X bar and R chart calculation for within part variat
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 This topic has 12 replies, 8 voices, and was last updated 16 years, 9 months ago by ramkumar m.

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November 15, 2005 at 7:55 am #41408
RamkumarParticipant@Ramkumar Include @Ramkumar in your post and this person will
be notified via email.I have a typical problem of within part variation and need to monitor the process using x bar and r chart.
example design spec is 10.250 mm +/0.015. This is measurement of one surface relative to other and the variation is primarily due to flatness. Observed dimension in the part is 10.238 to 10.254 mm.
What is the data point i should use for calculating X bar & R chart.
Dim 10.235 and 10.265 both are determintal to part performance and average is misleading. What methodology can I use to calculate.
Please suggest.
regards,
Ram0November 15, 2005 at 11:07 am #129716Just a suggestion : why do you want to use x bar chart. why dont u use individual chart instead if the average as you say is misleading.
0November 15, 2005 at 12:35 pm #129718
Charles H.Participant@CharlesH. Include @CharlesH. in your post and this person will
be notified via email.Seems you are making the classic mistake of imposing the spec (voice of the customer) on the control chart (voice of the process). The control chart has nothing to do with specifications – we use process capability for this comparison..
Best of luck,
Charles H.0November 15, 2005 at 2:05 pm #129721Ramkumar,
If the data is not independent you should not use an Xbar and R chart. Instead, you should consider using the two multivariate control charts provided in Minitab 14. (But you’ll need to perform some preprocessing!)
If you can show the data is independent you could consider using two Xbar and R charts – one for the process mean ( average of all the data) and one for the uniformity. I would not advise you to disregard the process mean even though you’ve stated you’re only interested in the uniformity.
Remember, all the elements in a subgroup should be independent and each element should correpond to a ‘run’ and in the order of manufacture. Do not form a subgroup from measurements taken from one piece!
Good luck!
Andy0November 15, 2005 at 4:07 pm #129725
RamkumarParticipant@Ramkumar Include @Ramkumar in your post and this person will
be notified via email.Thanks Andy. Will try this out. Apprecaite your response.
0November 15, 2005 at 7:42 pm #129736Seems like you have 2 characteristics of interest. Part Average, and part range. A good method of monitoring the process is the use IMR charts on each characteristic.
Chart 1 : IMR for part average
Chart 2: IMR for part range (I have used this in past jobs. TTV, or total thickness variation is a “withinpiece characteristic” that is independent of average part thickness. Different “knobs” control this characteristic).
If you don’t like IMR charts, use rational subgrouping and monitor the same characterisitics.
Part 1, Readings 1, 2, 3 Compute Part Avg, and part range
Part 2, Readings 1, 2, 3 Compute Part Avg, and part range
Part 3, Readings 1, 2, 3 Compute Part Avg, and part range
Part 4, Readings 1, 2, 3 Compute Part Avg, and part range
Part 5, Readings 1, 2, 3 Compute Part Avg, and part range
Now you have 5 averages and 5 ranges for your subgroup.
Do and Xbar/R for part average and an XBar/R for part Range.
Seems confusing at first, but it is effective.
0November 22, 2005 at 6:31 am #130162
R.M.ParkhiParticipant@R.M.Parkhi Include @R.M.Parkhi in your post and this person will
be notified via email.Dear Sir,
I suggest you follow Multi Vari technique as demonstrated in the book ‘World Class Quality & How to Make it Happen by my Guru Mr. Keki Bhote.
I n case you have difficulty in solving the problem,pl. send me the total data. I shall work out the details for you, free of cost.
Regards,
R.M.Parkhi
PresidentR.MParkhi & Associates0November 22, 2005 at 8:15 am #130167
Bill CraigParticipant@BillCraig Include @BillCraig in your post and this person will
be notified via email.Just wondering why he should use multivari if he already knows the major source of variability? Withinpiece variation is a concern. He should be monitoring the variation appropriately and doing DOEs in parallel to identify the key input factors. Just my humble opinion!
0November 22, 2005 at 11:06 am #130172
R.M.ParkhiParticipant@R.M.Parkhi Include @R.M.Parkhi in your post and this person will
be notified via email.Dear Sir,
I totally agree with you; however when you have taken efforts, it is advisable to find out other sources of veriations job to job & temporal veriations, so that we can improve CPk to a higher level; thereby reducing further the requirement of inspecion.
But you are 100 % correct.
With regards,
R.M.Parkhi0November 22, 2005 at 12:27 pm #130178It is amazing how little people understand the basic six sigma tools!
An XBar&R chart represents the voice of the process. Measure the actual dimension in question, subgroup at a scheme and frequency that will gather a representative sample of all the variation in the process (shift to shift, machine to machine, operator to operator etc.) plot the data for a minimum of 2530 samples and see what the process is telling you!
The individual that mentioned the specifications is correct! Specifications are only a consideration after you have monitored the process and determined how this process is performing. When you have this (UCL & LCL) compare it to the spec to see where you are.
Remeber XBar&R charts are robust to distribution so this is not a worry. The XBar chart will show you group to group variation the RBar chart will show you the within group variation.
Do this and see what the nature of your problem is.0November 22, 2005 at 1:57 pm #130187Ron,
I also think it’s amazing how big people understand the basic six sigma tools :)
By the way, UCL and LCL can’t be compared directly with tolerance specifications.
There are other errors in your post as well, but I shall leave that to other median sized individuals.
Regards,
Andy0November 22, 2005 at 2:27 pm #130192
Bill CraigParticipant@BillCraig Include @BillCraig in your post and this person will
be notified via email.R.M.,
I see your point about exploring all sources of variation, even though “withinpiece” has been identified as the most predominant. Many of my variation reduction plans have had differing critical X’s for withinpiece, piecetopiece, etc. variation. Multivari is probably one of the most underutlilized tools in the industry (by people of all sizes, perhaps in Munchkin land too!) :)
We used to preach “Characterize, Optimize, Control” in my early days. Seems like the characterization piece should get a little more focus in this case.
Find out the extent of variation for each source, run DOES to determine critical X’s
Run optimization studies
Control process.
Just my 2 cents!0November 23, 2005 at 2:40 pm #130235
ramkumar mParticipant@ramkumarm Include @ramkumarm in your post and this person will
be notified via email.thanks Mr Parkhi. I’ll try your recommendation. regards.Ram
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