Cp 1.33 why

I hope you can help to clarify these doubt. What would be correct? Thank you for the question. However, I was able to find the following:. A Key Characteristic KC is a feature of a material, process, or part includes assemblies whose variation within the specified tolerance has a significant influence on product fit, performance, service life, or manufacturability. Hello Ted, Thank you so much for the clarification! Best regards. Ted, If I run 30 pieces through a new process can I get an accurate CpK and will this tell me if the process is capable the next time I run pieces?

Or is it best to randomly select pieces throughout the pieces or pick the first 30 pieces from the run of ? Lot of good information in the article.

I needed some clarity on whether I had to do complete process capability studies or a simple process performance would do during PPAP. Your article sheds good info on that.

Say I am measuring torque on a part how much twisting force for it to turn , and the USL is oz-in but the less the better. But then the Cpk is taking the lower of the two Cp values. Thanks for the reply! I am also inclined to agree with that article. Which is unfortunate. At any rate, thanks again! Hi , Can anyone explain this question. Answer is 12 if 6sigma spread for a process is 6, and process average is 16 ,what should be lower spec limit be set to ensure less than.

Hii sir, let consider below situation. Am having maximum material condition tolerance for hole position, So specifications changes depends on part size. I moved the question to the member-only thread here and should be able to work it shortly. Hi What is different between natural tolerance and standard deviation? You can send me the article even better for me, as you propose. We are a resin compounder.

We have a new customer that that molds automotive parts from our resin. They want us to provide Cpk data on our compounding process. Our process is one of discrete batches. For example, we have a blender that holds of a resin recipe. We compound a small amount of the batch and perform property testing on it. If it tests in spec, we run out the batch. If it needs a recipe adjustment for a certain tested property, then we make the adjustment and repeat the process. What we end up with over time is a collection of lot data that is always in-spec but scattered all over the place because when a batch is in-spec we run it out whether centered or just barely in.

Also, our lots for this resin run only four or five times a year. Can we make a case that our bulk material process of discrete batches is just not suited to the type of Cpk statistical analysis they want? Thanks, ME. Hey Ted. I think these two terms have been switched. PP and PPk are used for long term data.

I have learned that you should calculate Cp and Cpk when your process is stable and take a production run of 30 parts without making any adjustments to the machine. I am working with a CNC machine and wish to calculate its capability to hold the tolerance. But, with every part produced, the dimension goes down because of the tool wear and I cannot make a run of 30 parts without making adjustments.

How can I calculate Cp or Cpk in this scenario? Is this related to SPC? Table, an empirical function? We have a few resources on data sampling techniques and sample size here.

Hi, I have a specification, which sets 6 limits for a production process. I have no problem for creating individual measurements control chart, but cannot figure out how to find my upper and lower limits for range chart based on the above conditions. I have a manager asking me to provide CPk data for a special design characteristic feature. We typically do not use CPk to track these, because our processes require that we measure every part and document our results.

Special design characteristics have a very small tolerance band, thus the CPk value will be naturally low. Am I thinking about this correctly? And if so, can you give any advice on how I can simply explain to this manager why his request is unreasonable?

So i need to adjust the machine as to ensure the part result pass assuming there have machine issue. Do i need to remeasure all those 29 parts after adjusting the machine? The values for Cpk and Ppk will converge to almost the same value because sigma and the sample standard deviation will be identical use an F test to determine.

I want to assess the impact on Cp when I chop a tail of a distribution which is normal to begin with. If you have a ready reference please let me know.

I am thinking of the following: say we have normal distribution data with a given sigma and mean. Now I want to see if I remove the lower 10th percentile and the top 90th percentile of the data what will happen to my cp and cpk. I am thinking of the following approach 1.

Knowing 10 and 90th percentile work through the z value and come up with the corresponding x values for these percentiles 2. Make these new x values as the new say 6 sigma values and compute the new sigma — this is trivial but it is a step 3. Compute the new cp and cpk using the same mean but new sigma. The question is: is there a mathematical formulation that you can point to vs doing this manual operation.

Is this approach valid or there are other approaches? Your email address will not be published. This site uses Akismet to reduce spam.

Learn how your comment data is processed. Process Capability This is a long article, but I thought it was important to keep Cp and Cpk together. Before We Begin!

Cp Cpk vs Pp Ppk. That was poorly centered! Capability Index. Unlock Additional Members-only Content! To unlock additional content, please upgrade now to a full membership. Upgrade to a Full Membership If you are a member, you can log in here. Now check your email to confirm your subscription.

There was an error submitting your subscription. Please try again. First Name. Email Address. We use this field to detect spam bots. If you fill this in, you will be marked as a spammer. Ted Hessing. Comments Hi, if I have a set of data where the subgroup size is different, how should I determine which d2 value to be used for the Cpk calculation? How was out of spec percentage 2. Yes, Parag. See the notes that Joanna Han left above.

How can I help? No idea what you are asking for. What is the value of Cpk for six sigma process. Read the article. What do you think, Prabin?

Yes, I can. What have you tried so far? Hi Chandana, What do you think would be appropriate and why? Where do you see that? Thank you so much. Do they help? Best, Ted. Thank you. Can you elaborate? Can you add a bit more detail? Hi, Sorry for my unclear question before. Thanks for the comments.

The sample size was homogeneous. The population will slightly change every time. The testing method will be the same each time. However, I will consider the other sources for my future analysis plan Thanks again. Then start applying your substitutions. Begin with the given, i. From there, see how to elegantly interconnect the Z score in the Cpk formula. Dear Sir, I wish to sign up to your newsletter. Thanks a lot! Alex, what have you tried here? I have the same question when preparing for ICBB — all solutions do not make sense.

Which is it? Sabarish, can you show your calculation here? All dimensions are in mm. Hi Ted: I appreciate that you continue share the six sigma information to me. Like it or not, process capability calculations are here to stay.

So, this month, we provide you with a visual method of seeing how process capability changes as the average, standard deviation and specifications change.

You can use the interactive workbook to help you see these changes or to help explain the impact of these changes to others. Hope you enjoy the interactive workbook. You may download a pdf copy of this publication at this link.

We will briefly review two process capability indices here: Cp and Cpk. Remember, the process capability indices calculations require that your process be in statistical control and that the individual measurements are normally distributed.

Note that s is not the calculated standard deviation. The visual presentation of Cp is shown in Figure 1. However, a process can be capable of meeting specifications but not be meeting specifications if the process is not centered relative to the specifications. Cp values are not the best indicators of process capability.

The value of Cp does not take into account where the process is centered. In addition, Cp values can't be calculated for one-sided specifications. A better measure of process capability is Cpk. Cpk takes into account where the process is centered. The value of Cpk is the minimum of two process capability indices. One process capability is Cpu, which is the process capability based on the upper specification limit.

The other is Cpl, which is the process capability based on the lower specification limit. Algebraically, Cpk is defined as:. Figure 2 shows how the Cpk values are calculated.

For the area below the average, it can be seen that the Cpl is simply the ratio of the process average minus LSL to 3 s. If that ratio is greater than one, the LSL is more than 3 s from the average. Likewise, Cpu is simply the ratio of USL minus the process average to 3 s.

If that ratio is greater than one, the USL is more than 3 s from the average. Cpk is the minimum of Cpu and Cpl. So, if Cpk is greater than 1, then no product is being produced out of specification on the high or low side. This is why more and more customers are demanding higher Cpk values, e. If there is only one specification, the value of Cpk is either Cpu or Cpl, whichever is appropriate for the specification. This section looks at the impact of decreasing the variation the standard deviation of your process on Cp, Cpk, sigma level and ppm out of specification.

The results are from the interactive workbook available for download at the end of this publication. In the workbook, you can enter an average, standard deviation and specification limits. For simplicity, we will start with the following:. When you enter these into the workbook, the chart below is updated to reflect the values entered. There are four normal curves on the chart. The average and specification limits are the same for each of the curves.

What changes is the standard deviation. The first normal curve blue has the standard deviation entered, in this case 1. Four values are also given for each curve: Cp, Cpk, sigma level and ppm out of spec.

Cp and Cpk are calculated using the formula above. Sigma level is simply equal to 3Cpk. You can see the formulas used by examining the worksheets in the interactive workbook. What does Figure 3 show us? Whenever the process is centered, Cp will equal Cpk. This means that the specification limits are three standard deviations away from the average. Here, we will ignore the Six Sigma practice of allowing a 1.

The total out of specification for this curve is about ppm. The standard deviation in the second curve has decreased from 1 to 0. What has happened to the curve? It is narrower since the standard deviation is smaller. The sigma level is now 4 — the specification limits are now four standard deviations away from the average. The out of specification has decreased from to about 63 ppm. These are the factors that are generally regarded as causing variation in capability measurements:.

Centring value for target value. This is the distance from the target value T to the mean value of the machine or process spread the hump on the normal distribution curve , expressed as a percentage of the tolerance width see Fig. Normal distribution curve. Also called the bell curve because of its shape, this is the pattern in which measurement readings are distributed in most cases as a result of random variations about the mean value the highest point on the hump, see Fig.

Note that most of the readings are grouped near the hump; the farther out toward the edges, the fewer the readings. In other words it is not very likely that you will find any components at all giving widely deviant readings when making normal spot-check measurements.

So it is not enough that the components you happen to measure are all within the tolerances. It takes measurements of a large number of components to determine the size and shape of the bell curve, and that can be time-consuming.

But the standard deviation offers you a shortcut! Read about the standard deviation ». Machine capability Machine capability is measured in Cm and Cmk; it is a snapshot picture that shows how well a machine is performing right now in relation to the tolerance limits. Figure 6 shows some examples. Process capability Process capability is a long-term study, measured in Cp and Cpk, that shows how well a process is performing in relation to the tolerance limits while the study is in progress, as well as indicating likely performance in the immediate future.

You could say that process capability is the sum of a index of machine capabilities measured over a period of time see Fig. When measuring process capability, you must include everything that affects the process, i. Read more about The six factors ». Standard deviation.

One standard deviation This is a statistical function used to calculate the normal distribution curve, for example. The procedure is that you measure the distance from the mean value highest point on the hump to the point where the curve changes direction and starts to swing outward.

This distance constitutes one standard deviation see Fig. This means that you do not need to measure hundreds of components to find out how much the machine or process is varying.

Instead you can calculate the spread using the standard deviation see below. Six standard deviations To calculate the normal distribution spread, you simply multiply the standard deviation by 6 to get the total width of the normal distribution curve.

If you had gone on making measurements you could have plotted the curve, but now you have calculated it instead see Fig. If the diameter of the shaft is on the large side, it leaves less-than-optimum room for lubricant between the shaft and the hole. This results in poorer lubrication, faster wear and shorter lifetime. A smallish diameter, on the other hand, means greater-than-optimum play. The play tends to increase faster, which also shortens lifetime.

The assembly works best at the target value T, which in this case is in the middle of the tolerance range see Fig. For unilateral properties such as run-out, surface smoothness or mechanical strength, the target value is zero see Fig. Statistical process control lets you centre your process on the target value. How are control limits determined?

The correct way is to let the control limits adapt to the process.