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Adding to Cart Want AIAG to bring the training to you? Check out our Onsite Training options or contact us at onsite aiag. Not a member? Control : System, policies, and procedures in place the overall output meets the requirement.
Identify the processes: Find those key process that have impact on the output of the product or the process which are crucial to the customer. Determine measurable attributes of the process: Determine the attributes that need to measure during the production. From the above instance, consider the plate thickness as a measurable attribute.
For instance, consider thickness gage to measure the thickness and create an appropriate measuring procedure. For instance collect 20 sets of plate thicknesses in a time sequence with a subgroup size of 4.
For instance, plate thicknesses with a subgroup size of 4, select Xbar -R chart. Narrate natural variation of attributes: Compute the control limits. Source: www. A Control chart is one of the main techniques of statistical process control SPC. The control chart is a graphical display of quality characteristics which comprises a center line to show the average value of the quality characteristics and 2 horizontal lines known as upper control limit UCL and lower control limit LCL.
Choosing a right control chart is essential in control charts mapping. Otherwise, it will end up with inaccurate control limits for the data. The selection of control chart depends on the data type: Continuous Variable or Discrete Attribute.
Continuous Variable data: It is information which is continuously measured; the values are any real numbers that are measured and noted on the SPC chart. They find out answers of questions like what height, how broad, how much time passed.
Discrete Attribute data : It is information that is discretely observed; the values can only be integers that are counted, not measured, and noted on an SPC chart.
They answer questions of how often, or what kind. A data point is triggered whenever the feature being watched is observed. X bar-R Charts. X bar-R chart is used to monitor the process performance of a continuous data and the data to be collected in subgroups at a set time periods.
X bar-S Charts. X Bar S charts often used control chart to examine the process mean and standard deviation over the time. These charts are used when the subgroups have large sample size and S chart provides better understanding of the spread of subgroup data than range.
X- MR Charts. X-MR chart is used when the data is continuous and also not gathered in subgroups. If the data is discrete, we capture Defects and subgroup size is the same, we use c Chart. P chart is used when the data are the fraction defective of some set of process output. A u chart displays how the frequency of defects, or nonconformities, is changing over time for a process or system.
Measurement system analysis MSA finds out whether the measurement system is ample for use by evaluating its accuracy, precision, and stability.
Measurement processes comprises measures, such as gages or software. The aim of the measurement system analysis is to conserve the integrity of both the data collection and the data itself. There are 5 components to investigate in MSA. The following components of measurement error need to be evaluated and passed before establishing process capability and making decisions from the data. A control chart always has a central line for the average, an upper line for the upper control limit and a lower line for the lower control limit.
These lines are determined from historical data. By comparing current data to these lines, you can draw conclusions about whether the process variation is consistent in control or is unpredictable. There are several functions of a control chart: It centres attention on detecting and monitoring process variation over time. It provides a tool for on-going control of a process. It differentiates special from common causes of variation in order to be a guide for local or management action.
It helps improve a process to perform consistently and predictably to achieve higher quality, lower cost, and higher effective capacity. It serves as a common language for discussing process performance.
Control charts are effective in defect prevention. Control charts prevent unnecessary process adjustment. Control charts provide diagnostic information.
Randomly sample n units throughout the day can be every 5 minutes, every 30 minutes, etc. If they are, then your process is considered to be in a state of statistical control as far as the variability of the process is concerned 6. If they are, then your process is considered in a state of statistical control as far as the average of the process is concerned.
The quality of the individual points of a subset is determined unstable if any of the following occurs: Rule 1: Any point falls beyond 3 from the centreline this is represented by the upper and lower control limits.
Rule 2: Two out of three consecutive points fall beyond 2 on the same side of the centreline. Rule 3: Four out of five consecutive points fall beyond 1 on the same side of the centreline. Rule 4: Nine or more consecutive points fall on the same side of the centreline. Number of leaking caulking tubes in a box of 48 Number of broken eggs in a carton Use C-Charts for discrete defects when there can be more than one defect per unit Eg. Suppose that:. Suppose that: --the tolerance is.
We wouldnt have very many part problems! The question isnt IF units vary. Its how much, when, and why. The most commonly used statistic to estimate this distance is the Standard Deviation also called Sigma.
It is a measure of how well a process is within a specification. A bigger A and a smaller B are better. The spec limits are the edges of your garage door. Design the product so a wider tolerance is functional robust design 2. Choose equipment and methods for a good safety margin process capability 3. Correctly adjust, but only when needed control 4. This process is producing good units with a good safety margin. Very good! Mean 58 A B What does a problem Cpk look like? This process is in danger of producing some defects.
It is too close to the specification limits. Remember: the bell curve tail goes further than B we only show the bell to 3-sigma to make it easier to draw. Not good! A significant part of the tail is hanging out beyond the spec limits. This process is producing scrap, rework, and customer rejects.
We desire a minimum of 1. What is It? Objective or Purpose To determine how much error is in the measurement due to the measurement process itself. Quantifies the variability added by the measurement system.
Applicable to attribute data and variable data. When to Use It On the critical inputs and outputs prior to collecting data for analysis.
For any new or modified process in order to ensure the quality of the data. Measurement System Analysis is an analysis of the measurement process, not an analysis of the people!!
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