Transcript EIN 3390 Chap 36 Qua..

Chapter 36
Quality Engineering
(Part 1)
(Review)
EIN 3390
Manufacturing Processes
Fall, 2010
36.1 Introduction
Objective of Quality Engineering:
Systematic reduction of variability, as shown
in Figure 36 – 1.
Variability is measured by sigma, s, standard
deviation, which decreases with reduction in variability.
Variation can be reduced by the application of
statistical techniques, such as multiple variable
analysis, ANOVA – Analysis of Variance, designed
experiments, and Taguchi methods.
36.1 Introduction
QE History:
- Acceptance sampling
- Statistical Process Control (SPC)
- Companywide Quality Control (CWQC) and Total Quality
Control (TQC)
- Six Sigma, DOE (Design of Experiment), Taguchi
methods
- Lean Manufacturing: “Lean" is a production practice that
considers the expenditure of resources for any goal other than the
creation of value for the end customer to be wasteful, and thus a
target for elimination
- Poka-Yoke: developed by a Japanese manufacturing engineer
named Shigeo Shingo who developed the concept. poka yoke
(pronounced "poh-kah yoh-kay") means to avoid (yokeru)
inadvertent errors (poka).
Process Control Methods
FIGURE 36-1 Over many years, many techniques have been used to reduce the variability in
products and processes.
36.1 Introduction

In manufacturing process, there are two
groups of causes for variations:
◦ Chance causes – produces random variations,
which are inherent and stable source of
variation
◦ Assignable causes – that can be detected
and eliminated to help improve the process.
36.1 Introduction
Manufacturing process is determined by
measuring the output of the process
 In quality control, the process is
examined to determine whether or not
the product conforms the design’s
specification, usually the nominal size
and tolerance

36.1 Introduction
Accuracy is reflected in your aim (the average
of all your shorts, see Fig 36 – 2)
Precision reflects the repeatability of the
process.
Process Capacity (PC) study quantifies the
inherent accuracy and precision.
Objectives:
- root out problems that can cause defective
products during production, and
- design the process to prevent the problem.
Accuracy vs. Precision
FIGURE 36-2 The concepts of
accuracy (aim) and precision
(repeatability) are shown in the
four target outcomes. Accuracy
refers to the ability of the
process to hit the true value
(nominal) on the average, while
precision is a measure of the
inherent variability of the
process.
36.2 Determining Process Capability
The nature of process refers to both the
variability (or inherent uniformity) and
the accuracy or the aim of the process.
Examples of assignable causes of
variation in process : multiple machines
for the same components, operator
plunders, defective materials, progressive
wear in tools.
36.2 Determining Process Capability
Sources of inherent variability in the
process: variation in material properties,
operators variability, vibration and chatter.
These kinds of variations usually display a
random nature and often cannot be
eliminated. In quality control terms, these
variations are referred to as chance
causes.
36.2 Making PC Studies by Traditional Methods
The objective of PC study is to determine the
inherent nature of the process as compared to
the desired specifications.
The output of the process must be examined under
normal conditions, the inputs (e.g. materials,
setups, cycle times, temperature, pressure, and
operator) are fixed or standardized.
The process is allowed to run without tinkering
or adjusting, while output is documented
including time, source, and order production.
36.2 Making PC Studies by Traditional Methods
Histogram is a frequency distribution.
Histogram shows raw data and desired value,
along with the upper specification limit (USL)
and lower specification limit (LSL).
A run chart shows the same data but the data are
plotted against time.
The statistical data are used to estimate the mean
and standard deviation of the distribution.
Process Capability
1.001
FIGURE 36-3 The process capability study compares the
part as made by the manufacturing process to the
specifications called for by the designer. Measurements from
the parts are collected for run charts and for histograms for
analysis—see Figure 36-4.
Example of Process Control
FIGURE 36-4 Example of
calculations to obtain estimates
of the mean (m) and standard
deviation (s) of a process
36.2 Making PC Studies by Traditional Methods
m +-3s defines the natural capacity limits of the
process, assuming the process is approximately
normally distributed.
A sample is of a specified, limited size and is
drawn from the population.
Population is the large source of items, which
can include all items the process will produce
under specified condition.
Fig. 36 – 5 shows a typical normal curve and the
areas under the curve is defined by the standard
deviation.
Fig. 36 – 6 shows other distributions.
Normal Distribution
FIGURE 36-5 The normal or
bell-shaped curve with the areas
within 1s, 2s, and 3s for
a normal distribution; 68.26% of
the observations will fall within
1s from the mean, and
99.73% will fall within 3s
from the mean.
36.2 Histograms
A histogram is a representation of a frequency
distribution that uses rectangles whose widths represent
class intervals and whose heights are proportional to the
corresponding frequencies.
All the observations within in an interval are considered to
have the same value, which is the midpoint of the
interval.
A histogram is a picture that describes the variation in a
progress.
Histogram is used to 1) determine the process capacity, 2)
compare the process with specification, 3) to suggest
the shape of the population, and 4) indicate
discrepancy in data.
Disadvantages: 1) Trends aren’t shown, and 2) Time
isn’t counted.
Mean vs. Nominal
FIGURE 36-7
Histogram shows
the output mean m
from the process
versus nominal
and the tolerance
specified by the
designer versus
the spread as
measured by the
standard
deviation s. Here
nominal =49.2,
USL =62, LSL
=38, m =50.2, s
=2.
36.2 Run Chart or Diagram
A run chart is a plot of a quality characteristic
as a function of time. It provides some idea of
general trends and degree of variability.
Run chart is very important at startup to identify
the basic nature of a process. Without this
information , one may use an inappropriate tool
in analyzing the data.
For example, a histogram might hide tool wear if
frequent tool change and adjustment are made
between groups and observations.
Example of a Run Chart
FIGURE 36-8 An example of a
run chart or graph, which can
reveal trends in the process
behavior not shown by the
histogram.
36.2 Process Capability Indexes
The most popular PC index indicates if the process
has the ability to meet specifications.
The process capacity index, Cp, is computed as
follows:
Cp = (tolerance spread) / (6s)
= (USL – LSL) / (6s)
A value of Cp >= 1.33 is considered good.
The example in Fig 36-7:
Cp = (USL – LSL)/(6s) = (62 – 38)/(6 x 2) =2
36.2 Process Capability Indexes
The process capability ratio, Cp, only looks at
variability or spread of process (compared to
specifications) in term of sigmas. It doesn’t take
into account the location of the process mean, m.
Another process capability ratio Cpk for off-center
processes:
Cpk = min (Cpu, Cpl)
= min[Cpu= (USL – m)/(3s), Cpl= (m – LSL)/(3s)]
Output Shift
FIGURE 36-9 The output from the process is shifting
toward the USL, which changes the Cpk ratio but not
the Cp ratio.
36.2 Process Capability Indexes
In Fig. 36 – 10, the following five cases are covered.
6s < USL –LSL or Cp > 1
b) 6s < USL –LSL, but process has shifted.
c) 6s = USL –LSL, or Cp = 1
d) 6s > USL –LSL or Cp < 1
e) The mean and variability of the process have both
changed.
If a process capability is on the order of 2/3 to
3/4 of the design tolerance, there is a high
probability that the process will produce all good
parts over a long time period.
a)
FIGURE 36-10 Five different
scenarios for a process output
versus the designer’s
specifications for the minimal
(50) and upper and lower
specifications of 65 and 38
respectively.
reduced
36.2 Process Capability Indexes
In Fig 36 – 11, an automated station checks parts
for the proper diameter with the aid of a linear
variable differential transformer (LVDT).
Embedded in the clamping device is an LVDT position
sensor for measurement of the diameter of a part.
Once the measurement is made, the computer
releases the clamp and the part can move on. If
the diameter is in tolerance, a solenoid-actuated
gate operated by the computer lets the part pass,
otherwise, the part is rejected into a bin.
Check Sheet
FIGURE 36-12 Example of a check sheet for gathering data on a process.