Research On Optimizing Direct Control Technique Of Green Sand Quality Based On Fast Ingredient Testing Method

The quality control of green sand has been the important task in domain of foundry all the while. Because there is no fast testing method for active clay, most of the green sand quality control systems at present work in direct mode, from performances testing to ingredient controlling. Based on a fast ingredient testing method, a direct control technique of green sand quality is investigated in this dissertation. The research includes experimentally studying of the relationship between conductive capabilities of bentonite-bonded molding sand and its active clay and water content, selecting the parameters of ingredient testing circuit, modeling of the artificial neural network for green sand ingredient calculation, developing the distributed control system for green sand quality. The investigation results have proved that the optimization control of green sand quality can be realized in direct mode, from ingredient testing to ingredient controlling, based on fast ingredient testing method.The relations between conductive capabilities of bentonite-bonded molding sand and its main ingredient, active clay and moisture, are confirmed by experimental research and theoretical analysis. The equivalent model of series RC for sand specimen conductivity is put forward, the nonlinear relationships among the model, equivalent resistor Rs and equivalent capacity Cd, and various factors such as electrode shape, amplitude and frequency of external exciting power source and green sand ingredient are analyzed qualitatively. A pair of electrodes in arc-shaped are embedded in inner wall of specimen tube, the frequency and peak value of AC exciting power source with sine wave are 1kHz and 10V respectively, the amplitude of DC exciting power source is +9V. The relations among green sand compactability, active clay, moisture and ratio of water to clay are studied, and the influence of dead clay on compactability in returned sand is also analyzed. The green sand ingredient and compactability fast testing unit, made up of an automatic sampling machine and a testing slave, is developed to prepare a standard sand specimen and test conductive parameters automatically. The automatic sampling machine works under the drive of compressed air and the working pressure is 4.5×105pa. During the process, the compacting pressure and displacement of compressing head are measured simultaneously and compactability of green sand is calculated by the testing slave. The testing range of compactability is 10~60%±1%. Cooperating with host computer, the testing unit can display and print the active clay and moisture of green sand within 10 seconds.An artificial neural network model to solve the active clay and moisture of green sand is designed. The model is a three-layer BP network with 4 input nodes and 2 output nodes. The input nodes represent green sand AC conductivity, original DC conductivity, variety rate of DC conductivity and compactability. The output nodes represent green sand active clay and moisture. The network includes a hidden layer with 6 nodes. In order to overcome the influence of instability of moulding materials on precision of ingredient calculation, some innovations of BP algorithm are taken to improve the generalization and robustness of BP network. The precision of solving active clay is±0.3%, and the moisture is±0.2%.Based on the fast ingredient testing method, a digital PID feeding control algorithm is put forward to control the feeding operation of the sand muller. Fuzzy rules are set up by expirical data from mixing experiment, and PID parameters are adjusted by fuzzy self-tuning method. A feeding control unit, including an automatic feeding machine and a feeding control slave, is developed and it is able to feed ingredients in manual, automatic and closed-loop modes. The automatic feeding machine consists of screw feeders, weighing filler and water adding unit. The feeding control slave is used to run the PID algorithm, and control the operation of feeding machine.A distributed control system including two-level computers is constructed. The host computer is used to collect and display field measuring data in real time, save and analyze them, run artificial neural network program and set all parameters of the system. The tasks of slave computers are controlling sampling machine, collecting and uploading information parameters and feeding green sand ingredients quantificationally. The system has the function of automatic on-line trouble shooting, monitoring the operation states of system key parts in real time and supervising them in grade, insuring the stability and practicability of system to meet the requirement of real production in foundry workshop. Experimental running result indicates that the control error of green sand ingredients with optimizing direct control system developed in this thesis are active clay<±0.5%, and moisture content<±0.3%.