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Applications of MSA Platform Tools in JMP® 17 (2023-EU-PO-1345)

Measurement System Analysis is a methodical approach for identifying and managing the sources of variation that can influence the measurement system. At the top level, this type of analysis enables the quantification of the measurement system variation (MV) present in the Total Observed Variation (TV) of a process or system. It separates it from the Process (part-to-part) Variation (PV). The measurement variation can be broken down further into Precision and Accuracy. For the Precision component, we follow a sequential method for Continuous Data to determine the adequacy of a measurement system. A Type 1 Gauge Study examines the accuracy and consistency of the measurement device, and a Full Gauge R&R Study explores the Repeatability and Reproducibility of the entire measurement system. This poster will demonstrate the practical application of these tools in JMP 17, as well as making reference to the new MSA Design tool to support the initial phase of measurement system analysis (data collection plan).


Attached are the JMP journal and JMP data tables (including reports) presented in the video.


Hope you enjoy !  



Welcome  to  this  post  of  presentation  in  the  applications  of  MSA  platform  tools  in  JMP  17.  Before  we  go  into  it,  I  just  would  like  to  give  a  brief  description  about  Measurement  System  Analysis,  short  for  MSA.  So  when  we  look  at  the  total  observed  variation  in  the  process,  we  use  measurement  system  analysis  to  try  to  identify  and  manage  the  sources  of  variation  that  can  influence  the  measurement  system  being  used.  It's  a  combination  of  measurement  devices,  people,  procedures,  standards,  etc.  So  we  can  decompose  that  total  observed  variation  into  two  other  components,  the  process  component,  or  sometimes  called  part  to  part  variation.  But  what  we're  really  interested  with  measurement  system  analysis  is  in  this  measurement  system  variation  component.

Looking  at  the  measurement  error  that  is  associated  with  this  measurement  system  variation,  that  can  be  broken  down  into  other  components,  precision  and  accuracy.  For  the  purpose  of  this  poster,  I  will  concentrate  on  the  tools  that  enable  us  to  identify  sources  of  variation  within  precision  and  specifically  repeatability  and  a  little  bit  about  bias  component  under  the  accuracy  component.

What  we  tend  to  do  when  looking  at  measurement  system  analysis,  there  are  several  methods  involved,  normally  and  particularly  for  continuous  data.  We  start  by  examining  the  accuracy  and  consistency  of  the  measurement  device  alone  using  a  technique  called   type 1 gage study.  This  is  sometimes  also  named  as  analyzing  the  pure  repeatability  of  the  system.  So  we  have  one  single  part,  one  single  device  if  the  measurement  system  requires  a  manual  intervention,  we  can  have  one  operator.  But  the  idea  here  is  that  we  start  evaluating  the  pure  repeatability  of  the  system  before  going  into  more  complex  analysis  where  other  sources  of  variation  may  be  part  of  the  measurement  system,  which  essentially  is  the  second  step  around  what  is  called   Full Gage R&R,  which  examines  both  repeatability  and  reproducibility.

Last  but  not  least,  we  have  continuous  gage  linearity  and  bias  study,  but  it's  not  going  to  be  covered  specifically  in  this  poster.  So  let's  have  a  look  of  what  this  means  in  terms  of  JMP  17.  In  the  new  version  of  JMP,  we  do  have  a  new  MSA  method,  type  1  gage  study,  that  essentially  is  going  to  help  us  identify  that  initial  phase  of  the  analysis  with  regards  to  the  pure  repeatability  of  the  system.  So  I'll  show  you  a  quick  example  of  that  of  an  output  report  with  the  Type  1  Gage  R&R.  And  what  you  can  see  here  is  that  by  default,  the  report  shows  a  run  chart.  So  this  looks  at  30  repeats  of  the  same  part  using  the  same  device  or  equipment  in  order,  so  this  timeline  really  helps  us  identify  any  special  situation,  any  special  measurements  that  didn't  work  very  well.

There  is  a  reference  that  we're  on  the  nominal.  If  you're  using  a  reference  part,  for  example,  we  can  definitely  identify  whether  the  average  of  those  measurements  are  in  line  with  the  reference  part.  That  mean  value  can  be  added  to  the  graph  if  we  want  to.  As  you  can  see,  it's  going  to  be  on  top  of  it.  But  if  I  remove  the  reference  line,  then  you  can  see  average  and  the  reference  are  very  similar  for  this  example.

We  also  want  to  have  a  look  at  this   Type 1 Gage R&R study  and  have  a  reference  around  20 %  of  our  tolerance.  What  in  the  type  1  gage  study  we're  doing,  we  are  limiting  the  analysis  to  only  20  %  of  the  total  tolerance  in  order  to  assess  whether  the  pure  repeatability  is  acceptable  or  not.  But  this  specification,  if  you  will,  for  the  type  1  can  be  all  consented  in  the  settings  of  this  tool.

It  provides  some  summary  and  capability  statistics,  so  the  normal  reference  location  and  spread  references,  particularly  when  it  comes  to  six  standard  deviations,  the  number  of  measurements  taken  and  the  tolerance.  T hen  here  are  the  two  limits  above  reference  on  the  graph  for  the  20 %  of  the  tolerance,  so  plus  or  minus  10  %  of  that  tolerance.

If  you  use  to  the  process  capability  indexes,  what  you  will  see  now  for  capability  of  the  gage,  CG  and  CGK,  they  are  exactly  the  same.  The  biggest  difference  here  is  that  obviously  we're  looking  at  the  capability  of  the  gage  and  assessing  this  variation  with  regards  to,  in  this  case,  the  20 %  of  the  tolerance,  but  suddenly  we  can  have  a  look  at  both  the  variation  relative  to  those  spec  limits  and  also  variation  and  location  for  CGK.  So  this  gives  us  a  summary  of  some  metrics  to  evaluate  the   Type 1 Gage R&R study  results.  There are  some  percentage  being  calculated  as  well  in  terms  of  percentage  of  variation  with  regards  to  repeatability,  but  obviously,  if  you're  using  a  reference  part  that  you  already  know  its  nominal  value,  we  can  evaluate  not  only  the  pure  repeatability  of  the  system  in  this  case,  but  also  the  bias.  So  the  difference  between  the  reference  value  and  the  nominal  value.

So that  can  be  actually  added  as  an  additional  test  if  we  want  to,  so  this  is  really  looking  at  the  hypothesis  testing  case  in  terms  of  if  the  bias  is  equal  to  zero  so  either  the  average  and  the  reference  value  are  the  same  or  very  close  to  each  other,  and  as  you  can  see,  the  reported  P  value  there,  in  this  case,  statistically,  there  is  no  significant  difference  between  average  and  reference  value.

Another  useful  visualization  within  this  tool  is  the  history  realm,  so  we  can  have  a  look  at  the  distribution  of  the  values  of  those  measurements  taken,  in  this  case,  those  30  measurements  for  reference,  that  can  be  customized  in  your  report  as  we  go  along.  So  very  quickly,  we  just  have  a  great  tool  to  initialize  our  measurement  system  analysis  process  by  now  having  what  is  called  a   Type 1 Gage R&R Study  as  part  of  the  MSA  platform  in  JMP.

What  we  sometimes  do  is  we  also  have  a  second  step  before  we  go  into  what  is  called  a   Full Gage R&R  assessment,  both  repeatability  and   [inaudible 00:07:43] ,  which  can  be  called  a  Type  2  Study.  We  have  an  example  here  of  that,  so  the  only  difference  here  is  the  fact  that  in  between  the  30  measurements,  we've  removed  the  part  from  a  potential  holding  fixture  in  between  each  measurement.  As  you  would  expect,  by  doing  that  intermediate  step  in  between  all  the  measurements,  then  we  expect  to  have  more  variation,  and  this  is  what  we  can  see  here  now.  So  not  only  we  have  more  variation,  where  you  can  also  see  that  the  average  value  of  the  readings  are  also  much  lower  than  its  target  location.

By  turning  on  the  bias  test,  we  will  be  able  to  see  that  now  the  P  value  when  compared  to  the  Type  1  study  where  we  didn't  remove  the  part  in  between  measurements,  the  part  was  fixed  and  just  measured  30  times  consecutively,  we  now  have  a  low  P  value  showing  that  there  is  significant  difference  between  the  reference  value  and  the  average. T his  can  be  built  into  several  increment  and  sources  of  variation  even  before  we  start  adding  multiple  parts  and  operators  or  equipment  into  this  analysis.

But  if  we  do,  JMP  already  had  a  gage  on  our  study  tool  in  previous  versions.  This  is  just  a  quick  example,  what  that  means  in  terms  of  variability  of  the  gage, a nd  in  this  case,  we  use  the  gage  on  our  method  involved.  So  if  you  go  to  Analyze,  Quality  and  Process,  there's  an  updated  version  for  the  Measurement  System  Analysis.  In  the  MSA  method,  we  can  see  now  we  have  the  Type  1  Gage  study  that  I've  used  for  both  Type  1  and  Type  2.  The  only  difference  there  is  that  on  the  report,  the  output  report  for  the  Type  2,  I  just  edited  the  title  and  called  it  Type  2  just  to  differentiate  between  the  two  output  reports.  But  what  we're  seeing  here  for  the   Full Gage R&R  or  the  variability  analysis,  I've  used  the  Gage  R&R  method,  and  this  is  where  we  can  also  decide  the  type  of  model  used,  normally  used  as  cross,  so  we  can  see  all  the  effects  crossed  with  each  other  in  the  analysis,  as  well  as  some  additional  options  are  also  available.

But  in  this  report,  I'm  customizing,  in  this  case,  I've  added  some  specification  limits.  Here,  essentially,  we're  not  looking  at  the  reproducibility  of  the  system.  It's  just  another  increment  we  evaluate  repeatability  in  this  case.  We  are  using  not  one,  but  10  parts  in  this  case  and  evaluating  that  variation  for  five  repeats  of  each  part.  As  you  can  see,  in  the  Gage  R&R  report  and  table,  the  reproducibility  component  is  zero  because  we  don't  have  additional  equipment  or  operators  being  evaluated  in  this  analysis,  so  all  the  variation  in  this  study  is  due  to  repeatability.  So  this  is  the  traditional  output  that  you  would  get  from  the  Gage  R&R  method  inside  of  JMP  for  reference.

To  finalize  the  new  tools  involved  in  JMP  17  for  the MSA  platform,  what  I  would  like  to  highlight  as  well  is  that  as  part  of  the  planning  phase  for  any  Gage  R&R  study,  it's  important  to  understand  what  is  the  method  utilized,  of  course,  but  also  what  will  be  a  good  method  of  data  collection.  A s  part  of  JMP,  we  now  have  as  part  of  DOE  special  purpose,  a  new  tool  called  MSA  Design,  and  this  enables  adding  factors  like  parts  and  operators.  If  I  quickly  show  adding  three  factors  there,  I  can  identify  what  is  the  MSA  role  involved  in  each  one  of  them,  for  example.  This  is  a  great  opportunity  during  the  planning  phase  to  start  to  come  up  with  the  design  that  will  help  you  doing  the  data  collection  even  before  any  analysis  is  done.

For  more  information  about  how  to  utilize  the  MSA  design  feature,  you  can  follow  this  link,  which  will  take  you  to  the  JMP  user  community  video  where  Hyde  Miller,  JMP  systems  engineer,  has  provided  more  information  about  this  tool.

Hope  this  was  useful  for  you.  Thank  you  very  much.