Block 4 - lecture 1 Flashcards

1
Q

How do you know when a process is in control?

A
  • special causes have been eliminated so that all subgroups are within control limits
  • there’s a natural pattern of variation
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2
Q

is uniformity improvement possible when a process is in control?

A

no, the process would have to be improved

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3
Q

what percentage of subgroups are expected between each standard deviation?

A
0-1 = 34%
1-2 = 13.5%
2-3 = 2.5%
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4
Q

Where are control limits typically established?

A

3 standard deviations from the central line

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5
Q

What are the 2 types of error?

A

Type I error - good but outside limits (ie. over 3sd 0.27% of the time)

Type II error - assignable cause, but within limits

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6
Q

why are control limits often set to 3 standard deviations?

A

to balance cost of type I and II errors

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7
Q

Positives of a process being in control?

A
  • more uniform so less rejections (scrap/rework)
  • less samples needed, reducing cost of inspection
  • process capability is 6sigma (stable and repeatable)
  • trouble can be anticipated
  • percentage in each range easy to predict
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8
Q

decisions to make about processes?

A
  • spec requirements
  • scrap vs rework
  • loosening specifications and using selective matching
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9
Q

benefits from producers sharing Xbar and R charts with customers?

A

customer requires less checks, as they have more trust

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10
Q

two causes of variation?

A
  • common causes eg. chance causes (stable)

- special causes eg. assignable causes (change)

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11
Q

what does ‘out of control’ show?

A

a change in the process due to an assignable cause

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12
Q

how can you view a subgroup that falls outside the control limits?

A

it’s as if it comes from a different population to the control limits, due to special causes

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13
Q

examples of patterns in control charts? (using bands of 1 standard deviation)

A

> 2 in 3 consecutive points outside 2sd

> 4 in 5 consecutive points outside 1sd

> 6 points increasing or decreasing

> 7 consecutive points on one side of the central line

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14
Q

examples of patterns in control charts? (using bands of 1.5 standard deviation)

A

> 2 consecutive points outside 1.5sd

> 6 points increasing or decreasing

> 7 consecutive points on one side of the central line

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15
Q

benefits of using 1.5 sigma bands for patterns vs 1 sigma bands

A

quicker and easier for operators

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16
Q

examples of patterns in control charts? (using the shape of plotted points)

A
  • change/jump in level
  • steady change in level
  • recurring cycles
  • two populations
  • mistakes
17
Q

possible causes of a jump in level in Xbar charts?

A
  • change of settings (intentional or not)
  • wrong setup
  • process skipped
  • new operator
  • different material properties
  • machine failure
18
Q

possible causes of a jump in level in R charts?

A
  • inexperienced operator
  • sudden gear play
  • material variation
19
Q

possible causes of a steady change in level in Xbar charts?

A
  • tool wear
  • deteriorating equipment
  • gradual change of environment
  • viscosity breakdown in chemical processes
20
Q

possible causes of a steady change in level in R charts?

A
  • improving skill
  • fatigue, boredom etc.
  • material becoming more uniform
21
Q

possible causes of a recurring cycle in Xbar charts?

A
  • environment (seasonal or daily)
  • daily/weekly event
  • operator rotation
22
Q

possible causes of a recurring cycle in R charts?

A
  • operator fatigue after breaks

- lubrication cycles

23
Q

danger of recurring cycles?

A

The inspection frequency might not pick up on a cycle if it only records one part of it (eg. same time every day)

24
Q

possible causes of two populations in Xbar charts?

A
  • large material differences
  • multiple machines
  • multiple methods
25
Q

possible causes of two populations in R charts?

A
  • multiple material suppliers

- different operators

26
Q

possible causes of mistakes in Xbar charts?

A
  • measuring calibration
  • calculation errors
  • equipment usage
  • samples from different populations (different machines etc.)