Prediction method of carbide blade chemical-mechanical polishing surface roughness

Abstract

The invention discloses a prediction method of carbide blade chemical-mechanical polishing surface roughness. The prediction method comprises the following steps of 1, designing experiment parameters, an experiment scheme and collection of experiment data of chemical-mechanical polishing carbide blades, 2, adopting an anomaly detection algorithm based on the gaussian function to preprocess experiment sample data and 3, constructing a prediction model in which a genetic algorithm optimizes a BP neural network, utilizing the preprocessed experiment sample data to conduct learning training on the prediction model, and thereby obtaining the prediction model of the carbide blade surface roughness under different conditions. According to the prediction method of the carbide blade chemical-mechanical polishing surface roughness, the anomaly detection algorithm based on the gaussian function is used for preprocessing the experiment sample data and eliminating anomaly data groups, then, the threshold value and weight value of the BP neural network are optimized by the genetic algorithm, so that a high-precision surface roughness prediction model is constructed, and the chemical-mechanical polishing efficiency is improved.

Description

technical field [0001] The invention relates to a method for predicting the surface roughness of chemical mechanical polishing of a cemented carbide blade, belonging to the technical field of mechanical processing technology. Background technique [0002] Chemical mechanical polishing has become one of the important precision machining methods for cemented carbide inserts because of its advantages of avoiding mechanical surface damage, improving the flatness of the insert and reducing thermal influence. However, in the actual processing of chemical mechanical polishing carbide inserts, the polishing process parameters are often determined through repeated experiments, and the polishing quality is controlled by experience and semi-empirical means. The above problems cause unstable processing and low production efficiency. In mechanical processing, the surface roughness of the blade is an important index to measure the quality of the blade product. Therefore, the establishment...

AI Technical Summary

Problems solved by technology

However, in the actual processing of chemical mechanical polishing carbide inserts, the polishing process parameters are often determined through repeated experiments, and the polishing quality is controlled by experience and semi-empirical means. The above problems cause unstable processing and low production efficiency.

Images