A gear form-grinding optimization method is proposed to obtain an optimal profile of a form-grinding wheel by solving a transcendental equation of the contact line between a form-grinding wheel and a helical gear workpiece. Since the equation of the contact line is transcendental, the relationship between the installation angle of the form-grinding wheel and the shape of the contact line cannot be represented by explicit functions, which makes it difficult to obtain the optimal profile of the form-grinding wheel. An optimization method of the contact line between the form-grinding wheel and the gear is proposed using three evaluation parameters that are the overrun, the shift, and the offset. Some gear form-grinding performances, such as machining stroke, tooth deviation, and grinding chatter, can be quantitatively described using those evaluation parameters. Further, a method for optimizing evaluation parameters is proposed, which uses a particle swarm optimization-support vector machine (PSO-SVM) model with the advantage of small-sample robustness to solve evaluation parameters. The PSO-SVM model is trained under the condition of different installation angles of the form-grinding wheel as the input and the evaluation function as the output. The R-squared value of the overrun is 0.986 that vilidate the high accuracy of the PSO-SVM model. Gear form-grinding test results show the proposed method can effectively improve grinding accuracy.

中文翻译：

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基于PSO-SVM模型的斜齿轮成型磨轮优化方法

提出了一种齿轮成形磨削优化方法，通过求解成形磨削轮与斜齿轮工件之间的接触线的超越方程来获得成形磨削轮的最佳齿廓。由于接触线方程是超越方程，成形磨削轮的安装角度与​​接触线形状之间的关系不能用显式函数表示，这使得很难获得成形磨削的最佳轮廓车轮。提出了一种利用超程、偏移和偏移三个评价参数的成形磨削轮与齿轮接触线的优化方法。一些齿轮成形磨削性能，如加工行程、齿形偏差和磨削颤振，可以使用这些评价参数进行定量描述。进一步提出了一种优化评价参数的方法，利用具有小样本鲁棒性优势的粒子群优化支持向量机（PSO-SVM）模型来求解评价参数。以成形砂轮不同安装角度为输入，以评价函数为输出，训练PSO-SVM模型。溢出的 R 平方值为 0.986，验证了 PSO-SVM 模型的高精度。齿轮成形磨削试验结果表明，该方法能有效提高磨削精度。