AIAG – Statistical Process Control (SPC) 2nd soundofheaven.info - Ebook download as PDF This Manual is copyrighted by DaimlerChrysler Corporation, Ford Motor. AIAG SPC MANUAL 3RD EDITION. Format: PDF. VISTAS THIRD EDITION LAB MANUAL. edition of the AIAG PPAP manual unless otherwise specified by. relationship between SPC and continuous To use SPC to achieve a state of statistical Statistical Process Control (SPC) applies statistical.
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It is not intended to limit evolution of SPC) methods suited to particular processes or Additional manuals can be ordered from AIAG and/or permission to copy. Keep AIAG's APQP manual on hand as a reference guideline. The manual covers the majority of situations that occur in early planning, design, or process. Be sure the measurement system analysis (MSA) is acceptable before collecting data . Also, per the AIAG PPAP manual (4th Edition) if the above items are not.
Elements of Control Charts During the initial analysis of the process. Gathering data and using statistical methods to interpret them are not ends in themselves. Examples include the presence of a required label, the continuity of an electrical circuit, visual analysis of a painted surface, or errors in a typed document. J Log any pertinent observation s. The disadvantage of stoplight control is that it has a higher false alarm rate than an X and R chart of the same total sample size.
For example. Any inferences drawn from computed indices should be driven by appropriate interpretation of the data from which the indices were computed.
Process indices can be divided into two categories: That is. The special cause to be allowed has been shown to act in a consistent manner over a known period of time. These results are used as a basis for prediction of how the process will perform. All indices have weaknesses and can be misleading. It is the reader's responsibility to communicate with their customer and determine which indices to use.
Chapter IV deals with selected capability and performance indices and contains advice on the application of those indices. If the specification is inappropriate. In some cases. It is important to remember that most capability indices include the product specification in the formula.
In applying the concept of continual improvement to processes, there is a three-stage cycle that can be useful see Figure 1. Every process is in one of the three stages of the Improvement Cycle.
Among the questions to be answered in order to achieve a better understanding of the process are: What should the process be doing? J What is expected at each step of the process?
J What are the operational definitions of the deliverables? What can go wrong? J What can vary in this process? J What do we already know about this process' variability? J What parameters are most sensitive to variation? What is the process doing? J Is this process producing scrap or output that requires rework?
J Does this process produce output that is in a state of statistical control? J Is the process capable? J Is the process reliable? Many techniques discussed in the APQP iManua17may be applied to gain a better understanding of the process. These activities include: Group meetings Consultation with people who develop or operate the process "subject matter experts" Review of the process' history o.
These simple statistical methods help differentiate between common and special causes of variation. The special causes of variation must be addressed. When a state of statistical control has been reached, the process7 current level of long-term capability can be assessed see Chapter IV. Once a better understanding of the process has been achieved, the process must be maintained at an appropriate level of capability.
Processes are dynamic and will change. The performance of the process should be monitored so effective measures to prevent undesirable change can be taken. Again, the simple statistical methods explained in this manual can assist. Construction and use of control charts and other tools will allow for efficient monitoring of the process.
When the tool signals that the process has changed, quick and efficient measures can be taken to isolate the cause s and act upon them. It is too easy to stop at this stage of the Process Improvement Cycle. It is important to realize that there is a limit to any company's resources. Some, perhaps many, processes should be at this stage.
However, failure to proceed to the next stage in this cycle can result in a significant competitive disadvantage. The attainment of "world class" requires a steady and planned effort to move into the next stage of the Cycle. Up to this point, the effort has been to stabilize the processes and maintain them.
However, for some processes, the customer will be sensitive even to variation within engineering specifications see Chapter IV. In these instances, the value of continual improvement will not be realized until variation is reduced. At this point, additional process analysis tools, including more advanced statistical methods such as designed experiments and advanced control charts may be usefid. Appendix H lists some helpful references for further study.
Process improvement through variation reduction typically involves purposefully introducing changes into the process and measuring the effects. The goal is a better understanding of the process, so that the common cause variation can be further reduced. The intent of this reduction is improved quality at lower cost. When new process parameters have been determined, the Cycle shifts back to Analyze the Process. Since changes have been made, process stability will need to be reconfirmed.
The process then continues to move around the Process Improvement Cycle. These three phases are repeated for continual process improvement. Section G Control Charts: Calculate trial control limits from process data. Identify special causes of variation and act upon them. Ascribe a variation or a mistake to a system common causes. There is a common misconception that histograms can be used for this purpose. Never doing anything to try to find a special cause is a common example of mistake No.
Although several classes of methods are useful in this task. Shewhart 1. Deming identifies two mistakes frequently made in process control: Experience has shown that control charts effectively direct attention toward special causes of variation when they occur and reflect the extent of common cause variation that must be reduced by system or process improvement. Histograms are the graphical representation of the distributional form of the process variation.
Ascribe a variation or a mistake to a special cause. Unfortunately normality does not guarantee that there are no special causes acting on the process. He developed a simple but powerful tool to separate the two.
Deming and Deming Since that time. He first made the distinction between controlled and uncontrolled variation due to what is called common and special causes. Over adjustment [tampering] is a common example of mistake No. Shewhart realized this and developed a graphical approach to minimize. It is impossible to reduce the above mistakes to zero.
Also a nonnormal distribution may have no special causes acting upon it but its distributional form is non-symmetric. Mistake 2. Time-based statistical and probabilistic methods do provide necessary and sufficient methods of determining if special causes exist. The distributional form is studied to verify that the process variation is symmetric and unimodal and that it follows a normal distribution.
But how are these limits determined? Consider a process distribution that can be described by the normal form. As with all probabilistic methods some risk is involved. Since the normal distribution is described by its process location mean and process width range or standard deviation this question becomes: Has the process location or process width changed?
Consider only the location. Tools For Process Control and Improvement If process control activities assure that no special cause sources of variation are activelo.
The exact level of belief in prediction of future actions cannot be determined by statistical measures alone. Data should always be presented in such a way that preserves the evidence in the data for all the predictions that might be made from these data.
The goal is to determine when special causes are affecting it. Subject-matter expertise is required. To do this. The active existence of any special cause will render the process out of statistical control or "out of control. Another way of saying this is. Whenever an average.
What approach can be used to determine if the process location has changed? One possibility would be to look at 10 11 This is done by using the process infomation to identify and eliminate the existence of special causes or detecting them and removing their effect when they do occur.. When Shewhart developed control charts lie was concerned with the economic control of processes. The alternative is to use a sample of the process. If the sample falls outside these limits then there is reason to believe that a special cause is present.
If a group of samples shows a pattern there is reason to believe that a special cause is present. The mean of the sample is the individual sample itself.
Using the formula: Has the process changed n. Shewhart selected the k3 standard deviation limits as useful limits in achieving the economic control of processes. But how is this possible?
After all. These are called control limits. Tools For Process Control and Improvement every part produced by the process. The answer is that this very rarely happens. Section C. Section A. With such random samples from the distribution. To make this a little clearer.
Doesn't that imply that the process mean remains the same? The reason for this is that the sample mean is only an estimation of the process mean. J Is the measurement system appropriate and acceptable?
Plot the data: J Plot using the time order Compare to control limits and determine if there are any points outside the control limits 14 Plan-Do-Study-Act cycle. Are the data reliable. Since Control Charts provide the operational definition of "in statistical control. J Are the data consistent. J Is the metric appropriate. Plan-Do-Check-Act cycle. Section F. Within each stage. Tools For Process Control and Improveinent Compare to the centerline and determine if there are any nonrandom patterns clearly discernible Analyze the data Take appropriate action The data are compared with the control limits to see whether the variation is stable and appears to come from only common causes.
After all special causes have been addressed and the process is running in statistical control. Plot each point as it is determined: J Compare to control limits and determine if there are any points outside the control limits J Compare to the centerline and determine if there are any nonrandom patterns clearly discernible 9 Analyze the data Take appropriate action: J Continue to run with no action talten.
If the variation from common causes is excessive.
The process itself must be investigated.. J Will the data be reliable. Process capability can also be calculated.
After actions see Chapter I. If special causes of variation are evident. Is the metric appropriate. Section D have been talten. J Will the data be consistent. For a process that is in statistical control. Signals of special causes of variation do not require the recalculation of control limits.
One area deserving mention is the question of recalculation of control chart limits. Once properly computed. Tools For Process Control and Improvement match this value. They do not realize that if a process is stable and the control limits are calculated correctly. As this understanding matures.
For continual process improvement. The purpose of the Improvement Cycle is to gain an understanding of the process and its variability to improve its performance. Some special causes. Improve the Process. This may not always hold true.. Maintain Control the Process. For long-term analysis of control charts.
This assumes that this adjustment does not affect the process variation. For those processes where the actual location deviates from the target and the ability to relocate the process is economical. New evidence of special causes might be revealed. Many people. The long-term performance of the process should continue to be analyzed. Reducing this variation will have the effect of "shrinking" the control limits on the control chart i.
Analyze the Process. For example: This can be accomplished by a periodic and systematic review of the ongoing control charts. Lower unit cost. Be used by operators for ongoing control of a process e Help the process perform consistently and predictably Allow the process to achieve.
Higher effective capability e Provide a common language for discussing the performance of the process Distinguish special from common causes of variation. Higher quality. Establish an open engineering environment that minimizes internal competition and supports cross-functional teamwork.
Support and fund engineering management and employees training in the proper use and application of SPC. Establish an open environment that minimizes internal competition and supports cross-functional teamwork. Apply SPC to promote the understanding of variation in engineering processes. The gains and benefits from the control charts are directly related to the following: Management Philosophy: How the company is managed can directly impact the effectiveness of SPC.
Apply SPC to management data and use the information in day-today decision making. The following are some ways that engineering can show effective use of SPC: Focus the engineering organization on variation reduction throughout the design process. Engineering Philosophy: How engineering uses data to develop designs can and will have an influence on the level and type of variation in the finished product.
Make regular visits and asks questions in those areas. Show support and interest in the application and resulting benefits of properly applied SPC.
The following are examples of what needs to be present: Focus the organization on variation reduction. Require an understanding of variation and stability in relation to measurement and the data that are used for design development. The above items support the requirements contained in IS0 Support and fund management and employee training in the proper use and application of SPC. Do not release control charts to operators until the process is stable. Mentor key people in the organization in the proper application of SPC.
Use the analysis of SPC information to support process changes for the reduction of variation.. Assist in the identification and reduction of the sources of variation. Assure proper placement of SPC data for optimum use by the employees. They should: Be properly trained in the application of SPC and problem solving. Apply SPC in the understanding of variation in the manufacturing processes. Have an understanding of variation and stability in relation to measurement and the data that are used for process control and improvement.
Ensure optimum use of SPC data and information. Quality Control: The Quality function is a critical component in providing support for an effective SPC process: Support SPC training for management. Require an understanding of variation and stability in relation to measurement and the data that are used for process design development.
How manufacturing develops and operates machines and transfer systems can impact the level and type of variation in the finished product: Focus the manufacturing organization on variation reduction.
Support and fund manufacturing management and employees training in the proper use and application of SPC. Production personnel are directly related to the process and can affect process variation. The transfer of responsibility for the process to production should occur after the process is stable. At a minimum. Proper use of SPC can result in an organization focused on ilnproving the quality of the prodtict and process.
Then the Plan-Do-Study-Act process can be used to fiirther improve the process. Individuals I chart. For example you need a larger sample size for attributes than for variables data to have the same amount of confidence in the results.. There are basically two types of control charts. Some of the more common chart types. When introducing control charts into an organization. If the use of variables measurement systems is infeasible.
Moving Range MR chart. The use of attributes control charts on key overall quality measures often points the way to the specific process areas that would need more detailed examination including the possible use of control charts for variables. If available.
If the data derived from the process are of a discrete nature e. The process itself will dictate which type of control chart to use. If the data derived from the process are of a continuous nature e.. Charts based on count or percent data e. Average X and Range R charts. Problem signals can come from the cost control system..
Within each chart type there are several chart combinations that can be used to further evaluate the process. Some current metrology literature defines accuracy as the lack of bias.
X is the arithmetic average of the values in small subgroups. A quantitative value e. This is impoi-taiit in seeking continual improvement. This can lead to lower total measurement costs due to increased efficiency. Although collecting variables data is usually more costly than collecting attsibutes data e. A variables chai-t can explain process data in terms of its process variation.
CHAPTER I1 Control Chai-ts Variables conti charts represent the typical application of statistical process control where the processes and their outputs can be characterized by variable ineasurements see Figure X The X and R charts may be the most common charts.
Because fewer pasts need to be clieclted before inaltiiig reliable decisions. Because of this. R is the range of values within each subgroup highest minus lowest. The most commonly used pair are the and R charts.
Do inspectors agree? How is it measured? Any material applied to mirror back to shall not cause visible staining of the backing Visible to whom? Under what conditions ' Figure Surface should conform to master standard in color.
Attributes Data. It should not be inferred that these are the only "acceptable" categories or that attributes charts cannot be used with Case 1 processes. With simple gaging e g. Attributes data are already available in many situations.
Attributes data have discrete values and they can be counted for recording and analysis. Wheeler 1. See also: Montgomery There are many occasions where specialized measurement slcills are required especially when the part measured falls in the "gray" area? Much data gathered for management summary reporting are often in attributes form and can benefit from control chart analysis.
Examples include scrap rates. Because of the ability to distinguish between special and common cause variation. Where new data must be collected. Control charts for attributes are important for several reasons: Attributes data situations exist in any technical or administrative process.
With attribute analysis the data are separated into distinct categories conforminglnonconforming. Wise and Fair The most significant difficulty is to develop precise operational definitions of what is conforming. The only expense involved is for the effort of converting the data to control chart form. This manual will use conforminglnonconforming throughout attributes discussions simply because These categories are "traditionally" used Organizations just starting on the path to continual improvement usually begin with these categories Many of the examples available in literature use these categories.
Examples include the presence of a required label. In these cases. LCL The ability to determine outliers which signal special causes the control chart requires control limits based on the sampling distribution.
This information should. For process control the analysis for special causes and their identification should occur as each sample is plotted as well as periodic reviews of the control chart as a whole for nonrandom patterns. Specifications limits should not be used in place of valid control limits for process analysis and control. A scale which yields a "narrow" control chart does not enable analysis and control of the process. E Event Log Besides the collection. D Identification of out-of-control plotted values Plotted points which are out of statistical control should be identified on the control chart.
Any format is acceptable as long as it contains the following see Figure B Centerline The control chart requires a centerline based on the sampling distribution in order to allow the determination of non-random patterns which signal special causes. Section E must be kept in mind. C Subgroup sequence I timeline Maintaining the sequence in which the data are collected provides indications of "when" a special cause occurs and whether that special cause is time-oriented.
However the reasons for the use of control charts see Chapter I. This information can be recorded on the control chart or on a separate Event Log. Such events need not be identified in subsequent information collection activities. If initial information collection activities are not sufficiently comprehensive. Elements of Control Charts During the initial analysis of the process.
S2 Sx indicates the shift. Sample Control Chart back side Event Log j The measurement performance must be predictable in terms of accuracy. It is very important to evaluate the effect of the measurement system's variability on the overall process variability and determine whether it is acceptable.
In addition to being calibrated. The characteristic must be operationally defined so that results can be communicated to all concerned in ways that have the same meaning today as yesterday.
Correlation between characteristics. Current and potential problem areas. J Define the process. In the absence of process knowledge. Periodic calibration is not enough to validate the measurement system's capability for its intended use. This involves specifying what information is to be gathered. J Define the characteristic. J Define the measurement system. Attributes control charts would be used to monitor and evaluate discrete variables whereas variables control charts would be used to monitor and evaluate continuous variables.
Establish an environment suitable for action. Total process variability consists of part-to-part variability and measurement system variability. Determine the features or characteristics to be charted based on: The customer's needs. Unnecessary external causes of variation should be reduced before the study begins. For more details see Chapter I. Even though convenience sampling andlor haphazard 1 sampling is often thought of as being random sampling.
The purpose is to avoid obvious problems that could and should be corrected without use of control charts.
The definition of the measurement system will determine what type of chart. In all cases. This includes process adjustment or over control. Section H. This will aid in subsequent process analysis. This could simply mean watching that the process is being operated as intended.
If one i assumes that it is. J Assure selection scheme is appropriate for detecting expected special causes. A rational subgroup is one in which the samples are selected so that the chance for variation due to special causes occurring within a subgroup is minimized. Interpret for Statistical Control 4.
In general. The sampling frequency will determine the opportunity the process has to change between subgroups.. These measurements are combined into a control statistic e.
Establish Control Limits 3. As stated earlier. Subgroup Size. The type of process under investigation dictates how the subgroup size is defined. The team responsible has to determine the appropriate subgroup size. Extend Control Limits for ongoing control see Figure Taking consecutive samples for the subgroups minimizes the opportunity for the process to change and should minimize the within-subgroup variation.
Data Collection 2. Create a Sampling Plan For control charts to be effective the sampling plan should define rational subgroups.
The measurement data are collected from individual samples from a process stream. The key item to remember when developing a sampling plan is that the variation between subgroups is going to be compared to the variation within subgroups. If the expected shift is relatively small. The samples are collected in subgroups and may consist of one or more pieces. The variation within a subgroup represents the piece-to-piece variation over a short period of time.
For special causes that are laown to occur at specific times or events. This chart assumes the meas- urement system has been assessed and is appropriate.
Until now, there has been no unified formal approach in the automotive industry on statistical process control. Certain manufacturers provided methods for their suppliers, while others had no specific requirements. In an effort to simplify and minimize variation in supplier quality requirements, Chrysler, Ford, and General Motors agreed to develop and, through AIAG, distribute this manual.
Brown of General Motors. The manual should be considered an introduction to statistical process control. It is not intended to limit evolution of statistical methods suited to particular processes or commodities nor is it intended to be comprehensive of all SPC techniques.
Establish a foundational knowledge-base to analyze your manufacturing system and enhance its effectiveness. Gain a basic understanding of how to establish, analyze and implement a statistical process control SPC system in a manufacturing environment.
Improve your understanding of the integration of statistical process control SPC and measurement systems analysis MSA into IATF and discover how to develop a higher quality process control system by selecting and applying the appropriate statistical tools. Examine methods for implementing and applying the principles of statistical process control to manufacturing processes.
Want to make sure that the learning sticks? The manual covers the majority of situations that occur in early planning, design, or process analysis phases. Program Management: