Surface roughness prediction method based on GA-GBRT and method for optimizing process parameters

A surface roughness and prediction method technology, applied in the field of mechanical processing, can solve the problems of reducing surface quality, increasing production costs, unreasonable selection of processing parameters and machine tools, etc., to improve test accuracy, improve processing accuracy, and improve generalization ability Effect

Active Publication Date: 2019-05-31
GUIZHOU UNIV
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Problems solved by technology

Unreasonable selection of processing parameters and machine t

Method used

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  • Surface roughness prediction method based on GA-GBRT and method for optimizing process parameters
  • Surface roughness prediction method based on GA-GBRT and method for optimizing process parameters
  • Surface roughness prediction method based on GA-GBRT and method for optimizing process parameters

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Embodiment 1

[0116] Example 1. A method for predicting surface roughness based on GA-GBRT is carried out in the following steps:

[0117] a. Collect turning experimental data to form a data set, and divide the data set into training group data and test group data; for example: collect 60 groups of experimental data to form a data set, wherein the training group data is 50 groups, and the test group data is 10 groups;

[0118] Use the training set data to train the key parameters of the GBRT model;

[0119] The data set includes cutting parameters and corresponding surface roughness; the cutting parameters include cutting depth a p , cutting speed V and feed rate f; the key parameters include the number of iterations of promotion M, the maximum depth D of the individual regression estimator and the learning rate v;

[0120] b. Parameter encoding and population initialization: Randomly generate a chromosome sequence for increasing the number of iterations M, the maximum depth D of the indi...

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Abstract

The invention discloses a surface roughness prediction method based on GA-GBRT and a method for optimizing process parameters. The method comprises the steps of: collecting data to construct a data set, and dividing the data set to training set data and test set data, and employing the training set data to perform training of key parameters of a GBRT model; b, performing parameter coding and population initialization: randomly generating a chromosomal sequence for increasing the number of iterations, the maximum depth of the individual regression estimator and the learning rate; c, employing the k-folded cross-validation method to train the GBRT model, and employing the genetic algorithm to calculate the fit goodness fitting value of each individual; d, when the number of cycles does not reach the maximum number of iterations, allowing the population to be selected, crossed and mutated to produce a new generation of populations, and continuously performing training of the GBRT model; and e, repeatedly performing the steps c and d until the number of cycles reaches the maximum evolution algebra or exceeds the maximum number of iterations to obtain the optimal model parameters. The surface roughness prediction method based on GA-GBRT and the method for optimizing process parameters are high in test precision and superior in prediction performance and improves the surface processing precision of the workpiece.

Description

technical field [0001] The invention relates to the technical field of mechanical processing, in particular to a GA-GBRT-based surface roughness prediction method and a process parameter optimization method. Background technique [0002] Establishing and controlling the surface quality prediction model of difficult-to-machine materials in production is a prerequisite for sustainable manufacturing. AISI 304 stainless steel has good performance in high temperature, high humidity, corrosive environment. Therefore, AISI 304 stainless steel is widely used in high-tech industries such as medical equipment, aerospace, shipbuilding, and nuclear power. However, in the process of processing AISI 304 stainless steel, there are problems such as large cutting force, high cutting temperature, severe work hardening, difficult chip breaking, short tool life, and difficult control of surface quality. It is a typical difficult-to-machine material. Surface roughness is an important index to ...

Claims

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Application Information

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IPC IPC(8): G05B19/408
Inventor 周滔何林吴锦行邹中妃杜飞龙杨肖委
Owner GUIZHOU UNIV
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