锻压技术

花键冷滚打表面粗糙度灰色预测模型

英文标题：Gray prediction model of surface roughness for spline by cold rollbeating

分类号：TH161

出版年,卷(期):页码：2018,43(6):60-66

摘要：

为了改善花键冷滚打表面粗糙度，提高花键冷滚打表面质量。根据花键冷滚打成形原理，以花键冷滚打表面粗糙度作为主要影响因素，滚打轮转速和工件进给量作为变量，进行花键冷滚打试验，利用花键冷滚打试验结果构建花键冷滚打表面粗糙灰色预测模型，对比分析表面粗糙度试验值与预测值，运用后验差比值和小误差概率验证构建的花键冷滚打表面粗糙度预测模型。研究表明：计算得到后验差比值为0.367，小误差概率大于0.95。将计算得到的后验差比值和小误差概率与灰色预测模型精度表进行对照，验证了所构建预测模型的正确性与可行性。

Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Luoyang 471003, China)
Abstract: In order to improve the spline surface roughness and surface quality obtained by cold rollbeating, according to the spline cold rollbeating forming principle, taking surface roughness of spline cold rollbeating as main influence factor and the rolling wheel speed and workpiece feed rate as variables, the spline cold rollbeating test was conducted. Then, the gray prediction model of surface roughness for spline cold rollbeating was constructed by applying the test results, and the test value and predicted value of surface roughness were comparatively analyzed. Furthermore, the prediction model was constructed and verified by the posterior difference ratio and the small error probability. Research shows that the calculated posterior difference ratio is 0.367, and the small error probability is greater than 0.95. The calculated posterior difference ratio and small error probability are compared with the gray prediction model accuracy table, and the correctness and feasibility of the constructed prediction model are verified.

基金项目：

国家自然科学基金资助项目（51475146，51475366）

崔凤奎（1957-），男，博士，教授，博士生导师；Email：cuifk2008@163.com；通讯作者：刘飞（1989-），男，硕士研究生；Email：liufei07104517@163.com

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