M2 STATISTICAL QUALITY CONTROL Flashcards by Aho Tei

Q

identified portion of a batch having uniform character and quality within specified limits

A

Lot/Batch

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Q

representative/portion

A

Sample

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Q

Look at all 5,000 shells

A

100% inspection

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Q

Don’t look at any, put the whole shipment into stock

A

0% inspection

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Q

Look at some of them, and if enough of those are good, keep the lot

A

Acceptance sampling

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Q

status

A

Lot disposition

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Q

decision

A

Lot sentencing

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Q

Testing is destructive
Cost of 100% inspection is high
100% inspection is not feasible
(require too much time)
If vendor has excellent quality history
8
Statistical Quality Control

A

Why Acceptance Sampling and Not 100% Inspection

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Q

Less expensive
Reduced damage
Reduces the amount of inspection error

A

Advantages of Sampling

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Q

Risk of
– accepting “bad” lots
Consumer’s Risk
– rejecting “good” lots
Producer’s Risk
Less information generated
Requires planning and documentation

A

Disadvantages of Sampling

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Q

accepting “bad” lots

A

Consumer’s Risk

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Q

rejecting “good” lots

A

Producer’s Risk

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Q

Indicated the number of units of product from each lot or batch which are to be inspected (sample size or series of sample sizes) and the criteria for determining the acceptability of the lot or batch (acceptance and rejection numbers)

A

Sampling Plan

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Q

The units selected for inspection should be chosen at “random.”

A

Random Sampling

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Q

unbiased

A

Random Sampling

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Q

When we took the capsule shell sample, we didn’t put them back into the lot during sampling, i.e., we didn’t replace them.

A

Sampling with/without Replacement

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Q

acceptance sampling

A

we sample without replacement

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Q

each piece in the lot has equal probability of being in the sample

A

simple random sample

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Q

he lot is divided into ‘H’ groups, called “strata.”

Each item in the lot is in one and only one stratum

A

stratified sample

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Q

Sampling plans are typically set up with reference to–

A

Acceptable Quality Level (AQL)

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Q

is the base line requirement for the quality of the producer’s product

A

Acceptable Quality Level (AQL)

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Q

– the poorest level of quality (percent nonconforming) that the process can tolerate

A

Acceptable Quality Level (AQL)

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Q

Scenario:
– N = 100 products
– AQL = 10% (max. 10 defectives allowed)
– If no. of defectives < 10 → Accept
– If no. of defectives > 10 → Reject

A

Acceptable Quality Level (AQL)

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Q

Scenario:
– N = 100 products
– AQL = 5% (max. 5 defectives allowed)
– If no. of defectives < 5 → Accept
– If no. of defectives > 5 → Reject

A

Acceptable Quality Level (AQL)

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Which one has higher standard of quality in the production process? Higher or lower AQL??

Lower

* Used to measure acceptable levels of quality of the products inspected * Widely used to decide whether to accept a production lot without checking every single item.

MIL-STD-105E

– The most recently published version is MIL-STD-105E * Notice 1 cancelled the standard and refers DoD users to ANSI/ASQC Z1.4-1993

MIL-STD-105

AQL and defect classification Defects detected during inspections are generally classified in 3 categories

- Critical defect - Major defect - Minor defect

An undesirable characteristics of a product

Defects

is a unit of a product which contains one or more defects

DEFECTIVE

Classification of Defects * According to Seriousness or Gravity

- Critical defect - Major defect - Minor defect

can endanger life or property, may render the product non-functional

Critical Defect

renders the product useless

Major Defect

slight variations

Minor Defect

Classification of Defects * According to Measurability

–Variable Defect –Attribute Defect

single, measurable quality characteristic

Variable Defect

* Not represented numerically * Defective or non-defective * Conforming or Non-conforming

Attribute Defect

Classification of Defects * According to Nature

–Ocular Defect - visible – Internal Defect – not seen but present –Performance Defect - function

Variations – Cannot be identified and unavoidable – Common and inherent on the process – Cause slight differences in process variables like diameter, weight, service time, temperature, etc.

Random

Variations – Assignable – Causes can be identified and eliminated – Typical causes are poor employee training, worn tool, machine needing repair, etc.

Non-random

(Assessing the Quality of Results)

Statistics

Sources of Variations – Variation between supplies of same substance – Between batches from same supplier – Within a batch

Materials

Sources of Variations – Variation of equipment for the same process – Differences adjustment of equipment – Aging and improper care

Machines

Sources of Variations – Inexact procedures – Inadequate processes – Negligence by chance

Methods

Sources of Variations – Improper working conditions – Inadequate training and understanding – Dishonesty, fatigue and carelessness

Men

* It is impossible to perform analysis without any error or uncertainties. * The TRUE value of a measurement is never known exactly. * Replicates are samples of the same size that are analyzed in exactly the same way.

Statistics

The TRUE value of a measurement is

never known exactly

are samples of the same size that are analyzed in exactly the same way.

Replicates

It is impossible to perform analysis without any

error or uncertainties

(Average)

Mean

central or “best” value

Mean and median

(middle result when arrange in order) – Odd numbered samples → the middle – Even numbered samples → average of the middle pair

Median

– Closeness of measurements with each other – Reproducibility of measurements

PRECISION

– STANDARD DEVIATION (s or SD) * The higher the s, the less precise the measurements are. – VARIANCE (s2) – COEFFICIENT OF VARIANCE (CV) – RANGE (SPREAD)

PRECISION

– Closeness of measurement to its TRUE (accepted) value

ACCURACY

* Composed of: – Central Line – Upper Line for the UCL – Lower Line for the LCL * Can conclude if variation is – CONSISTENT (in-control) – UNPREDICTABLE (out of control)

Control Charts

– ERROR * ABSOLUTE ERROR * RELATIVE ERROR

ACCURACY

* means of visualizing variations that occur in the central tendency and dispersion of a set of observations * used to study how a process changes over time

CONTROL CHARTS

TYPES OF CONTROL CHARTS

* ATTRIBUTES – p-chart – c- chart * VARIABLES – X-chart – R-chart

– X-chart – R-chart

VARIABLES

* Determine the mean, UCL and LCL. * Estimate an arbitrary range (with same interval). * Draw the lines. * Plot the data. – Number of sample (X-axis) – Sample Weight (Y-axis) * Interpret the results. – Out-of-control signals

Construct a Control Chart

– p-chart – c- chart

ATTRIBUTES

establishes a high degree of certainty

Validation

Validation Parameters

* Accuracy * Precision * Selectivity * Linearity * Range * Sensitivity * LOD * LOQ * Ruggedness * Robustness

– Measure of exactness

Accuracy

Measure of degree of "reproducibility/repeatability" under [normal] condition

Precision

– Ability to measure the analyte of interest

Selectivity/Specificity

Ability to produce results that are directly proportional to concentration of analyte

Linearity

Lowest concentration of analyte that can be detected/measured

Limit of Detection (LOD) / Limit of Quantification (LOQ)

Highest and lowest level of analyte that can be measured

Range

Degree of reproducibility under variety of test conditions

Ruggedness

– Ability to remain unaffected by small variations

Robustness

– Ability to record small variations (changes)

Sensitivity

Meaning of MIL-STD

Military Standard