Optimization decision method for spur gear cold extrusion process parameters driven by small sample data

Aiming at the problem of insufficient effective tooth width in the debugging and production of a spur gear, a small-sample data-driven “experimental design-model construction-algorithm optimization-decision verification” multi-objective optimization and decision-making method for spur gear cold extr...

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Vydáno v:International journal of advanced manufacturing technology Ročník 140; číslo 11-12; s. 6657 - 6675
Hlavní autoři: Qiang, Liang, Binyuan, Xu, Yonghang, Xu, Bin, Ma, Yanyan, Jia
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.10.2025
Springer Nature B.V
Témata:
Accuracy
Algorithms
CAE) and Design
Cold
Cold extrusion
Computer simulation
Computer-Aided Engineering (CAD
Debugging
Decision making
Design of experiments
Design optimization
Engineering
Extrusion dies
Gear teeth
Industrial and Production Engineering
Manufacturing
Mechanical Engineering
Media Management
Metal forming
Multiple objective analysis
Neural networks
Optimization
Optimization models
Original Article
Process parameters
Quality control
Service life
Simulation
Spur gears
Verification
Small sample drive
Spur gear
Improved multi-objective grey wolf optimization algorithm
ISSN:0268-3768, 1433-3015
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Shrnutí:Aiming at the problem of insufficient effective tooth width in the debugging and production of a spur gear, a small-sample data-driven “experimental design-model construction-algorithm optimization-decision verification” multi-objective optimization and decision-making method for spur gear cold extrusion forming process parameters was proposed. Small-sample data-driven algorithms were introduced to construct a multi-objective optimization model for the relationship between process parameters and the bottom protrusion length, effective tooth width, and maximum effective stress of the tooth profile concave die of the extrusion gear. The improved multi-objective grey wolf algorithm was applied to optimize the multi-objective optimization model and obtain a non-dominated solution set. The subjective–objective comprehensive entropy weight optimal solution distance method was used to evaluate and determine the optimal combination of process parameters. The dual verification results of numerical simulation and physical process experiments show that the simulation results were basically consistent with the process experiments, and the effective tooth width and tool service life met the design requirements. The optimization and decision-making method based on a small-sample data-driven approach proposed in this study breaks through the technical limitations of insufficient accuracy of multi-objective models under small samples and provides an efficient solution path for similar complex process optimization problems.
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ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-025-16632-2