阿狸 发表于 2026-8-21 14:40:44

讨论:为什么会出现PPK大于CPK的情况

本帖最后由 阿狸 于 2026-8-21 14:42 编辑

最近遇到一个情况,PPK大于了CPK,如图所示
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

查了下度娘,解释如下:
这可能是由于以下几个原因:
1. 数据采样不足:如果采样数据不足,可能导致PPK的计算结果偏高。PPK是基于样本数据计算的,如果样本数量不足,可能无法准确反映整个过程的能力。
2. 数据分布不均匀:如果数据的分布不均匀,即存在明显的偏离正态分布的情况,可能导致PPK的计算结果偏高。CPK是基于正态分布假设计算的,如果数据不符合正态分布,CPK可能会更准确地反映过程的能力。
3. 过程变异性:如果过程的变异性较大,可能导致PPK的计算结果偏高。PPK考虑了过程的总体能力,而CPK考虑了过程的中心能力。如果过程的变异性较大,PPK可能会受到影响,而CPK可能更能反映过程的实际能力。需要注意的是,PPK和CPK都是用于衡量过程的能力,但是它们的计算方法和假设不同。


是否还有其他原因?感兴趣的可以留言讨论

伊麽拉萨 发表于 2026-8-22 08:51:21

1. 数学上的直接原因
CPK 的分母是组内标准差(σ^within=0.1017σ^within​=0.1017)
PPK 的分母是整体标准差(σ^overall=0.0983σ^overall​=0.0983)
因为CPK和PPK 的分子(均值到规格限的距离)是相同的,所以当组内标准差(0.1017)大于整体标准差(0.0983)时,分母越大,计算出的指数越小。因此 CPK(0.63)< PPK(0.65)。
2. 为什么组内标准差会大于整体标准差?
这与数据的抽样分组方式和时间序列形态有关:
子组内差异过大(“锯齿状”波动):Minitab 默认的组内标准差通常基于合并极差或移动极差计算。如果您的100个数据按生产顺序排列,且相邻两个零件之间的尺寸忽大忽小(高频振荡),那么移动极差的均值就会很大,导致组内标准差被拉高。
子组均值过于集中(“抱团”效应):与此同时,如果您每隔5个或10个零件分成一组,这些子组的平均值(子组均值)非常接近总均值(49.9908),没有明显的长期漂移或偏移。计算整体标准差时,是用每个单值离总均值的距离算平方和,这种高频振荡在整体离差平方和中会被相互抵消(忽大忽小),导致整体标准差反而更小。
通俗比喻:这就像一个人走路,整体标准差看的是他 1 小时内离出发点的平均距离(可能没走多远);而组内标准差看的是他每一步的步幅大小(步子迈得很大,左右摇晃)。步幅(组内)大于净位移(整体),就会出现这种情况。
3. 这对您的改善方向意味着什么?
这个反常规的数据特征,精准地锁定了故障模式,给您的改善指明了截然不同的方向:
不是“均值漂移”的问题:无需浪费时间调整对刀或补偿值(因为子组均值很稳)。
是“单件重复精度”或“高频振动”的问题:
主轴或刀塔间隙过大:切削时刀具受力不均,导致连续加工的零件尺寸呈现“之”字形交替变化。
材料内应力释放:上一刀和下一刀的让刀量不一致。
测量误差干扰:如果测量时零件未擦干净或量具重复性差,会给数据叠加随机“噪声”,这些噪声在相邻点间跳跃,会剧烈拉大移动极差(组内σ)。
4. 最终建议
请立即查看原始数据的时间序列图(趋势图),您大概率会看到一条像“锯齿”一样剧烈震荡的折线,而不是缓慢漂移的曲线。
对策:不要尝试修改 CNC 补偿值。应重点检查主轴轴承预紧力、液压夹具夹持刚性,以及刀具的径向跳动(偏摆)。将高频振动消除后,组内标准差会迅速下降,届时 CPK 不仅会反超 PPK,还会大幅提升至合格水平。

飞翔的鸟 发表于 2026-8-22 08:20:03

PPK不就是>CPK嘛

zhan_paolo 发表于 2026-8-22 08:24:38

谢谢分享

y7a1n3 发表于 2026-8-22 08:40:22

:)

hutian02 发表于 2026-8-22 17:20:56

:handshake:handshake

samtang 发表于 2026-8-23 06:20:50

:)

flyerchang 发表于 2026-8-24 20:40:39


谢谢分享
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