Energy consumption prediction method and system for plate laser cutting

Abstract

The invention relates to an energy consumption prediction method and system for plate laser cutting. The method comprises the following steps of (1) dividing laser cutting energy consumption data corresponding to process parameters of laser processing equipment into a training set and a verification set, (2) dividing the training set into K sub-training sets through a K-fold cross validation method, (3) optimizing an input weight and a threshold value of the extreme learning machine by using a fireworks algorithm to obtain a laser cutting energy consumption prediction model based on the fireworks algorithm to optimize the extreme learning machine, and then respectively inputting the sub training sets into the laser cutting energy consumption prediction model to train the laser cutting energy consumption prediction model to obtain K trained laser cutting energy consumption prediction models, and (4) calculating the accuracy rates of the K trained laser cutting energy consumption prediction models through the verification set, and selecting the model with the highest accuracy rate for laser cutting energy consumption prediction. The method and the system are beneficial to accurately predicting the laser cutting energy consumption.

Description

technical field [0001] The invention belongs to the technical field of production and manufacturing, and in particular relates to a method and system for predicting energy consumption of plate laser cutting. Background technique [0002] With the development of science and technology and economic society, machine tools have become the main production tools of the manufacturing industry. While machine tools bring high profits to enterprises, the energy consumption caused by them cannot be ignored. Existing research shows that more than 90% of the environmental impact of the processing and manufacturing industry is caused by the power consumption of machine tools during the processing and use phase. However, the number of machine tools in my country ranks first in the world, but the energy utilization rate is lower than 30%. The huge power consumption of machine tools and their environmental emissions have been paid more and more attention internationally. Reducing the energ...

AI Technical Summary

Problems solved by technology

At present, there are relatively few studies on the prediction of laser cutting energy consumption using the second method, so the present invention provides a method for predicting energy consumption based on machine learning algorithms

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