Key Takeaways & Executive Findings
- •• A multi-objective optimization model links grinding process parameters—cutting speed, feed rate, and cutting depth—to gear tooth surface, profile, and lead deviations. • Response surface methodology (RSM) enables efficient experimental design, yielding optimal process parameters for worm wheel gear grinding. • GRA-PCA, PSO, and GA-PSO all improve gear machining precision, but GA-PSO achieves superior results. • The findings provide a systematic route to meet ISO 1328-1:2013 grade 4–5 precision requirements for new energy vehicle gears.
Abstract
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads, which puts forward higher precision essentials for gear manufacturing. However, machining process parameters can cause changes in cutting force/heat, resulting in affecting gear machining precision. Therefore, this paper studies the effect of different process parameters on gear machining precision. A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations, tooth profile deviations, and tooth lead deviations through the cutting speed, feed rate, and cutting depth of the worm wheel gear grinding machine. The response surface method (RSM) is used for experimental design, and the corresponding experimental results and optimal process parameters are obtained. Subsequently, gray relational analysis-principal component analysis (GRA-PCA), particle swarm optimization (PSO), and genetic algorithm-particle swarm optimization (GA-PSO) methods are used to analyze the experimental results and obtain different optimal process parameters. The results show that optimal process parameters obtained by the GRA-PCA, PSO, and GA-PSO methods improve the gear machining precision. Moreover, the gear machining precision obtained by GA-PSO is superior to other methods.
1. Introduction
Gears play an integral role in many industrial segments, as they supply one of the basic mechanical components for transmitting motion and/or power to ensure the proper functioning of machines, instruments, and equipment [1]. Especially in the field of new energy vehicles, gears need to withstand higher rotational speeds and greater loads, which require gear machining precision to reach 4–5 levels (ISO 1328-1: 2013). Insufficient gear machining precision will produce slight vibrations during meshing, which affect the vehicle’s noise, stability, and service life [2, 3]. Therefore, ensuring the gear machining precision that includes tooth surface deviations, tooth profile deviations, tooth lead deviations, and tooth pitch deviations is important [4, 5].
Gear grinding is usually the last step in the finishing gear manufacturing process, which can remove defects such as uneven materials and pores on the gear surface, thereby meeting the requirements for gear machining precision in most fields [8]. However, the gear grinding mechanism is too complex to ensure gear machining precision [9]. Therefore, it is urgent to study methods for improving gear machining precision [10]. Gear grinding machine is gear machine tools that use the grinding wheel as a cutting tool to process workpiece gears to realize the grinding process. Since the machining process is affected by geometric, thermal, and force errors that can cause a decrease in gear machining precision [11], many researchers have modeled errors and compensated for gear machine tools to achieve an increase in gear machining precision. For instance, CHEN et al [12] developed the geometric error model and a NUM error compensation system for gear grinding machines, which can reduce tooth profile deviations, tooth lead deviations, and tooth pitch deviations. XIA et al [13] proposed a geometric error modeling and compensation for gear grinding machines based on single-axis kinematic measurements and the actual inverse kinematic model, and experiments showed that the tooth profile deviation of the machined gear was reduced. TANG et al [14] proposed an innovative geometric error modeling and compensation for gear grinding machine with the non-rotary cutter, and experiments verified through theoretical calculations and actual machining to reduce the tooth surface deviation of machined gears. LIU et al [15] introduced the effects of geometric and thermal errors on gear grinding machines, proposed an analytical model for rolling guide rails to calculate the geometric errors of the X-axis and Z-axis, and established thermal error models of the spindle and C-axis.
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YOU Tong-fei, HAN Jiang, TIAN Xiao-qing, TANG Jian-ping, LU Yi-guo, LI Guang-hui, XIA Lian (2025). Multi-objective optimization of grinding process parameters for improving gear machining precision. Journal of Central South University. https://doi.org/10.1007/s11771-025-5877-7
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Frequently Asked Questions
What is the main objective of this study?
The study aims to improve gear machining precision for new energy vehicle gears by establishing a multi-objective optimization model that relates grinding process parameters—cutting speed, feed rate, and cutting depth—to tooth surface, profile, and lead deviations.
Which optimization methods are compared in this research?
The research compares gray relational analysis-principal component analysis (GRA-PCA), particle swarm optimization (PSO), and genetic algorithm-particle swarm optimization (GA-PSO) methods for obtaining optimal grinding process parameters.
How does GA-PSO perform compared to other methods?
The results demonstrate that GA-PSO provides superior gear machining precision compared to GRA-PCA and PSO, making it the most effective optimization method among those tested.
Why is gear machining precision critical for new energy vehicles?
Gears in new energy vehicles must withstand higher rotational speeds and greater loads, requiring precision levels of 4–5 according to ISO 1328-1:2013 to prevent vibrations that affect noise, stability, and service life.
What process parameters are optimized in this study?
The optimized process parameters are cutting speed, feed rate, and cutting depth of the worm wheel gear grinding machine, which directly influence cutting force/heat and consequently gear machining precision.
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