iSixSigma

X bar and R chart calculation for within part variat

Six Sigma – iSixSigma Forums Old Forums General X bar and R chart calculation for within part variat

Viewing 13 posts - 1 through 13 (of 13 total)
  • Author
    Posts
  • #41408

    Ramkumar
    Participant

    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.
    Β 
    regards,
    Ram

    0
    #129716

    Whitehurst
    Participant

    Just 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.

    0
    #129718

    Charles H.
    Participant

    Seems you are making the classic mistake of imposing the spec (voice of the customer) on the control chart (voice of the process).Β  The control chart has nothing to do with specifications – we use process capability for this comparison..Β 
    Best of luck,
    Charles H.

    0
    #129721

    Ramkumar,
    If the data is not independent you should not use an X-bar 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 X-bar 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!
    Andy

    0
    #129725

    Ramkumar
    Participant

    Thanks Andy. Will try this out. Apprecaite your response.

    0
    #129736

    Craig
    Participant

    Seems like you have 2 characteristics of interest. Part Average, and part range.Β  A good method of monitoring the process is the use IMR charts on each characteristic.Β 
    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 “within-piece 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, 3Β Β Β  Compute Part Avg, and part range
    Part 2, Readings 1, 2, 3Β Β Β  Compute Part Avg, and part range
    Part 3, Readings 1, 2, 3Β Β Β  Compute Part Avg, and part range
    Part 4, Readings 1, 2, 3Β Β Β  Compute Part Avg, and part range
    Part 5, Readings 1, 2, 3Β Β  Compute Part Avg, and part range
    Now you have 5 averages and 5 ranges for your subgroup.
    Do and X-bar/R for part average and an XBar/R for part Range.
    Seems confusing at first, but it is effective.Β 
    Β 
    Β 

    0
    #130162

    R.M.Parkhi
    Participant

    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
    President-R.MParkhi & Associates

    0
    #130167

    Bill Craig
    Participant

    Just wondering why he should use multi-vari if he already knows the major source of variability? Within-piece 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!

    0
    #130172

    R.M.Parkhi
    Participant

    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 areΒ  100 % correct.
    With regards,
    R.M.Parkhi

    0
    #130178

    Ron
    Member

    It 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, sub-group 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 25-30 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.

    0
    #130187

    Ron,
    I also think it’s amazing how big people understand the basic six sigma tools :-)
    By the way, UCL and LCL can’t be comparedΒ directly with tolerance specifications.
    There are other errors in your post as well, but I shall leave that to other median sized individuals.
    Regards,
    Andy

    0
    #130192

    Bill Craig
    Participant

    R.M.,
    I see your point about exploring all sources of variation, even though “within-piece” has been identified as the most predominant. Many of my variation reduction plans have had differing critical X’s for within-piece, piece-to-piece, etc. variation. Multi-vari is probably one of the most under-utlilized 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!

    0
    #130235

    ramkumar m
    Participant

    thanks Mr Parkhi. I’ll try your recommendation. regards.Ram

    0
Viewing 13 posts - 1 through 13 (of 13 total)

The forum ‘General’ is closed to new topics and replies.