AQL Calculator
Calculate your sample size and accept/reject criteria based on ISO 2859-1 (ANSI/ASQ Z1.4) standard. Used by quality professionals worldwide.
Enter Your Parameters
Enter the total number of units in your shipment
Level II is standard for most inspections
AQL Limits (Defect Tolerance)
Safety defects
Functional issues
Cosmetic issues
Common AQL Presets:
Inspection Parameters
Sample Size
32
units to inspect
Code Letter
G
ISO 2859-1
Accept / Reject Criteria
AQL: Zero
Accept: 0
Reject: 1
AQL: 2.5%
Accept: 5
Reject: 6
AQL: 4.0%
Accept: 7
Reject: 8
Summary: For a lot of 500 units at inspection level II, you need to inspect 32 samples. The inspection passes if you find 5 or fewer major defects, and fails if you find 6 or more.
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Book InspectionUnderstanding AQL Sampling
What is AQL?
AQL (Acceptable Quality Limit) is the maximum defect rate considered acceptable during sampling inspection. It's defined in ISO 2859-1 standard and helps determine how many samples to check and what defect level triggers rejection.
Defect Classifications
Critical: Safety hazards, typically 0% AQL.
Major: Affects function/appearance, usually 2.5% AQL.
Minor: Slight imperfections, often 4.0% AQL.
Inspection Levels
Level I: Reduced sampling for trusted suppliers.
Level II: Standard level for most inspections.
Level III: Tightened sampling for high-risk products.
Frequently Asked Questions
What does the sample size mean?
The sample size is the number of units that need to be randomly selected and inspected from your total order quantity. Based on defects found in this sample, the entire lot is either accepted or rejected.
What AQL should I use?
The most common standard is 0 / 2.5 / 4.0 (Critical / Major / Minor). For high-value or safety-critical products, consider stricter limits like 0 / 1.5 / 2.5. For promotional items with lower quality expectations, 0 / 4.0 / 6.5 may be acceptable.
What is the "arrow procedure"?
When the sample size is too small for a given AQL, the ISO 2859-1 standard uses arrows to indicate that you should move to a larger sample size. This ensures statistical validity of the sampling plan.
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