求解：测量系统分析的结果里，该看哪个数据？
本帖最后由 WinterKing 于 2025-8-14 08:40 编辑这个用Excel输出的测量分析结果，每个数据代表啥意思呢？percent of tolerance该如何理解呢？谢谢！
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
以附件解釋 TV是指过程总变差， tolerance是指产品规格公差，统计的特性（误差）要一直保持在期望的范围内，并且足以满足测量的目的（过程控制或产品控制）
{:1_180:} badboy0133 发表于 2025-8-14 10:05
以附件解釋
专业 臭要饭的 发表于 2025-8-14 12:03
比较形象 badboy0133 发表于 2025-8-14 10:05
以附件解釋
专业{:1_89:}{:1_89:} 臭要饭的 发表于 2025-8-14 12:03
受教了！ {:1_180:}
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