The Prediction of Stability in the Time-Delayed Milling Process of Spiral Bevel Gears Based on an Improved Full-Discretization Method

Spiral bevel gear drives are widely used in mechanical transmission devices due to their compact structure, smooth transmission, and cost-effectiveness. With the continuous improvement in mechanical product quality, higher and higher requirements are set for the precision, smoothness, and power dens...

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Bibliographic Details
Published in:Mathematics (Basel) 2024-07, Vol.12 (13), p.2061
Main Authors: Tian, Chong, Wang, Taiyong, Tian, Ying
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Chatter
Computational efficiency
Continuous improvement
Cost effectiveness
Cutting wear
Efficiency
full-discretization method
Machine tools
Mathematical models
Mechanical transmissions
Methods
Milling (machining)
milling chatter
Predictions
Smoothness
Spiral bevel gears
Stability
Surface finish
time-delayed
Tool wear
Vibration
Workpieces
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Summary:Spiral bevel gear drives are widely used in mechanical transmission devices due to their compact structure, smooth transmission, and cost-effectiveness. With the continuous improvement in mechanical product quality, higher and higher requirements are set for the precision, smoothness, and power density of gear transmission devices. Chatter can lead to poor workpiece surface finish on spiral bevel gears, excessive tool wear, and even damage to machine tools. Therefore, the effective prediction of milling chatter during the processing of spiral bevel gears is essential. Regenerative chatter is one of the most fundamental types of vibration in machining processes. This paper presents an improved fully discrete algorithm for predicting the stability of time-delayed cutting in the milling process of spiral bevel gears. The method is validated using single- and double-degree-of-freedom models, demonstrating its accuracy and computational efficiency. The results show that the proposed method improves computational efficiency while ensuring accuracy.
ISSN:2227-7390
2227-7390
DOI:10.3390/math12132061