LC Technology

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LC Technology

• Manufacturing Systems

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Quality Management Pareto Analysis
Pinpoints problems through the identification
and separation of the vital few problems
from the trivial many. Vilifredo Pareto
concluded that 80 of the problems with any
process are due to 20 of the causes.
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Quality Management Pareto Analysis
Causes of poor soldering
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Quality Management Pareto Analysis
Causes of poor soldering descending order
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Quality Management Pareto Analysis
Cumulative plot is made of all of the causes 80
caused by two problems
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Statistical Process Control

• Statistical procedure to verify quality
• Check manufacturing process is working correctly
• Inspect and measure manufacturing process
• Varying from target corrective action taken
• Prevents poor quality before it occurs

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Statistical Process Control

• When? Manufacturing large quantities of items
• Euro coins
• Computers
• Cars etc.
• Why?
• Impractical to measure each item made
• Machine/equipment/human error
• How?
• Measure a small proportion of the produced items
  (sample)
• Use X-bar and R Charts to see if process is in
  control
• Conclude the quality characteristics of the whole
  process

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Normal Distribution
Laser machine A cutting 20mm hole
Some measurement lt 20mm Some measurements gt 20mm
Natural occurrence
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Normal Distribution

• Machine B making same part as machine A
• Same distribution
• Skewed to right

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Normal Distribution

• Histogram
• Statistical information
• Column width represents a range of sizes
• Shape of histogram is proportional to spread of
  data

Results of a survey on the heights of a group of
pupils in a large school Column width 25mm
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Normal Distribution
Larger survey population of a town Column width
10mm

• Centered about mean
• Characteristic Bell shape curve
• Number of occurrences reduce as they deviate from
  the mean

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Normal Distribution
A very small sample interval approximates a curve
as shown
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Normal Distribution
All measurable attributes show a variation Spread
of Sizes Normal Distribution
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Normal Distribution

• The spread or width of the curve has a precise
  mathematical meaning - Variance
• The greater the variance the wider the curve
• Defined by a parameter called the standard
  deviation

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Normal Distribution
Calculation of standard distribution
(sigma) -measured sizes of a sample of parts
y1,y2etc are the measured values of the sample
is the average value N is the number of
samples taken
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Normal Distribution
Sharpen 5 pencils to a length of 8 mm
6.5mm 8.2mm 8.5mm 7.5mm 7.0mm
Mean average 7.54
Sigma
SIGMA 0.73
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Normal Distribution

• If sigma is known then we know that
• 95 of parts will lie within /- 2s of the mean
• 99.74 of parts will lie within /- 3s of the mean

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Control Charts

• Used to establish the control limits for a
  process
• Used to monitor the process to show when it is
  out of control
• X-bar Chart (Mean Charts)
• R Charts (Range Charts)

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Control Charts
Process Mean Mean of Sample Means Upper
control limit (UCL) Process mean3 sigma Lower
control limit (LCL) Process mean-3 sigma
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Control Charts X-bar Charts
1. Record measurements from a number of samples
sets (4 or 5)
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Control Charts X-bar Charts
2. Calculate the mean of each sample set
Oven temperature data Oven temperature data Oven temperature data
Morning Midday Evening Daily Means
Monday 210 208 200 206
Tuesday 212 200 210 207
Wednesday 215 209 220 215
Thursday 216 207 219 214
Friday 220 208 215 214
Saturday 210 219 200 210
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Control Charts X-bar Charts
3. Calculate the process mean (mean of sample
means)
Daily Means
206
207
215
214
214
210
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Control Charts X-bar Charts

• 4. Calculate UCL and LCL
• UCL process mean 3ssample
• LCL process mean - 3ssample
• The standard deviation ssample of the sample
  means
• where n is the sample size (3 temperature
  readings)
• s process standard deviation (4.2 degrees)
• ssample

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Control Charts X-bar Charts

• 4. Calculate UCL and LCL
• UCL process mean 3ssample
• LCL process mean - 3ssample
• ssample
• UCL 211 3(2.42) 218.27 degrees
• LCL 211 3(2.42) 203.72 degrees

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Control Charts X-bar Charts

UCL 218.27 LCL 203.72
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Control Charts X-bar Charts
Interpreting control charts Process out of
control
Last data point is out of control indicates
definite problem to be addressed immediately as
defective products are being made.
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Control Charts X-bar Charts
Interpreting control charts Process in control
Process still in control but there is a steady
increase toward the UCL. There may be a possible
problem and it should be investigated.
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Control Charts X-bar Charts
Interpreting control charts Process in control
All data points are all above the process mean.
This suggests some non-random influence on the
process that should be investigated.
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Control Charts Range Charts
The range is the difference between the largest
and smallest values in a sample. Range is used
to measure the process variation 1. Record
measurements from a set of samples
Oven temperature data Oven temperature data Oven temperature data
Morning Midday Evening
210 208 200
212 200 210
215 209 220
216 207 219
220 208 215
210 219 200
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Control Charts Range Charts
2. Calculate the range highest lowest reading
Oven temperature data Oven temperature data Oven temperature data Oven temperature data
Morning Midday Evening Range
210 208 200 10
212 200 210 12
215 209 220 11
216 207 219 12
220 208 215 12
210 219 200 19
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Control Charts Range Charts
3. Calculate UCL and LCL UCL D4 x Raverage
LCL D3 x Raverage
Sample size (n) D3 D4
2 0 3.27
3 0 2.57
4 0 2.28
5 0 2.11
6 0 2.00
7 0.08 1.92
8 0.14 1.86
9 0.18 1.82
10 0.22 1.78
11 0.26 1.74
UCL 2.57 x 13 33.41 degrees LCL 0
x 13 0 degrees
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Control Charts Range Charts
UCL
R average
LCL
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Process Capability

• Matches the natural variation in a process to the
  size requirements (tolerance) imposed by the
  design
• Filling a box with washers
• exact number not in all boxes
• upper limit set
• lower limit set

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Process Not Capable
Process not capable a lot of boxes will be over
and under filled
Normal distribution gt specifications Cannot
achieve tolerances all of the time
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Process Capable
Process capable However there will still be a
small number of defective parts
Normal distribution is similar to
specifications Tolerances will be met most of the
time
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Process Capable
Process capable No defective parts
Normal distribution lt specifications Tolerances
will be met all the time
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Process Capability Index

• If Cp 1
• Process is capable
• i.e. 99.97 of the natural variation of the
  process will be within the acceptable limits

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Process Capability Index

• If Cp gt 1
• Process is capable.
• i.e. very few defects will be found less than
  three per thousand, often much less

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Process Capability Index

• If Cp lt1
• Process is NOT capable
• i.e. the natural variation in the process will
  cause outputs that are outside the acceptable
  limits.

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Statistical Process Control
Is doing things right 99 of the time good enough?
13 major accidents at Heathrow Airport every 2
days 5000 incorrect surgical procedures per week

Pharmaceutical company producing 1 000 000
tablets a week, 99 quality would mean
tablets would be defective!
10 000
Process of maintaining high quality standards is
called Quality Assurance
Modern manufacturing companies often aim for a
target of only 3 in a million defective parts.
The term six sigma is used to describe quality at
this level
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Sampling

• Size of Sample?
• Sufficient to allow accurate assessment of
  process
• More does not improve accuracy
• Less reduced confidence in result

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Sampling
Size of Sample
S sample size e acceptable error - as a
proportion of std. deviation z number relating
to degree of confidence in the result
Confidence Value for z
99 2.58
95 1.96
90 1.64
80 1.28
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Sampling

• Example find mean value for weight of a packet
  of sugar
• with a confidence of 95
• acceptable error of 10
• Weight of packet of sugar 1000g
• Process standard deviation 10g

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Sampling
z 1.96 from the table e 0.1
(i.e.10) Therefore the sample size s (1.96 /
0.1)2 Therefore s 384.16 Sample size 385
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Sampling
Assume mean weight 1005g Sigma 10g
Therefore error 10g 10 11g Result 95
confident average weight of all packets of sugar
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QC, QA and TQM

• Quality Control
• emerged during the 1940s and 1950s
• increase profit and reduce cost by the inspection
  of product quality.
• inspect components after manufacture
• reject or rework any defective components
• Disadvantages
• just detects non-conforming products
• does not prevent defects happening
• wastage of material and time on scrapped and
  reworked parts inspection process not foolproof
• possibility of non-conforming parts being shipped
  by mistake

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QC, QA and TQM

• Quality Assurance
• Set up a quality system
• documented approach to all procedures and
  processes that affect quality
• prevention and inspection is a large part of the
  process
• all aspects of the production process are
  involved
• system accredited using international standards

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QC, QA and TQM

• Total Quality Management
• International competition during the 1980s and
  1990s
• Everybody in the organisation is involved
• Focussed on needs of the customer through
  teamwork
• The aim is zero defect production

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Just-In-Time Manufacture
Modern products shortened life
cycle Manufacturer pressure for quick response
Quick turnaround - hold inventory of stock
Holding inventory costs money for
storage Inventory items obsolete before use New
approach Just-In-Time Manufacture
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Just-In-Time Manufacture

• Underlying concept Eliminate waste.
• Minimum amount
• Materials
• Parts
• Space
• Tools
• Time
• Suppliers are coordinated with manufacturing
  company.

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Just-In-Time Manufacture
Kanbans Japanese word for card Order form for
components Passed from one station to
another Initiates the production or movement of
parts