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Chapter 36 Quality Engineering (Part 1) EIN 3390 Manufacturing Processes Spring, 2011. USL -. Process Control Methods. FIGURE 36-1 Over many years, many techniques have been used to reduce the variability in products and processes. Objective of Quality Engineering :
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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.36.1 Introduction
- 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).36.1 Introduction
Precision reflects the repeatability of the process.
Process Capacity (PC) study quantifies the inherent accuracy and precision.
- root out problems that can cause defective products during production, and
- design the process to prevent the problem.36.1 Introduction
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
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 Determining Process Capability
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 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.36.2 Making PC Studies by Traditional Methods
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.36.2 Making PC Studies by Traditional Methods
FIGURE 36-6 Common probability distributions that can be used to describe the outputs
from manufacturing processes. (Source: Quality Control Handbook, 3rd ed.)Common Distributions
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.36.2 Histograms
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.Mean vs. Nominal
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.36.2 Run Chart or Diagram
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) =236.2 Process Capability Indexes
Another process capability ratio Cpk for off-center processes:
Cpk = min (Cpu, Cpl)
= min[Cpu= (USL – m)/(3s), Cpl= (m – LSL)/(3s)]36.2 Process Capability Indexes
FIGURE 36-9 The output from the process is shifting toward the USL, which changes the Cpk ratio but not the Cp ratio.Output Shift
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.36.2 Process Capability Indexes
scenarios for a process output
versus the designer’s
specifications for the minimal
(50) and upper and lower
specifications of 65 and 38
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.36.2 Process Capability Indexes
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.