I 'm a technical director in Henkel Corporation .
I 've been using JMP for more than five years in product development .
Today I 'm happy to share
with our best practice in utilizing JMP data analytics
in product development of adhesive and sealants .
My presentation has two parts .
The part one is application gallery
where JMP used in various stage in product development will be discussed,
and in part two I will focus on using JMP
as a formulation worksheet with the demonstration .
In my organization ,
folks either have not heard of JMP
or have an impression that JMP is an advanced DOE software .
For the latter, it is true , but it 's not the whole truth .
Since there are other DOE software options ,
it is hard to persuade folks
to switch away from tool they are familiar with .
JMP is an excellent all- around data analytics tool .
To promote JMP adoption ,
we need example to demonstrate its power in the data analytics .
In my presentation ,
the example are taken from my experience in adhesive and sealants .
About Henkel .
Henkel is a 22- billion enterprise with two business unit :
adhesive technology
and consumer brand of laundry , home, and beauty care .
I 'm in the adhesive technology unit .
We are global adhesive leader serving 800 industries
with more than 20 ,000 products .
Let 's start with case number 1 .
One customer has a need ,
they may call a service center asking for product recommendation .
For example , a customer may need a room temperature adhesive hook
with the shear strength between 1 ,500 -3 ,000
and a work life between 15 -30 minutes
and in the package of 10 .
What we need is a searchable product selected guide ,
what is showing in our case 1 .
Once the product information are entered into the JMP table ,
one can use the tabulate and local data filter
to downselect the product
based on the customer requirement as shown in here .
This is a very, very powerful tool in the preformation stage .
My team has been applying this tool to search for formulation ,
pattern , literature and the raw material specifications .
In case number 2 ,
multivariate analysis is applied to a silver filler ,
which are used in making conductive adhesive .
From multivariable analysis ,
if we focus on the surface area ,
it has a moderate negative correlation with the tap density
and then it has a stronger positive correlation to lubricant amount
as measure at the weight loss at 530 AC .
There is a graphical way here and it 's a table format here .
With this analogy ,
chemistry can select the right silver package for the application .
Case 3 is the literature .
Literature is a great place for learning the chemistry and formulation
and this particular cited literature
illustrates how epoxy tensile strength
are affected by the mixture of two amine hardener .
The results are described qualitatively by a table
and a graph of the stress train curve here .
For formulator quantitative description is good ,
but learning via quantitative modeling
is even better for the prediction purpose .
In case number 3 ,
we take the literature data
and then create a two -factor column right here .
These are the epoxy /amine stoichiometry , or we call it the index .
The other factor is the fraction
of one of the amine PAE to the total amine fraction .
With the Fit Model platform, and we use the ISM model ,
it showed that the quadratic effect of the index,
together with two main effects, are all significant .
The prediction provider and the contour profiler
are used to quantify the learning and give the prediction .
In case number 4 ,
my group was assigned to support a technology platform
that include about 30 products .
Since we are not involved in the original formulation development ,
how to study the formulation family in this case is not trivial .
Looking at a big Excel table
with all the formulation certainly is not effective either .
Case number 4 is the example of addressing this challenge .
I select the three top most used ingredient
in these 29 formulation :
monomer 1 , 2, and then oligomer .
Then by using the hierarchical clustering analysis right here ,
right here our formula was identified to have very , very small distance
assumed right here ,
implying that they are in cross related .
Actually they are only different
in the photo initiator for different wavelength in this case .
We can add more ingredients one by one in this hierarchical clustering
and then learn the formulation family by using this method .
Most of the chemist analyze the formulation performance in Excel .
Case number 5 is a JMP tabulate that has the same data format as Excel .
Basically , various information of a formula
are displaced in the same column .
Like what you see here has a heading , has a recipe ,
has a processing material characteristic, and all the results .
To create such data structure ,
we need to enter data in JMP table in a special format
and that will be discussed in detail in part two of my presentation .
Case 6 is a silicone study involved 23 formulations
and more than 10 measurement property .
It is quite overwhelming trying to analyze the raw data in such big system .
We first construct a series of graph
with a property retention in Y and then the initial property in X .
For example , if you look at the first graph here ,
the adhesion retention is in Y and initial adhesions in X .
We also give the reference line , acceptance line for each axis .
When a formulation is selected , for example , I choose this point ,
they are in the quadrant with acceptable initial adhesion and retention ,
then all its associate property such as the tensile strength ,
elongation , hardness , all show up ,
also formulation all show up at the same time ,
these are all thanks to automatic highlight .
This is all thanks to JMP dynamic link capability .
Visualization analysis in such a way is very effective
for chemists to know the overall behavior of this system .
In polymer science ,
we measure the modulus of polymer as function of the temperature
with an instrument called dynamic mechanical analyzer , DMA .
DMA data has a temperature modulus
and then attend delta are typically transferred to Excel
in a wide format for plotting .
To overlay several DMA curve for comparison it is durable in Excel ,
but it 's not a trivial effort .
In case number 7 , we stack 74 DMA results together
and by using the graph builder ,
we can compare DMA results very , very quickly just by clicking .
I cannot imagine doing the same in Excel that has a 222 column .
It 's basically 74 sample times 3 signal per sample .
It 's going to be very difficult to handle in Excel environment .
A graph builder is excellent in turning
a very complicated graph seen in Excel into a visually digestible analysis .
In case number 8 , the needle pull strength
is illustrated in graph builder using four variable .
We have eight adhesive on the top ,
we have three different radiation system on the Y here ,
and we also have a four radiation time
and then two needle hub combination .
See how easy it is to understand this JMP graph
as compared to the Excel graph right here .
Good .
In case number 9 ,
we are conducting accelerated aging study for four epoxy prototype formulation
by measuring their initial adhesion
on three substrate and with a three replica .
The aging condition are two different temperature
and then eight week aging time with two weeks testing interval .
This aging design and the data was initially recorded in Excel
and we converted the Excel data into JMP table with seven column ,
seven column only
and the stack format and then we make a graph .
You will agree that the visualization in JMP graph builder in this case
is much easier to see the aging performance
than looking at the busy Excel table here .
In formulation stage , we frequently need to optimize composition .
Case number 10 is example
where two catalysts in polyurethane are optimized with the DOE design .
The factor are : catalyst ratio and the catalyst total amount .
There is a 10 -run face -center central composite design ,
the predictor provider indicating that the catalyst total amount factor
has a long linear effect on the work life .
The white area in the contour profiler
is actually the suite design space which desire work life .
In this case , 20 -28 .
It is important for chemistry to select
this green highlighted white area for better production robustness
than the area [inaudible 00:12:10] in blue ,
it has a higher tolerance for the amount change there
just in case operator makes some minor mistake .
That 's why it has a better production robustness .
We routinely see chemistry perform
statistics analysis of adhesion data like what you see here ,
but we hardly see anybody presenting
the results about the failure mode analysis .
In JMP the failure mode analysis can be performed in two places .
One is in the contingency analysis in Y by X platform
and the second one is using the graph builder .
The case number 11 is the example
applying to the silicone sealant
where the failure mode change before and after high temperature aging ,
before and after is clearly shown .
Clearly shown ,
Beside a good adhesion ,
adhesive rheology will need to be formulated
so that it can be effectively applied to the substrate .
We have a project to develop a seam sealant to protect the metal joint
by spraying process .
Case number 12 is the example that illustrating the complication
of spraying process with respect to the sealant viscosity variation .
We have three spraying variable .
They are the pressure , nozzle type , and then the head travel speed
and then we have one material factor in this case is the viscosity .
Initially , we plan the experiment in factorial DOE fashion ,
but one of the factor ,
the sparing pressure are very very hard to control .
We end up performing regression of the 40 round with ISM model
using the strain width and strain thickness as the response .
We get a good model with R -squared about 0 .91 for width
and 0 .81 for the thickness .
The modeling result tell us that the spraying condition
will need to be adjusted dependent on the sealant viscosity .
This is illustrated in this prediction profile here .
Each processing parameter
has their own machine limit and also desirable operation limit .
If this predicted processing variable are outside those limits ,
then the chemist will need to redesign
formulation reality and making sure that manufacturing
has the processing capability to meet the viscosity requirement .
This example show that the formulation design
and application constraint will need to be considered side by side
and JMP is actually a very good tool in facilitating this type of study .
Case number 13 is example that JMP
is used to handle huge instrumentation data sets .
In testing thermal interface material ,
the temperature at a different location and the power consumption data
are collected and then uploaded to the JMP .
Once the data are in JMP table ,
visualization of the data and data analysis of data set
as much as 500 ,000 row
are still very manageable and has a fast response .
That means the geometry actually can be used to handle instrumentation data .
We have a project to apply adhesive to software by the sensor printing
and this application is challenging with pinhole defect issue .
Process engineer changed six processing variable randomly
and then collect 21 wrong results .
His data analysis did not reveal any special trend ,
so the JMP was then used for the troubleshooting in this case
and the prediction partition analysis has identified factor F
as the key factor .
Later on we apply the predictor screening analysis
and then identify additional factor D that need a further investigation .
For JMP training , we learned that the predictor screening
can identify predictor , they may be weak alone ,
but strong when they are used in combination with other predictor .
In the scaling up and the manufacturing stage production ,
when the batch run into the issue ,
the raw material lot -to -lot analysis is one of the troubleshooting item
in order to isolate the potential raw material effect .
This exercise is typically done in the Excel table .
But when the multiple raw material
and multiple lots of each raw material are involved ,
it is difficult to look at a huge Excel table
to analyze the raw material effect .
In case number 15 , a polyester formulation
with three raw material
and about 45 separate lots are plotted verses the date of manufacturing
with the color scale of the gel time .
This heat map plot provide a visual analysis
for the production engineer to determine
whether a particular loss of raw material
is the major cause of the out of spec batch .
We turn the Excel table into a visual way for better analysis .
Statistics comparison in T -test or ANOVA analysis
are performed routinely in the product development .
A product benchmark exercise typically involves
multiple product running under various testing protocol ,
aiming to have a very comprehensive the product comparison learning here .
Case 16 is an example of statistic analysis
involved large combination of 23 products
and then more than 10 testing protocol .
In JMP , a large volume statistics analysis is not a challenge
since creating of the sub -table is not required in this case ,
as compared to other software .
One can utilize the column switchers
and the local data filter to create all the combination of property
and adhesive for statistics analysis .
Plus the results of each analysis
can be copied into a JMP journal to streamline the reporting .
For case number 17 ,
the needle bonding testing of light cured , historically , have a high data variance .
Case 17 use JMP to summarize 18 reports of needle -bound testing
which involve multiple lots of adhesive ,
and those are tested in various time .
The needle pore strand , its the COV , are plotted in graph builder
under various lighting , radiation condition ,
as well as the substrate combination .
With the local data filter here , one can easily
change the criteria selection to have a comprehensive comparison
of this adhesive and their consistency performance .
When this result was presented , everyone was amazed with the JMP capability .
It is so versatile and so powerful .
This is the last case for the application gallery .
In this case , number 18 ,
we use the parallel plot feature in the graph builder to demonstrate
visual comparison of 15 performance items and 10 adhesives .
Each performance has its own unit and scale
which provide a visual comparison more quantitatively in contrast
to the qualitatively comparison in spider chart which is used in Excel .
So far , in the 18 application gallery examples ,
the data are coming from literature , instrumentation ,
processing , and not much emphasis on formulated .
Now we will switch gear to discuss formulation creation ,
use worksheets , and it 's a JMP -based worksheet ,
not a traditional one using Excel .
Before we show you the JMP worksheet , let 's discuss about adhesive type .
Broadly speaking , adhesive can be divided in two categories :
one component adhesives or the two component adhesives ,
or 1K or 2K .
A 1K system like the Super Glue everybody knows
require no mixing and it can be cured
by moisture , by light , by heat , or by other method .
In case we are dealing with one component but heat cure adhesive such as epoxy ,
then we will need to design and then calculate the stoichiometry
or the index to balance the proportion of the epoxy to the amine hardener .
Then for the two component system , 2K system ,
their mixture will react at NDM temperature
so that they are kept apart before use .
In a 2K system , their stoichiometry will need to be designed and calculated
based upon the desired mixing ratio , either by weight or by volume .
There are some formulation calculation here we need to perform .
This type of calculation design historically been done in Excel .
This is the Excel .
Everybody know that Excel spreadsheet allow mixed data type in the same column
and its formulas can be applied to individual sales level
that make it very flexible as a formulation calculation worksheet .
Formula are typically organized in column format like this .
Each column has a full group of formulation information
such as their heading , which is the ID , their recipe ingredient ,
the formulation characteristic or processing parameter ,
and followed by the result .
What about the result ?
Excel -based worksheet is very useful .
Everybody using that because it 's easy to learn ,
but it does come with some shortcoming
such as first of all the row matching .
When you have a new ingredient or new testing results ,
you need to match to the right row , and they take time .
Then one may need to hide or unhide a column for comparison .
Then third thing is it 's harder to analyze the data
when the results are put in different tab .
It 's a tab -to -tab format .
It 's also very difficult to make a graph in such kind of a data structure .
JMP offer webinars to go beyond the Excel spreadsheet
in various features as listed here .
But the worksheet calculation is not emphasized .
Perhaps this is due to the inherent data structure that each column
cannot have a mixed data type
and the column formulas is applied to the entire column
which is not as versatile or flexible as compared to the Excel .
Despite of these constraints ,
we have developed JMP worksheet with the following objectives in mind .
It should have a broader capability
for formulation design , calculation , recording , and analysis .
It is all in one and we want to minimize cross -platform copy -pasting .
It should be easy to operate ,
easy data entry and use the JSL for a lot of the automation .
Then the final data set is ready for machine learning exercise .
Let 's look at our Gen1 , and that is for one component system .
This includes four data group .
We have a formulation ID , we have a recipe ,
we also have a material processing characteristic ,
and then we have a testing result right there .
The four data group are the same as what you see in the earlier Excel worksheet ,
but layer structure was organized in the column from the left to right .
This is different from the Excel which is from top to the bottom .
The data of the three group , 2 , 3 , and 4
are shared and recorded in the same column ,
which has a numeric data type .
All the recipe , all the testing results , and all the formulation characteristics
all in the numerical data type ,
and they are documented in the same column here .
With this kind of a format …
The data was also stacked together .
I have formulation 1 here , formulation 2 here .
With a stacking format , one can freely enter the new ingredient
or new testing item without needed to match the role as needed in Excel .
JSL was also created to enable data analysis
in either tabular way or in a graph format .
This is in a tabular way .
Chemist can pick several formulation ID and compare their recipe characteristic
and performance in a very , very condensed format here .
This is very different from Excel without needing to hide /unhide columns
to bring formulation to be adjacent to each other .
Much , much easier under the JMP format here .
Besides tabulation , one can make a graph
of the property versus the property comments or the sample ID ,
but not the ingredient percentage .
This graph can be combined with the recipe table here
into a group under the dashboard operation .
This make it as a very effective visualization analysis .
As for testing involves multiple replicates .
We typically just record the average result .
But one can enter the individual replicate data in the property column ,
and then perform the T test , the all -over test , using this worksheet here .
In case people doesn 't want to enter data in this way ,
there is the other way to virtually link the data file with the replication result
with the worksheet .
That will be shown later in the presentation .
So far , what you see is our Gen1 worksheet
which involves no formulation calculation .
Chemists in my group has been using this tool for more than one year .
They get used it its easy data entry and very , very powerful tabulation analysis .
Next we 're going to look at the Gen 2 worksheet
that can overtake the Gen1 feature .
It has an additional feature
for the formulation calculation for the 1k and 2k system .
This worksheet also link with the other JMP file
that has additional raw material information needed for calculation .
We have the other worksheets , we call Gen 3 , that are designed
to deal with the solvent borne system .
It also allow formulator to incorporate master batches ,
but due to the time constraint it will not be discussed here .
This is our Gen2 worksheet .
There are three sections .
We have a heading and then the formulation input section right here .
The middle one , we have a calculation output .
The third section is the processing material characteristic
and also the testing results .
Section 1 and section 3 are like the one in Gen1 ,
but the section 2 here is newly added .
The column row name is used to link the reference file
that has additional data information needed for calculation .
You can see the symbol for the virtual link right here .
After chemist enter the formulation ID ,
they will specify for columns , parts , row , name , and initial weight .
If they are doing the 2K system , they need to also specify the mixing ratio
either by index , by volume , or by weight ratio .
Then the worksheet will output the mixing ratio characteristic here
again by index , by volume , or by weight .
They also provide a normalized composition , either by part .
By part means A and B sum up together by themselves and equal to 100 ,
or A and B mixed together .
We call it normalized by total here .
After seeing this one and the chemist can perform the experiment
and then come back to enter the results right here .
The other thing is
in the property material characteristic , we have the other column called Lookup .
This can extract the information from the calculation
and also the raw material fraction percentage ratio
and automatically displays right here .
Then chemists just need to copy parameter in the value enter column
and then this will be automatically
transferred to the two normalized percentage column for display purpose .
We also have three JSL there to facilitate in analysis .
The first one is showing you normalization , normalized by total .
That means A and B being mixed together and sum up to 100 .
Here , I showed you the formula ,
showed you the characteristic and showed you the result .
You have a second JSL that 's normalized by part .
In this case , you can see your part A formulation and part B formulation ,
and then A and B all have been normalized to 100 by themselves .
With the other JSL , we can change the formulation
worksheet format from the stacked to the white format .
In this case their ID performance , individual ingredient ,
and then the characteristic will all have their own individual columns .
With this format , one can make the graph
with the property versus the ingredient percentage
which cannot be done under the stack format .
One can also looking for the correlation
between the performance or the performance with the formulation characteristic .
At this moment , I like to show you the live demonstration .
This is the formulation worksheet I just showed you in the PowerPoint .
Basically , we have the heading .
Then we have a formulation input section .
We have a calculation between n1 and n2 .
Anything here is for calculation .
Then we have the last section here ,
that is a performance and then the property material characteristic .
I mentioned that we have a JSL ,
allow people to look at this result easily .
Let 's look at this one , JSL by total .
We can easily highlight any formulation or compare 2 and 8 ,
and then compare their formulation and their result .
These are mixed together .
We can look at it by part .
Part A here and then part B here .
They all sum up to a hundred by themselves .
Easily , we can compare …
Oh no , I need to remove this one first .
I can compare formulation easily by manipulating the local data filter .
Again with the JSL , we click the Join All .
We are turning the stack format into a wider format .
Each row belong to one formulation with the heading here ,
with their property , with their formulation ,
and with their formulation characteristics showing right here .
For machine learning ,
we can highlight a role ingredient and then just
manually add zero so that each ingredient has zero or whatever ,
and then now we can do this one .
We can create a summation or something , easy to operate in this .
I 'm going to show you next how this one work in the sense that
assuming that we 're going to create a formulation .
I 'm going to copy the heading .
Sorry , I 'm going to delete everything here because I create this one already before .
I 'm going to delete the demonstration one .
I 'm going to create it from scratch
by copying the heading here .
I change the name to Demonstration here .
I will copy the formulation because
I 'm going to modify formulation from this one , the DOE 8 .
Then the DOE 8 is based on one -to -one mixing ratio by volume .
But in this new one , we could change it to one -to -two mixing .
A divided by B is one divided by two , so it will be 0 .5 .
Then I copy the heading including the mixing ratio all the way down .
Now all the calculation has been done here .
With this weight percentage I 'm entering , it showed that the material has an index
model ratio A to B to be 0 .65 , which is too low .
We need to , using our chemistry knowledge , to turn this around .
In this case , for example , I make this one 2 .
I can easily make this one into 1 .05 .
That is the range I 'm looking for .
Basically , assuming it is the design that we want , formulation we want ,
the next thing we want to do is to copy
some of the testing that we already had before ,
that we are monitoring before ,
but without the results , of course .
We have a new result here , so I 'm going to delete that one .
But we also want to add additional property
which for example is viscosity
measure at a room temperature .
With this section here , then we want to extend our heading
to specify those are belong to this formulation .
As soon as I specify the heading ,
the Lookup automatically give me
the information such as the missing characteristic .
1 .5 or 0 .5 , they are automatically copied to here through the Lookup function
and then the feeder loading in the formulation normalized to Total
while also being extracted , sum up together and put it right here .
Now I can copy this information , put them in value enter ,
and specify my mixer is number 2 ,
and then start to enter my results , time that 's going to be 80 ,
and adhesion 450 assuming , viscosity 20 ,000 .
I 'm pretty much finished everything , so let 's look at the result here .
We just enter Demonstration .
This one was based on the DOE number five .
DOE number five is one to one mixing and this Demo is only one to two mixing ,
and we added the viscosity result right here .
It 's very , very easy .
One click you see the result
and in the format it 's very easy to understand for comparison .
This is the end of my demonstration .
Let me go back to the presentation here .
We consider the JMP worksheet that I 'm just showing you is an integrated platform
and here is the summary .
The worksheet in the stack format , here ,
is used for formulation design , calculation and for recording the results .
The data entry of raw material
which is needed for the worksheet is minimized by virtually linked with
the other file that has additional raw material information .
JSL was widely used to automate
the worksheet output to the tabulate , to graphic , to the statistic analysis ,
and also to create a table with wide data format .
The wide data format , they already have a data structure
for modeling via the machine learning
and also allow the graphical analysis using the ingredient as one of the axis .
Then since each of the row in this wide format
is a unique tool formulation ID ,
this actually can be used as a reference table
to join the other JMP file that has a testing result that has a replication .
When these are joined together ,
then we can plot the raw data and do statistic analysis ,
either as function of the ingredient or as function of the formulation ID .
This JMP Integrated Worksheet Platform
truly illustrates it is an all -in -one platform , very , very capable .
In summary , JMP is not just an advanced DOE software .
JMP 's data analytics has been effectively utilized
in my group for product development
at various stage to speed up the innovation process .
JMP -based formulation worksheet is an integrated platform that feature
broad formulation capability , all in one , easy operation ,
and machine learning ready data structure ,
and more and more waiting to be further explored .
With this , thanks for your attention
and I also like to acknowledge the people I work with and learning to JMP together
and also our management system for supporting JMP adoption initiative .
Thank you very much .
