application depended, because are oriented toward imitation of behaviour, not detail imitation of processes. In fact neural network models cal be referred as black box models. This general neural model black box can be used in foundry industry in many ways. One of the most important is application to the automatic detection of flaws through non destructive testing. In this area interesting results are presented in paper [1]. Neural classifiers are used in the recognition of flaws in the radioscopic inspection of cast aluminium pieces. The experiments resulted in perfect classification of 22936 hypothetical flaws, of which only 60 were real flaws and the rest were false alarms.
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Another type of applications is using of artificial neural network as a tool for improving laboratory investigations in foundry industry. Advantages and possibilities of such type application can be evaluated on the base of paper [2] in which classification of gray cast iron according to the graphite morphology was performed by artificial neural network. The goal was achieved by means of the texture feature extraction and pattern classification issues. The images was obtained from the metallographic electron microscope. The textural features was extracted (mainly based on the fractal parameter, roughness parameter and regression), and comparisons was made between textural modes. The classification was performed through a multilayer backpropagation neural network which (as everybody know) is based on a kind of feedforward neural network. Very impressive applications of neural networks are related to the material properties obtained during metallurgical processes, especially cast. Good example of the paper of such category maybe [3]. This paper is dedicated to the application of artificial neural networks in building prediction models of mechanical properties of new high speed steel, including predictions of hardness (H) and impact toughness (Ak) according to quenching and tempering temperatures (T1, T2). Multilayer backpropagation (BP) networks were created and trained using comprehensive datasets. As reported the authors very good performances of the neural networks prediction was achieved. The prediction values sufficiently mine the basic domain knowledge of heat treatment process of HSS. This way the new application field of neural network was developed and tested.
3.
NEURAL NETWORK MODELING OF THE THERMODYNAMICAL PROCESSES
Most applications of neural networks in foundry industry and in metallurgical research can be related to heat exchange analysis. Until now there are not papers presenting such kind of models (or we can not find it). Nevertheless some examples of successful applications of neural networks to the modelling of thermodynamical processes gives us hope, that this can be good way for solving many scientific and practical problems in foundry. First example maybe paper [4]. In this paper neural network (of RBF type) is used to the modelling the HVAC (heating ventilating and air conditioning) systems. As the author reports, the model build on the basis of neural network application was more precisely and more useful for control purposes than previously used models based on the physical description of the system and differential equations. Some of authors presents application of neural networks as a models of selected tools used also in foundry industry. For example in paper [5] authors reports the predictions of laser cutting packages QFN (quad flat non lead) using neural network learned by Levenberg Marquardt and backpropagation algorithms.
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Foundry processes are in fact both mechanical and thermodynamical ones. Therefore neural models of other thermodynamical processes modelled and controlled by means of neural networks can be very good inspiration for every research works dealing with foundry processes. Example of the paper giving good example of neural network modelling of thermodynamical processes is paper [6], describing model of such type for superheated steam. As to the superheated steam temperature control system has large time constant, long time delay and time varying in thermal power plant, in this paper a control strategy of internal model control based on parallel self learning neural network is presented. Authors reported good ability of the neural network to identify the model and inverse model of the object. The main goal is obtained by division of the control system into two processes: control process and parallel self learning process. Control process realizes the function of the internal model control, which includes the NNM, NNC and a feedback robust stable controller (RC). The parallel self learning process is used to train the NNC and then its weights are copied to control process online. Simulation results show that this strategy has perfect control performances, strong robustness and self adaptive ability. Generally, for modelling of heat exchange processes, where the various parameters must be taken into account, neural networks are one of the best tools, as was proved in many papers, for example [7].
4.
TECHNOLOGICAL APPLICATIONS OF NEURAL NETWORKS IN FOUNDRY AND METALLURGICAL INDUSTRY
The application of neural network can be related to the different application on foundry industry. For example paper [8] shows neural network as an intelligent tool for fracture forecast. In this paper we propose applications of neural networks in automatic control of foundry processes. Until now nobody tried make such application, nevertheless some interesting results of neural control of complex thermodynamical processes can be found in bibliography and used as a source of inspiration. The example of such article can be paper [9] in which the neural network based control system is developed to allow the conversion of a gasoline ECU to a bi fuel form with compressed natural gas at minimal cost. Another interesting example in foundry industry is discussed in paper [10]. In this paper neural networks are applied as an element of virtual metrology (VM) used in semiconductor foundry for wafer fabrication. This proposed method designs key steps to establish a VM control model based on neural networks and to develop and deploy applications following SEMI EDA (equipment data acquisition) standards.
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In the paper [11] the optimal operating conditions of a gold stud bumping process were determined using neural networks for a microelectronic packaging foundry. Artificial neural networks (ANN) modelling was adopted in this case to establish the relationship between the operating parameters and the bump properties with the experimental data. Some optimization cases of the bumping process with constraints were evaluated using the optimization scheme. Another approach of neural network model for metal forming is described in the paper [12]. This paper describes the application of neural network techniques to sheet metal bending process, particularly for the prediction of springback phenomenon and bending part final geometry (final radius and bending angle). Springback is an important unwanted change in shape causing accuracy problems. Traditional and new simulation techniques (FEM) of springback minimizing are laborious trial and error procedures that involve long cycle times and cost increases. To reduce the trial an error procedure, an artificial neural network (ANN) model is developed as an approximator. A back propagation neural network model has been developed using experimental data from several tension and bending tests performed on aluminium and stainless steel. The convergence of the mean square error in training came out very well and the performance of the trained network has been tested with unseen kept back data from experiments and found to be in good agreement. Another example of metal elements mechanical properties prediction by means of neural networks is presented in paper [13]. In the manufacture of rolled steel from a hot strip mill, the final mechanical properties, such as yield strength, ultimate tensile strength and elongation to fracture, are important requirements specified by the customer. The use of mathematical modelling techniques such as multiple regression analysis, or computational developments such as artificial neural networks, can result in the creation of acceptably accurate predictive models. However, the accuracy of any predictive model will depend on the quality of data used in its creation, and thus a brief statistical analysis of the mechanical property data used for model development is discussed, In the paper [13] a comparison of the application of linear multiple regression, non linear multiple regression and non linear neural networks is made for various steel families using data taken from the Corus Port Talbot hot strip mill. A statistical summary of their relative predictive errors is given, and although all three are comparable, the non linear, black box approach of a suitably structured neural network provides overall more accurate predictive models than the use of linear or non linear multiple regression. At the both previous references neural network was used for prediction of physical (mechanical) properties of the formed metal elements. In contrary to this in paper [14] the predictive model of sinter chemical composition was developed to predict R (CaO/SiO2), TFe and SiO2 in metallurgical factories. Based on the backpropagation neural network algorithm, it used the predictive result as a precondition. An expert system was designed to assist in controlling the sinter 193
chemical composition by estimating the change of all the relative chemical components and providing the necessary adjustment. After the system commenced, the hit ratio of the predictive model was consistently over 90% and the goal of controlling chemical composition was achieved.
5.
GENERAL SCHEME PROPOSED FOR FOUNDER PROBLEM DESCRIPTION BY MEANS OF NEURAL NETWORKS
As was shown on examples described above the neural networks are very useful tool for many problems related to the foundry processes modelling, design and control. The general scheme of the application of neural networks to the problems mentioned above is given on fig. 1. Technological data
Material information
Evaluation of parameters
Process description
Fig. 1. Typical scheme of neural network application Very important role is connected with the learning process, necessary in every neural network application. On the base of observed data we must perform the adaptation process, which can tune parameters inside neural network (e.g. synaptic weights) to the needs of particular problem and particular application. The quality of learning process can be evaluated on the base of many parameters, described in details in oral presentation of this paper.
6.
CLOSING REMARKS
In the paper we try to show, how the valuable tool of general purpose – neural networks – can be used for solving of some practical and research problems related to the foundry industry and foundry processes. We can prove the thesis, that neural networks are suitable and useful tool for modeling of some foundry processes (e.g. heat and mass interchange, physical and chemical transformation during cooling and crystallization processes, casting and metal forming),
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forecasting of properties of foundry products (e.g. its quality including prediction of many types of defects - for example forging ingots), and so on. Sponsored by KBN- grant No: 3-T08B 011 27
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