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Chapter 36 Quality Engineering (Part 1) EIN 3390 Manufacturing Processes Spring, 2011PowerPoint Presentation

Chapter 36 Quality Engineering (Part 1) EIN 3390 Manufacturing Processes Spring, 2011

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Chapter 36 Quality Engineering (Part 1) EIN 3390 Manufacturing Processes Spring, 2011

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Chapter 36 Quality Engineering (Part 1) EIN 3390 Manufacturing Processes Spring, 2011

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Chapter 36Quality Engineering(Part 1)EIN 3390 Manufacturing ProcessesSpring, 2011

USL -

FIGURE 36-1 Over many years, many techniques have been used to reduce the variability in

products and processes.

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 so on.

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-kahyoh-kay") means to avoid (yokeru) inadvertent errors (poka).

- 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.

- 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.

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.

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.

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.

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.

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.

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.

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.

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.

FIGURE 36-4 Example of

calculations to obtain estimates

of the mean (m) and standard

deviation (s) of a process

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.

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.

FIGURE 36-6 Common probability distributions that can be used to describe the outputs

from manufacturing processes. (Source: Quality Control Handbook, 3rd ed.)

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.

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.

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.

FIGURE 36-8 An example of a

run chart or graph, which can

reveal trends in the process

behavior not shown by the

histogram.

The most popular PC index indicates if the process has the ability to meet specifications.

The process capability 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

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)]

FIGURE 36-9 The output from the process is shifting toward the USL, which changes the Cpk ratio but not the Cp ratio.

In Fig. 36 – 10, the following five cases are covered.

- 6s < USL –LSL or Cp > 1
- 6s < USL –LSL, but process has shifted.
- 6s = USL –LSL, or Cp = 1
- 6s > USL –LSL or Cp < 1
- 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.

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.

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.

FIGURE 36-11 A linear variable differential transformer (LVDT) is a key element in an inspection station checking part diameters. Momentarily clamped into the sensor fixture, a part pushed the LVDT armature into the device winding. The LVDT output is proportional to the displacement of the armature. The transformer makes highly accurate measurements over a small displacement range.

FIGURE 36-12 Example of a check sheet for gathering data on a process.