Research on Machining Quality Prediction Method of Aerospace Precision Parts Based on BAS-RF
In the process of traditional aviation parts processing,the challenge of fluctuating component quality has long been a prominent issue in the manufacturing industry.To address this challenge,we propose a method for predicting the quality of components in manufacturing based on the combination of the Behavioral Attraction Search(BAS)algorithm and Random Forest(RF),referred to as BAS-RF.This method aims to enhance the accuracy and stability of component quality predictions by leveraging the strengths of the heuristic optimization algorithm BAS,and the ensemble learning algorithm RF.Primarily,the RF algorithm is employed for predicting component features.By constructing multiple decision trees and aggregating their predictions through a voting mechanism,the RF algorithm effectively handles complex non-linear relationships and exhibits strong generalization capabilities.To optimize the parameters of the RF algorithm,we introduce the BAS algorithm.BAS,inspired by the foraging behavior of beetles,is a heuristic optimization algorithm that efficiently explores complex parameter spaces.In this study,BAS is utilized to adjust key parameters within the RF algorithm,aiming to achieve optimal predictions of component quality.Through the optimization facilitated by the BAS algorithm,the model demonstrates improved adaptability to diverse manufacturing scenarios,resulting in enhanced prediction accuracy and generalization capabilities.To validate the effectiveness of the proposed method,a series of experiments were conducted.The results indicate that the BAS-RF method outperforms traditional approaches in terms of prediction accuracy,particularly showcasing significant practical implications for reducing component quality fluctuations and enhancing the controllability of the manufacturing process.This research contributes to advancing the accuracy of quality predictions for aerospace precision components,underscoring its noteworthy practical sig-nificance.
parts processingrandom forestbeetle antennae searchquality predictionoptimization algorithm
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