The oblique incident phase-shifting interferometric measurement method is an effective technique for assessing gear tooth form deviation. In the measurement process, the tooth flank incident angle is a crucial factor determining the quality of interferograms. Addressing the challenge in traditional optical path design, where the selection of the tooth flank incident angle encounters a trade-off among quality features such as measurable tooth area (MTA), interference fringe density (IFD) and interferogram compression ratio (ICR), this paper proposes an optimized method for the tooth flank incident angle. Firstly, a comprehensive evaluation model is established to calculate and analyze the MTA, IFD and ICR of the helical gear. Secondly, by integrating BP neural network and Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, a tooth flank incident angle optimization method is devised to simultaneously optimize the measurement light incident angle and the rotation angle of the gear axis . Finally, simulation analyses and physical experiments are employed to demonstrate the feasibility of the proposed method, compared to before optimization, the pseudocorrelation (PSD) value has increased by an average of 34%, and the residual points have decreased by an average of 80%, confirming its capability to improve quality interferogram and measurement accuracy.

中文翻译：

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倾斜激光干涉测量中基于BP-MOPSO的多冲突目标入射角优化

斜入射相移干涉测量方法是评估齿轮齿形偏差的有效技术。在测量过程中，齿面入射角是决定干涉图质量的关键因素。传统光路设计中，齿面入射角的选择需要在可测量齿面积 (MTA)、干涉条纹密度 (IFD) 和干涉图压缩比 (ICR) 等质量特征之间进行权衡，解决了这一挑战。本文提出了一种齿面入射角的优化方法。首先建立综合评价模型，对斜齿轮的MTA、IFD和ICR进行计算分析。其次，结合BP神经网络和多目标粒子群优化（MOPSO）算法，设计了齿面入射角优化方法，同时优化测量光入射角和齿轮轴的旋转角。最后通过仿真分析和物理实验验证了该方法的可行性，与优化前相比，伪相关（PSD）值平均提高了34%，残差点平均减少了80% ，确认其提高干涉图质量和测量精度的能力。