Processing equipment process parameter optimization method and system

Abstract

The invention discloses a processing equipment technological parameter optimization method and system. The method comprises the following steps: collecting production line data in real time and obtaining a target value of a quality index; setting an initial value of a process parameter and a preset deviation value of the quality index; utilizing a section of historical production line data which comprises a current moment and the process parameters to respectively predict and obtain a predicted value of the quality index and an uncertainty range of the quality index; determining an optimization target according to the preset deviation value and the prediction value of the quality index and the uncertainty range of the quality index, and optimizing and updating the process parameters and the preset deviation value; performing an iterative optimization process by utilizing the updated process parameters and the preset deviation value until a termination condition is met; and after iterative optimization is completed, outputting the value of the current process parameter as the optimal process parameter under the current incoming material information and the environmental parameter. According to the invention, the accuracy and stability of the process parameter optimization result can be improved.

Description

technical field [0001] The invention relates to the field of equipment control, in particular to a method and system for optimizing process parameters of processing equipment. Background technique [0002] Process parameter optimization is an important technical means to improve product quality and achieve "zero" defective products. There is a strong demand for process parameter optimization in tobacco, chemical, advanced manufacturing and other industries. A process can be simply divided into three stages: before processing, during processing and after processing. Before processing, it is necessary to obtain the information of incoming materials and current environmental parameters, such as shape, size, temperature, humidity, etc.; during processing, the processing equipment will be adjusted Process parameters, such as processing time, temperature, pressure, speed, etc.; after processing, it is necessary to detect product quality indicators, such as yield, temperature, humi...

AI Technical Summary

Problems solved by technology

However, in practice, it is found that the accuracy of the prediction model is often not satisfactory, and the deviation of the prediction results will directly affect the optimization goal of process parameter optimization, which will lead to errors in control recommendations

Moreover, after the model accuracy reaches a certain level, the space for further improving the model accuracy is limited, even almost impossible, and the cost will be huge

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