High accuracy roll forming springback prediction model of SVR based on SA-PSO optimization

He, Jingsheng; Cu, Shiyi; Xia, Hui; Sun, Yong; Xiao, Wenchao; Ren, Yinwang

doi:10.1007/s10845-023-02222-0

Cited by 7 publications

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, the setting of WEDM process parameters mostly relies on the operator's experience, which is unable to adapt to the processing of variable working conditions and affects the processing quality of the workpiece. Prediction problems have been tackled using a range of algorithms in recent years, including neural networks, random forests, support vector machines (SVMs), and the least squares support vector machine (LSSVM) [18][19][20][21]. Neural networks, despite their complex structure, have poor generalization ability.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tooth Profile Deviation for WEDM Rigid Gears Based on ISSA-LSSVM

Wang,

Wang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

This study aimed to develop and validate an improved sparrow search algorithm (ISSA)-optimized Least Squares Support Vector Machine (LSSVM) model for accurately predicting the tooth profile deviation of rigid gears produced by wire electrical discharge machining (WEDM). The ISSA was obtained by optimizing the sparrow search algorithm (SSA) using Tent chaotic mapping, adaptive adjustment strategy, dynamic inertia weights, and grey wolf hierarchy strategy. The effectiveness of the ISSA was verified using four different classes of benchmark test functions. Four main process parameters (peak current, pulse width, pulse interval, and tracking) were taken as inputs and the tooth profile deviations of rigid gears were considered as outputs to develop an ISSA-LSSVM-based profile deviation prediction model. The prediction performance of the ISSA-LSSVM model was evaluated by comparing it with the LSSVM model optimized by three standard algorithms. The prediction results of the ISSA-LSSVM model were R2 = 0.9828, RMSE = 0.0029, and MAPE = 0.0156. The results showed that the established model exhibits high prediction accuracy and can provide reliable theoretical guidance for predicting the tooth profile deviation of rigid gears.

show abstract

Section: Introductionmentioning

confidence: 99%