Multi-target process parameter intelligent optimization method based on multi-algorithm fusion

Abstract

The invention discloses a multi-target process parameter intelligent optimization method based on multi-algorithm fusion. The method comprises the steps of firstly obtaining surface shape data of a machined part; then establishing a prediction model and an optimization model, and generating and outputting a prediction result value by the prediction model by adopting an improved generalized regression neural network IGRNN algorithm; inputting into an optimization model, and calculating a target value of an individual randomly generated in an algorithm in the optimization model; finally, building a technological parameter decision model, and determining technological parameters finally used for actual machining through a principal component analysis method PCA. According to the method provided by the invention, the optimal process parameters can be automatically obtained based on the sparse data, and each target does not need to be weighted manually, so that the implementation of intelligent manufacturing is facilitated. An improved grey wolf algorithm is adopted to carry out intelligent optimization of smoothing factors, so that the overall prediction precision of a prediction modelis improved, principal component analysis is used for selecting optimal process parameters, human interference is avoided, weighting and evaluation are automatically carried out on each target, and therefore, the level of automatic parameter determination is improved.

Description

technical field [0001] The invention relates to the technical field of parts processing technology, in particular to an intelligent optimization method for multi-objective process parameters based on multi-algorithm fusion. Background technique [0002] Process parameters affect the geometric accuracy (including roughness, etc.) and surface integrity (residual stress, etc.) of the machined parts during part processing. In order to achieve multi-objective collaborative optimization, various multi-objective optimization methods have been proposed in existing research. [0003] The existing multi-objective optimization methods are mainly divided into: [0004] 1) Transform multi-objective optimization into single-objective optimization by manually assigning weights; [0005] 2) After a series of non-dominated solutions are obtained through multi-objective optimization, the optimal objective and corresponding process parameters are determined through methods such as expert sco...

AI Technical Summary

Problems solved by technology

[0006]These methods require experienced experts to assign weights or score, which seriously affects the intelligent level of parameter decision-making

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