Introduction
This paper introduces the sintered process control expert system, which is a professional system developed to infer knowledge from the sintered process. The aim of this expert system is to improve the efficiency and success rate of the sintering process and reduce costs. The system is based on a series of artificial neural networks, which are used to learn parameters from the sintering process and then infer conclusions.
The sintered process control expert system is comprised of three main components: the expert system, the data acquisition and the artificial neural network. The expert system itself is a set of IF-THEN logical rules that are based on established sintering parameters. These parameters include sintering time, temperature, atmosphere and other parameters that will affect the outcome of the sintering process. By providing these parameters to the expert system, it can infer a conclusion about the expected outcome.
The data acquisition component is responsible for collecting the necessary data from the sintering process. It measures the temperature, atmosphere and other parameters that can be used to infer conclusions. It also collects the outcomes of the sintering process and stores them in a database.
Finally, the artificial neural network component of the sintering process control expert system is used to learn patterns from the data collected by the data acquisition component. By training the neural network with collected data, it can learn to make predictions and infer conclusions. Based on these predictions and conclusions, the expert system can adjust parameters to ensure a successful sintering process.
In conclusion, the sintered process control expert system is a powerful tool for improving the success of the sintering process. It can reduce the cost of the process and improve the efficiency of the sintering process. By inferring knowledge from the sintering process, the system can make more accurate decisions and take appropriate action. The data acquisition component helps to collect the necessary data and the artificial neural network component aids in learning the necessary patterns for inference. In the end, the expert system is able to provide more accurate and timely decision making for optimal sintering results.
