No, Stability tracks change in a specific lot over time. Process control tracks how different lots adhere to a target.

Statistics for stability center around multiple regression.

]]>Hello D Limit,

I would like to help provide an answer to parts of your question. If I read your question correctly, it illustrates a common point of confusion between Sigma, a measure of dispersion, and Sigma Level, a metric of process capability. They both use the same word–Sigma which can sometimes be confusing. But, Sigma Level and Sigma are NOT EQUIVALENT and many people struggle with this issue.

Sigma Level refers to the number of Sigma, or process standard deviations, between the mean and the closest specification for a process output. Note that when we talk about Sigma Level, this is looking at the process capability to produce within the CUSTOMER SPECIFICATIONS. There is a lot of material out there about the 1.5 shift so I won’t dive into that discussion here – you can read check that out. But the shift is used in the Sigma level to accommodate for process shifts that occur over time. Again, the Sigma level is the measurement of success in achieving a defect-free output which uses the standard deviation and the customers’ specification limit to determine process capability.

Whereas, Sigma in the control charts is about the capability of your PROCESS. You start with the average (or median, mode, and etc.,) which is a measure that represents the standard deviation. You are looking at the process and process capability – you are not looking at the process capability against your customer specifications, so you do not factor in the 1.5 shift on a process chart.

Regards,

Keith Kornafel

if all values of x bar are close to central line and none are near 3 sigma limits .in fact, when you draw one sigma limits all the points fall within narrow limits this is called hugging

would such a chart make you suspicious that something was wrong?

why?

what possible explanations occur to you that might account for an x bar chart of this type ]]>

What you might really want is a mean with 95.5% confidence interval. This requires the assumption of normality.

]]>Company X produces a lot of boxes of Caramel candies and other assorted sweets that are sampled each hour. To set control limits that 95.5% of the sample means, 30 boxes are randomly selected and weighed. The standard deviation of the overall production of boxes iis estimated, through analysis of old records, to be 4 ounces. The average mean of all samples taken is 15 ounces. Calculate control limits for an X – chart. ]]>

Think about your key diameter manufacture as merely a ‘process’.

Firstly you need to gather the experts to determine an appropriate sample size and frequency to take that is representative of the process.

You can then start plotting points of your process as a learning phase. As a rough guide, take 25 points during this phase. Once the 25 points are taken, you can calculate all of your control limits off that. For now, take no notice of the tolerance limits. They will be considered for capability after you have a stable process.

Once you have your limits, lock them in place, continue to run your process using these limits and look out of assignable causes. If an assignable cause arises, you need to understand why.

You’ll find that perhaps the process changes mean or spread, either by an improvement or something has changed that is detrimental to your process.

To re-calculate limits, you must know the answer to these four questions:

1. Is there evidence of assignable cause?

2. Is the cause known?

3. Is the change in process improvement desirable?

4. Is the change sustained?

I hope this helps.

]]>I am new here, your topics are really informative.I’ve been working in the quality for almost 10 years and want to pursue a career in Quality Engineering. I tried making a control chart but have doubt about it. Example: I have a KEY Diameter of 1.200 ±.001 and want to have a control chart for it. What could be the UCL and LCL? ]]>

UCL = c4 (ni)s + kc5 (ni)s

where:

c4 and c5 = values from a table

ni = size of the ith subgroup

k = the parameter that is specified for Test 1 of the tests for special causes, 1 point > K standard deviations from center line. By default, k =3.

s = the estimated standard deviation using sum of squares method

]]>Thank you,

Wil

]]>I have 10 subgroup, each subgroup has different sampel size. The object that is being inspect is chair and there are 4 observed component per chair. I have been told that control chart used in this case is p chart with proportion of each subgroup is total defective components/(number of chair*4). This is what I’m confused about, what defect proportion is that? Is it the proportion of defective chair or proportion of defective component? ]]>

What is the best approach to build a control chart for this kind of data, can you please recommend a reference.

Thank you. ]]>

This summary helped me a lot but I have still have questions, If I’m working in an assembly with two stations

(A–>B) and I’m having defectives in station A but are still re workable and I can still proceed them to station B. Should I plot those defectives from station A in my p-chart?

Thanks for any answers!

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