Prediction and optimization of gear skiving parameters and geometric deviations

• Published in: International journal of advanced manufacturing technology Vol. 121; no. 5-6; pp. 4169 - 4185
• Main Authors: Lin, Xiaochuan, Liu, Yanghe, Sun, Shouli, Jin, Ge, Hong, Rongjing
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2022; Springer Nature B.V
• Subjects: Algorithms; CAE) and Design; Computer-Aided Engineering (CAD; Deviation; Engineering; Error analysis; Industrial and Production Engineering; Mathematical models; Mechanical Engineering; Media Management; Optimization; Original Article; Prediction models; Process parameters; Tool wear; Global best analysis; Gear skiving technology; Skiving parameters optimization; IPSO-BP-PSO algorithm; Geometric deviations prediction
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-022-09639-6

Gear skiving is a high-efficiency and high-precision manufacturing process. Inappropriate skiving parameters will aggravate tool wear and reduce gear accuracy. To scientifically select the skiving parameters and improve the skiving precision, it is essential to accurately establish the prediction model between the skiving parameters and the gear geometric deviations. This paper proposes an improved PSO-BP-PSO algorithm based on the geometric deviation of actual measurement. The root mean square error calculation results show that the prediction model established by the proposed algorithm has good performance. The prediction results introduce a global best analysis, and the optimized parameters are comprehensively evaluated. The skiving parameters obtained after the optimization process are skived, and the gear geometric deviations are measured to verify the correctness of the proposed method. The experimental results show that the gear accuracy increases by 2 ~ 3 levels, and the maximum prediction error is only 11.8%. The results verify the effectiveness of the prediction algorithm and prove that the proposed method can improve skiving precision.