As for critical bolts - like on bridges (looked this up) state governments require sampling and testing of every lot (purchase order) by an independent QA house.
Actually, there's another statistical explanation. The bolt's strength is distributed like a normal (or probably like a Chi). So, the statistical sample is not big enough to catch the outliers.
Quote from: johnx98374 on 08/06/2015 11:29 pm As for critical bolts - like on bridges (looked this up) state governments require sampling and testing of every lot (purchase order) by an independent QA house. Like any other statistical test, this is making critical assumptions on the distribution of errors. If your bolts are in general too weak, this is the right test. But if 99 of every 100 bolts are perfect, and one is completely and utterly defective, then some lots will pass with a perfect score and yet you'll be building bridges with at least some defective bolts.I suspect this is exactly what happened here. The supplier's process, for whatever reason, produced struts where the vast majority are fine, but the ones that were defective were very defective. (One could imagine lots of reasons for this - a few counterfeit bolts mixed with real bolts, a heat-treating machine that intermittently fails, etc.). Any QA process that uses sampling can miss this. Unfortunately, the only way to *know* the distribution is to test every bolt. And then you don't need to know the distribution.
40:46 Elon Musk: … we tested a whole bunch of struts, none of them failed at a level, like the lowest we saw anyone fail was at six thousand pounds of force…… So we thought well, that couldn't be the issue…... Then we got just a huge number of these struts with this particular bolt and after testing some enormous number we were able to find one that failed below the two thousand pound level. So it was sort of a statistical thing
Quote from: baldusi on 08/07/2015 07:32 pmActually, there's another statistical explanation. The bolt's strength is distributed like a normal (or probably like a Chi). So, the statistical sample is not big enough to catch the outliers.SpaceX certainly has tested enough bolts by this point to know whether the strength distribution is normal/Chi. From the tiny bit of data we have ("some" failed at 6000lb, 2 failed at 2000lb) I think it's unlikely it's a normal distribution: you'd expect to see more failures between 2000-6000lb before you saw another 2000lb failure. You could compute an exact probability given some reasonable assumptions.
Why are we have this same line of questions about the struts and bolts? Sorry but its getting old
I think this is interesting:http://naca.central.cranfield.ac.uk/reports/arc/rm/3654.pdfTable 2, steel bolts in tension on a nut.Coefficient of Variation (standard deviation / mean) is listed at 2%-7%. That's pretty good.If that represents the real variability, the likelihood of a part being 60-80% below the mean is vanishingly small. Either the CoV is really much too large, or they have a process that can produce a bad batch or a bad single parts.
'Congress asks question that makes them appear silly.' Should be the headline. First there's an engine explosion on the stand, then a propulsive failure on the vehicle that involved an engine failure indicating a failure to fix the root cause applied on top of the inherent doubt about using a 30 year old engine. Then you have a first time LOM with a vehicle with a much more solid track record ... Gee I wonder why they are being treated somewhat differently.Further, the finger pointing war between gencorp and orbital(atk) certainly doesn't suggest the root cause can be settled without another opinion. Someone more familiar with the details feel free to correct any misperceptions I have.