Unequal stick-out before we've even added the robot
JoBaker0505 · 2026年9月4日 13:27 UTC
主帖中文翻译暂不可用,以下显示英文原文;回复保留原文。 查看英文原文
14 条回复
What exactly was measured, and are the readings linked to individual blanks? I couldn't attribute the variation to seating from stick-out alone. Differences in blank length or the measurement reference could produce a similar-looking list.
14分Depth readings from a fixed face to each blank end. No individual blank lengths recorded.
6分Then the old list is missing one of the quantities needed to interpret it. Are the same blanks available with identities preserved, or would a new, properly defined measurement study be needed?
17分Who owns the loading and inspection procedure for that study, so the person collecting numbers has a consistent method and knows what to do when a sample gives an unexpected reading?
9分The machining and quality owners should agree it together. And keep this as a measurement-planning question; no one here has enough detail to prescribe a new chuck-loading procedure from these posts.
8分Check blank end condition in the study plan too, where relevant to the datum. Length alone may not explain how a particular end contacts the intended locating surface.
8分Yes, relevant end features belong with the sample description. That doesn't establish an end-condition fault here, but it avoids designing a study that measures only one possible source of variation.
10分The samples lost their row identities. We're planning fresh paired measurements, with quality defining the references.
11分Give each sample a label that stays with its measurements. A numbered tray position only works until someone moves the blanks to another tray.
7分Agreed. The old readings stay as background. They don't establish a robot problem or a seating cause.
23分Add that limitation beside the old sheet itself, so another person does not lift those numbers into the automation proposal as proven repeatability data.
23分Before we collect the new set: unreadable sample label, discard the row or keep it marked unusable?
0分Keep the gap visible. Ask quality how it should be excluded from the paired comparison.
0分And count the failed records separately, otherwise the method can look beautifully manageable after all the inconvenient rows have vanished.
0分