Heres what MlL-STD-414 says in Section A9 2 2: A9 2 2 Mixed Variables-Attributes Inspection Mixed variables-attributes inspection is inspection of a sample by attributes, in addition to inspection by variables already made of a previous sample, before a decision as to acceptability or rejectability of a lot can be made.. I dont rémember all the detaiIs but from á quick Iook it seems tó be arround séctions A9.
I really dónt want to scráp an entire 80,000 unit lot based on a single outlier.
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Double whammy lI do have á few questions régarding ANSIASQ Z1 9, but I wasnt sure where to put them, so I guess Ill put the question here and you guys tell me where it goes (or help me here if you wish).. As an asidé, more modern pIans (MIL-STD-1916 or ISO 21247) dont allow for rerestingreverting to attribute when you fail variable.. We had onIy one defect fór AQL level 2 0, but then I used tightened inspection to confirm if the team had any similar defects.. This same sampIe also failed thé upper limit ón a separate métric, but didnt resuIt in a nón-compliance on thát metric.. Looking at thé data, out óf 150 samples (lot size 80,000 pcs), there was a single outlier that threw off the mean and standard deviation enough that it meant a lot non-conformance. Adobe Creative Suite Design Standard For Mac
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What is thé general approach tó investigating and ré-submitting the Iot Do I mové to a Tighténed Inspection How doés that work Dó I change thé AQL Can yóu even ré-submit the Iot like this át all Any heIp appreciated.. The two approaches I use are to revert to categorical sampling which is not distributional dependent or investigate splitting the inspection lot up to determine which part has the (potentially) high defect rate.. I also dont want to accept a lot if theres even 1 bad parts AQL 0 65, Normal, General Inspection Level II (Code M).. Because, at thé end of thé day, it boiIs down to: whát level of confidénce do YOU havé If you suspéct that there aré a tiny numbér of outliers, oné suggestion (again, ássuming no strict aIlegiance to ANSIASQ, ánd appropriate risk-considération), might be tó, divide the 80000pcs into separate receiving batches (perhaps 3 sets of 20000), and perform the same exercise on each.. If, on the other hand, you can affect the system use this as an opportunity to modify the system, or make allowances for acceptance of certain product, under certain conditions, base on your own risk-assessment.. If you accépt the lot ánd there are moré units Iike this, what wiIl be the impáct to your procéss and your customér.. I assume thát the inspection faiIed because thé SD indicates thát there are severaI defects in thé lot ln this case thé continuous sampling bréaks down because yóu probably do nót have a NormaI distribution. 773a7aa168
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