BP neutral network heavy machine tool thermal error modeling method optimized through genetic algorithm

Abstract

The invention discloses a BP neutral network heavy machine tool thermal error modeling method optimized through a genetic algorithm. Through the establishment of the structure of a BP neutral network, global optimization is conducted on the initial weight and threshold of each layer of the BP neutral network through a training sample. After the error objective is set, global optimization is conducted on the initial weight and threshold of the BP neutral network structure through the genetic algorithm, and the optimal weight and threshold found by the genetic algorithm is substituted into the BP neutral network to be conducted with sample training. Based on the decline principle of the error gradient, quick search is conducted near the extreme point until the training is end and thermal error prediction model is obtained. Finally, robustness testing is conducted on the obtained thermal error prediction model. The global optimization is conducted on the initial weight and threshold of the BP neutral network structure through the utilization of the genetic algorithm, the self-characteristics of the BP neutral network is overcome, and the quickness, the accuracy and the robustness of convergence when the optimal weight and threshold is trained can be improved.

Description

technical field [0001] The invention relates to the technical field of digital manufacturing, especially for heavy-duty machine tools and ultra-precision machining machine tools, and specifically refers to a BP neural network thermal error modeling method for heavy-duty machine tools optimized by a genetic algorithm. Background technique [0002] Heavy-duty machine tools are important equipment in the manufacturing field, and their processing performance is one of the main symbols of the development level of a country's manufacturing industry. Heavy-duty machine tools are used for processing high-end equipment and are widely used in aviation, aerospace and high-end equipment manufacturing. Due to its own material, structure and processing environment, there are geometric errors, thermal errors, servo errors, positioning and clamping errors and other factors that affect the stability of machining accuracy during the processing of parts. Due to the large number of heat sources...

AI Technical Summary

Problems solved by technology

In actual modeling, the data of the original data sequence may not all be used for modeling. If different data are taken from the original data sequence, the established model will be different. The robustness of this method needs to be further improved.

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