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FIGURE 2-11. Cause-and-effect diagram.
to day, or week to week. Apportioning the variation in a process puts focus on the variables related to a particular type of variation. For example, the positional variation is normally attributed to product or process design, as the defects recur at certain locations. The cyclical variation is attributed to the variables related to the process setup that cause variation in performance from one process cycle to the next. The temporal variation can be related to the maintenance activities, whether daily, weekly, or monthly, as well as degradation in consumable items in the process, such as laser lamps, machine tools, chemical concentrations, or the limited shelf life of a chemical. FMEA. Failure Mode and Effects Analysis (FMEA), as shown in Figure 2-12, is an excellent tool that has been used in mainly the automotive and aerospace industries, or where personnel safety is a concern. As implied by the name, FMEA is used to anticipate potential failure modes during the product or process design or redesign, to determine the effects of failure modes on performance, and to identify action items that will prevent anticipated failure modes. Each failure mode is ranked for severity of the effect on performance, frequency of
CHAPTER TWO
Description of
Protection:The spreadsheets
System Subsystem Component Design Lead Core Team
Potential Failure Mode and (Design Key Date
Item / Function
Potential Failure Mode(s)
Potential Effect(s) of Failure
S e v
Potential Cause(s)/ Mechanism(s) of Failure Over pressure
P r o b 8
Current Design Controls
Coolant containment. Hose connection. Coolant fill.
Crack/break. Leak Burst. Sidewall flex. Bad seal. Poor hose retention Write down each failure mode and potential consequence(s) of that failure.
Burst, validation pressure cycle.
Severity - On a scale of 1 10, rate the Severity of each failure (10 = most severe). See Severity sheet.
Likelihood - Write down the potential cause(s), and on a scale of 1 10, rate the Likelihood of each failure (10 = most likely). See Likelihood sheet.
FIGURE 2-12.
Failure mode and effects analysis template.
SIX SIGMA AN OVERVIEW
FMEA Worksheet
are not protected or locked.
FMEA Number Effects Analysis FMEA) Prepared By FMEA Date Revision Date Page of Action Results D e t R P N Recommended Action(s) Responsibility Actions Taken and Target Completion Date New RPN New Occ New Sev New Det
J.P. Aguire 11/1/95 64 Test included in prototype and E. Eglin 8/1/96 production validation testing.
Response Plans and Tracking
Risk Priority Number - The combined weighting of Severity, Likelihood, and Detectability. RPN = Sev X Occ X Det
Detectability - Examine the current design, then, on a scale of 1 10, rate the Detectability of each failure (10 = least detectable). See Detectability sheet.
FIGURE 2-12. (Continued)
Failure mode and effects analysis template.
CHAPTER TWO
occurrence of its cause, and detection of the failure mode based on the effectiveness of the control methods. A risk priority number (RPN) is calculated by multiplying the ranking for severity, occurrence, and detection. The RPN is used to prioritize the failure modes and corrective actions related to the failure modes.
IMPROVE
The improve phase consists of developing solutions and selecting the optimum solutions for best results and most robust performance. Two key aspects of the improve phase include the use of Design of Experiments (DOE) and change management. The traditional approach to finding a solution to a problem focuses on one variable at a time, holding the other factors constant. Shortcomings of this approach include the following:
It is usually not possible to hold all other variables constant. Too many experiments are required to study the impact of all the input variables. The interaction between variables cannot be determined. The optimum combination between variables may never be revealed. Resources might be wasted in studying the wrong variables.
Statistically designed experiments involve varying two or more variables simultaneously and obtaining multiple measurements under the same experimental conditions. The objective of DOE is to assess the effects of the critical variables and the interaction among them, and then to determine the significance of those effects compared to the experimental error. If the effects of the process changes happen to be significantly better, a new process can be implemented. The advantages of this approach are the following:
Many variables can be measured simultaneously, making the DOE approach more economical. Interactions between variables can be detected and measured.
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