The ANSYS 3D Design family of products enables CAD modeling and simulation for all design engineers. Since the demands on today’s design engineer to build optimized, lighter and smarter products are greater than ever, using the appropriate design tools is more important than ever.
Rapidly explore ideas, iterate and innovate with ANSYS Discovery 3D design software, evaluate more concepts and rapidly gauge design performance through virtual design testing as you delve deeper into your design’s details, with the same results accuracy as ANSYS flagship products – when and where you need it.
Join PADT’s Training & Support Application Engineer, Robert McCathren for a look at the new 3D design capabilities available in ANSYS 2019 R3 for ANSYS Discovery AIM, Live, and SpaceClaim. These new updates include:
Mass flow outlets
Transient studies with time varying inputs
Structural beam support
Linear buckling support
Physics-aware meshing improvements
Mesh failure localization and visualization improvements
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In this episode, your host and Co-Founder of PADT, Eric Miller is joined by PADT’s Specialist Mechanical Engineer/Lead Trainer Joe Woodward, and Simulation Support Manager Ted Harris, for a discussion on what’s new in the mechanical release for ANSYS 2019 R3, as well as a look at their favorite features. This includes a focus on updates and enhancements to improve ease of use, reduce set-up time, and provide more valuable solutions.
If you would like to learn more about what this release is capable of, check out our webinar on the topic here:
https://www.brighttalk.com/webcast/15747/376304
If you have any questions, comments, or would like to suggest a topic for the next episode, shoot us an email at podcast@padtinc.com we would love to hear from you!
Customizations developed using ANSYS ACT adhere closely to
the user experience that is native to Mechanical and other Workbench apps. Obviously, this is to be expected, but
sometimes it can be a little challenging to fit a particular workflow into the
“tree object” plus “object properties” model.
One way to broaden the available set of user experiences from which to
construct a customized behavior is to use what are known as property
controllers.
Property controllers are classes, which can be implemented
either in C# or Python that are associated with a given property in the details
pane of a given ACT object. These
classes allow the programmer to specialize the functionality and behavior of
the particular property to which they are associated. The association between a given property and
its property controller is made in the ACT XML definition file. For this article, like most of the ones I
write on ACT, I will be using C# as the implementation language.
The degree to which any given property can be specialized by
a property controller is quite vast.
Therefore, I won’t be able to touch on all of the possible
combinations. However, I will
demonstrate two that I have found particularly useful in various ACT apps I’ve
written.
The first is a custom “select” controller that allows the
user to pick one of a set of given Mechanical objects. For most customizations, perhaps the
canonical example, is a select controller that allows a user to pick a particular
coordinate system out of all of the defined coordinate systems in the
model. Yes, there is a template for this
that ships with Mechanical, but I will show a given implementation. Understanding how the controller works will
enable you to apply the same technique to other object types, even other ACT
objects within the same app.
The second is a way to “fly out” a dialog box that can
contain additional custom controls, and that is “anchored” to the side of the
given property box within the details pane.
This is useful for scenarios when we can’t easily fit a particular data
entry within a given property field.
Tabular data is a prime example.
Again, there are some templates for this in Mechanical, but
understanding how to build it up from scratch will allow you to apply the same
principles to more complex dialogs. This
second example will be covered in a subsequent blog post.
Foundations
Before we dive into the individual examples above, let’s
understand some of the basics of property controllers in general. First, how do we associate a given property
controller class with a particular property.
This is accomplished by using the “class” attribute in the property tag
within the XML definition. So, here is
an example from an extension XML file:
You can see that we’ve added a “class” attribute inside the
property tag and set it equal to a fully qualified class name. All other property attributes are the same as
with a typical property. In order for
this to work, however, we will need to implement a class called
“CoordinateSystemSelectController” in the PADT.PropertyControllers namespace.
You will also notice that there is a nested
<attributes> tag inside the <property> tag. This can be used to pass additional
configuration data to the controller as we will see. Clearly, in this case, the additional data is
designed to constrain the types of coordinate systems that will be populated
within the control.
Example 1: Coordinate System Select Property Controller
The method by which behavior is customized for a given
property is by implementing overrides for a series of virtual functions defined
on the property itself. These virtual
functions allow us to hook into various points within the properties lifetime
and operation. The names for these
virtual functions correspond to the callbacks listed in the ANSYS help for the
<property> tag in the ACT XML reference manual. The names are always lowercase. Common ones that I use are functions such as
“onactivate”, “onshow”, “isvalid”,”value2string” and “getvalue”. Except for the “value2string” function, most
of these are probably self-explanatory as to when they would fire. For this controller, I’ll demonstrate a few
of these functions including when and how to use the “value2string”.
Let’s begin with the “onactivate” function. This function is called when the user
“selects” or activates the property. So,
within this function is a good place to populate the list of currently
available coordinate systems. It is
tempting to cache this list so that it doesn’t have to be recomputed. However, if the user deletes, or adds a
coordinate system after we have cached the list, we would not display it as an
option the next time they activated this control. Therefore, on each activation, we build up a
list of available coordinate systems.
Here is the code:
You can see the parameters to this function are parameter
representing the “tree object” to which this property is a member of that
object’s details pane, and a parameter representing this “property”. The second parameter might seem
counterintuitive. You might think that
we are subclassing the property itself and thus this parameter would be
redundant. (i.e, it would be equivalent to the “this” object). However, we are not subclassing the property
per say, but rather implementing a controller object that property itself makes
calls against to modify its own behavior.
Sounds convoluted, I understand, but my guess is that this is what
allows us to specify all of this within the extension XML file. So, it’s a good thing.
Once we get into the function proper, on line 55 we clear
out all of the items within this properties associated drop down control. Then, in lines 56-58 we figure out what are
the enum constants that represent the types of coordinate systems (cylindrical,
Cartesian, etc…) that we would like to present to the user. Note, our attribute “type_filter” could
contain multiple types. Then, in lines 59-67, we iterate over all of the
coordinate systems current defined in the Mechanical session and pick out the
ones that are of the right type. We then
add them to the “options” property of this SimProperty object. Note,
however, that we don’t add the coordinate system objects themselves, but rather
string representations of the object Ids.
This is important. The reason we
don’t add the name of the coordinate system is because names (or labels) in
Mechanical are not required to be unique.
You can create five coordinate systems and name all of them “Bob”,
Mechanical doesn’t care and will treat them as unique. So, we need a unique attribute of the coordinate
system to store in our list of options.
The object Id is guaranteed to be unique. So, we store this instead.
The final bit of code in this function just makes sure to
default select one coordinate system if the user hasn’t already selected one. That functionality is on lines 69-83. If the Value property is null, then the user
(or this code) has not populated the select with a given coordinate system. So,
if there are any coordinate systems that are appropriate we just find the first
one and select it automatically. Note,
if the user later changes this to a different CSYS and this function fires a
second time, it will not overwrite their choice because the null check will
fail on line 69. The reason for this
behavior is because the extension for which this code was written made
extensive use of a single cylindrical coordinate system in a number of
different objects. Typically the user
would add just this one coordinate system in addition to the default global
Cartesian system. So, by adding this
code, the user would not be required to select this coordinate system each time
they added a new object, but rather, the tool would do it for them.
The next bit of code to examine is the “value2string”
function, which is shown below:
You may recall from above that the data we store in the
options property was a list of string representations of the various coordinate
system Ids. Now, if we didn’t implement
this function, when the user interacted with the drop down control what they
would see would be a list of numbers.
They might see a list like “42” “87” and “94”. Clearly, it’s not very intuitive as to which
coordinate system these numbers may refer.
So, what the “value2string” function allows us to do is to
transform the data that the property actually stores into a visual
representation that is meaningful to the user.
However, this is purely a stateless transformation. The actual data store in the property always
remains the string representation of the object’s id. So, you can think of this function as sitting
between the internal code that pulls a value out of the property, and the
internal code that renders that value to the screen. In between these two calls, we have the
opportunity to transform what gets rendered.
So, essentially what we do inside this function is parse the
Id string back into an integer. If
that’s cool, we then lookup the particular mechanical tree object that has this
given Id. Finally, if everything is
kosher with this object, we return the name of the object we looked up. If at any point something goes wrong, we just
return an empty string.
Now, when the user interacts with the property controller,
the will see a list of names corresponding to the coordinate systems of the
appropriate type. If they sadistically
named all of these coordinate systems the same name, then they will see a list
with multiple entries of the same name.
However, each one in the list is a unique coordinate system. How they figure out which one is the one they
actually want is now their problem…
Finally, the last function we will look at is the “getvalue”
function. As the “value2string” function
made the experience of the end user more palatable, so too the “getvalue”
function makes the experience of the developer more palatable. Essentially what it does is analogous to the
“value2string” function, but rather than returning a string, it returns an
actual coordinate system object that can be used in other places in the system. It looks like the following:
As you can see, it is very similar to the “value2string”
object, but instead of returning a string, it returns the actual tree object
itself. Note, you have to cast the
return value at the caller site to the appropriate type, but meh… it’s still
nice to have.
Finally, to see this property controller in action, I’ve
taken a quick screen grab of the properties pane of an ACT object I’ve
implemented. This is a little symmetry
object that implements a homebrewed CPCYC, but you can see the coordinate
system object.
That’s all for this post. Next time we’ll look at how to implement the
flyout feature. Good luck with your ACT
programming needs. Oh, and if you need
some help, or ever want to have some ANSYS customization done for you, let us
know. We do all sorts of customization
work from more run of the mill type
Way back in 2011, PADT participated in our first Governor’s Celebration of Innovation, or GCOI. We actually won the award for being a Pioneer that year, and we also started making custom awards with our 3D Printing systems. And every year we get to see friends, customers, and partners take a PADT original home. 2019 was no different.
You can read about the event in the Phoenix Business Journal here.
This year FreeFall Aerospace was won the Innovation Award for startups. They are part of the ANSYS Startup program and someone we really enjoy working with. In addition, Qwick won the Judges award. They are a local software startup that we have interacted with through our mentoring and angel investing activities.
This year’s awards came out nice, combining PolyJet and Stereolithography to make a kinetic sculpture:
We were pleased to watch these being handed out to eight winners. The Tucson winners, half of those recognized, were happy to show their’s off:
With ANSYS structural analysis software, users are able to solve more complex engineering problems, faster and more efficiently than ever before. Customization and automation of structural solutions is much easier to optimize thanks to new and innovative finite element analysis (FEA) tools available in this product suite.
From designers and occasional users looking for quick, easy and accurate results, to experts looking to model complex materials, large assemblies and nonlinear behavior, ANSYS has you covered. The intuitive interface of ANSYS Mechanical enables engineers of all levels to get answers fast and with confidence.
Join PADT’s Specialist Mechanical Engineer Joe Woodward, for an in-depth look at what’s new in the latest version of ANSYS Mechanical, including updates regarding:
If this is your first time registering for one of our Bright Talk webinars, simply click the link and fill out the attached form. We promise that the information you provide will only be shared with those promoting the event (PADT).
You will only have to do this once! For all future webinars, you can simply click the link, add the reminder to your calendar and you’re good to go!
NOTE 10/28/2019: See updated information regarding Diran extruder heads, below.
Does the idea of 3D printing parts in semi-aromatic polyamides (PA) sound intriguing? Too bad it has nothing to do with making nicely scented models – but it has everything to do with reaping the benefits of the Nylon family’s molecular ring structure. Nylon 6, Nylon 12, carbon-filled Nylon 12 and now a new, smoother Nylon material called Diran each offer material properties well-suited for additive manufacturing on industrial 3D printers.
3D printed Nylon 12 Battery Box. (Image courtesy Stratasys)
Quick chemistry lesson: in polyamides, amine sub-groups containing nitrogen link up with carbon, oxygen and hydrogen in a ring structure; most end up with a strongly connected, semi-crystalline layout that is key to their desirable behaviors. The number of carbon atoms per molecule is one way in which various Nylons (poly-amines) differentiate themselves, and gives rise to the naming process.
Now on to the good stuff. PA thermoplastics are known for strength, abrasion-resistance and chemical stability – useful material properties that have been exploited since Nylon’s discovery at Du Pont in 1935. The first commercial Nylon application came in 1938, when Dr. West’s Miracle Tuft Toothbrush closed the book on boar’s-hair bristle use and let humans gently brush their teeth with Nylon 6 (then called “Exton”) fibers.
Today’s Nylon characteristics translate well to filament-form for printing with Stratasys Fused Deposition Modeling (FDM) production-grade systems. Here’s a look at properties and typical applications for Nylon 6, Nylon 12, Nylon 12 CF (carbon-fiber filled) and Diran (the newest in the Stratasys Nylon material family), as we see their use here at PADT.
When Flexibility
Counts
Nylon 12 became the first Stratasys PA offering, filling a need for customized parts with high fatigue resistance, strong chemical resistance, and just enough “give” to support press (friction-fit) inserts and repetitive snap-fit closures. Users in aerospace, automotive and consumer-goods industries print Nylon 12 parts for everything from tooling, jigs and fixtures to container covers, side-panels and high vibration-load components.
3D Printed Nylon 12 bending example. (Image courtesy Stratasys)
Nylon 12 is the workhorse of the manufacturing world, supporting distortion without breaking and demonstrating a high elongation at break. Its ultimate tensile strength in XZ part orientation (the strongest orientation) is 6,650 psi (46 MPa), while elongation at break is 30 percent. Users can load Nylon 12 filament onto a Stratasys Fortus 380mc CF, 450mc or 900mc system.
As evidenced by the toothbrush renaissance, Nylon 6 has been a popular
thermoplastic for more than 80 years. Combining very high strength with
toughness, Nylon 6 is great for snap-fit parts (middle range of
flexing/stiffness) and for impact resistance; it is commonly used for things
that need to be assembled, offering a clean surface finish for part mating.
Nylon 6 displays an XZ ultimate tensile strength of 9,800 psi (67.6 MPa) and elongation at break of 38%; it is available on the F900 printer. PADT customer MTD Southwest has recently used Nylon 6 to prototype durable containers with highly curved geometries, for testing with gasoline/ethanol blends that would destroy most other plastics.
Prototype gas-tank made of Nylon 6, printed on a Stratasys system, using soluble support. (Image courtesy MTD Southwest)
Both Nylon 12 and Nylon 6 come as black filament that prints
in tandem with a soluble brown support material called SR-110. Soluble supports
make a huge difference in allowing parts with internal structures and
complicated overhangs to be easily 3D printed and post-processed.
Getting Stronger and
Smoother
As with these first two PA versions, Nylon 12CF prints as a black filament and uses SR-110 soluble material for support; unlike those PAs, Nylon 12CF is loaded at 35 percent by weight with chopped carbon fibers averaging 150 microns in length. This fiber/resin combination produces a material with the highest flexural strength of all the FDM Nylons, as well as the highest stiffness-to-weight ratio.
Nylon 12 CF (carbon-fiber filled) 3D printed part, designed as a test brake unit. (Image courtesy Stratasys)
That strength shows up in Nylon 12 CF as a high ultimate XZ tensile strength of 10,960 psi (75.6 MPa), however, similar to other fiber-reinforced materials, the elongation at break is lower than for its unfilled counterpart (1.9 percent). Since the material doesn’t yield, just snaps, the compressive strength is given as the ultimate value, at 9,670 psi (67 MPa).
Nylon 12 CF’s strength and stiffness make it a great choice for lightweight fixtures. It also offers electrostatic discharge (ESD) protection properties better than that of Stratasys’ ABS ESD7, yet is still not quite conductive, if that is important for the part’s end-use. (For more details on printing with Nylon 12 CF, see Seven Tips for 3D Printing with Nylon 12 CF.) The material runs on the Fortus 380mc CF, 450mc or 900mc systems.
Just announced this month, Stratasys’ Diran filament
(officially Diran 410MF07) is another black Nylon-based material; it, too,
features an infill but not of fibers – instead there is a mineral component
listed at seven percent by weight. This filler produces a material whose
smooth, lubricious surface offers low
sliding resistance (new vocabulary word: lubricious, meaning slippery, with
reduced friction; think “lube job” or lubricant).
Robot-arm end printed in Diran, a smooth Nylon-based filament. (Image courtesy Stratasys)
This smooth surface makes Diran parts perfect for applications needing a non-marring interface between a tool and a workpiece; for example, a jig or fixture that requires a part to be slid into place rather than just set down. It resists hydrocarbon-based chemicals, displays an ultimate tensile strength of 5,860 psi (40 MPa), and has a 12 percent elongation at break.
Close-up of Diran’s smooth surface finish. (Image courtesy Stratasys)
(Revised) For the first time, Diran also brings the benefits of Nylon to users of the Stratasys office-environment, plug-and-play F370 printer. The system works with the new material using the same extruder heads as for ABS, ASA and PC-ABS, with just a few material-specific requirements.
To keep thermal expansion consistent across a model and any necessary supports, parts set up for Diran automatically use model material as support. A new, breakaway SUP4000B material comes into play as an interface layer, simplifying support removal. The higher operating temperature also requires a different build tray, but the material’s lubricious properties (just had to use that word again) make for easy part removal and allow that tray to be reused dozens of times.
Read more about this intriguing material on the Diran datasheet:
and contact PADT to request a sample part of Diran or any of these useful Nylon materials.
PADT Inc.
is a globally recognized provider of Numerical Simulation, Product Development
and 3D Printing products and services. For more information on Stratasys
printers and materials, contact us at info@padtinc.com.
In this episode, your host and Co-Founder of PADT, Eric Miller is joined by PADT’s CFD Team Lead Engineer Clinton Smith for a discussion on how ANSYS fluids tools are being used to help predict and control environmental pollution. This information is helping engineers in a variety of ways, such as understanding the formation and dispersion of pollutants such as NOx, SOx, CO and soot.
If you would like to learn more about what this application is capable of, check out our webinar on the topic here: https://www.brighttalk.com/webcast/15747/374571
If you have any questions, comments, or would like to suggest a topic for the next episode, shoot us an email at podcast@padtinc.com we would love to hear from you!
Environmental pollution has been a fact of life for many centuries, though it became a real issue after the start of the industrial revolution. An estimated 6.5 million premature deaths have been linked to air pollution every year.
In order to properly combat this growing issue, the world’s leading minds have turned to a more effective tool for environmental analysis; numerical simulation. Computational fluid dynamics has proven to be a powerful tool when it comes to predicting and controlling air, water, and noise pollution.
Join PADT’s CFD Team Lead Engineer Clinton Smith to learn how ANSYS fluid mechanics solutions provide insight and detailed understanding of the formation and dispersion of pollutants such as NOx, SOx, CO & Soot as well as effective ways for modelling pollution control equipment such as ESP’s, bag filters, and wastewater treatment plants.
If this is your first time registering for one of our Bright Talk webinars, simply click the link and fill out the attached form. We promise that the information you provide will only be shared with those promoting the event (PADT).
You will only have to do this once! For all future webinars, you can simply click the link, add the reminder to your calendar and you’re good to go!
This time we look at another tool for overcoming convergence
difficulties, the Semi-Implicit method.
ANSYS, Inc. describes the semi-implicit method as a hybrid, combining
features of both implicit and explicit finite element methods.
In highly nonlinear problems involving significant deformations we may get a solver error like this one:
*** ERROR *** CP = 18110.688 TIME= 11:58:42 Solution not converged at time 0.921 (load step 1 substep 185). Run terminated.
Like it does with other problems that lead to convergence
failures, the Solution branch will have telltale red lightning bolts,
indicating the solution was not able to complete due to nonconvergence.
In this case, it can be difficult to determine from the error message in the solution output exactly what the problem is. Plotting the Newton-Raphson residuals can be a good starting point. In order to plot the Newton-Raphson residuals, though, we need to turn them on prior to solving. See this older Focus blog for instructions on how to do that:
A plot of the Newton-Raphson residuals shows us where the
highest force imbalance is in the model:
That’s a nice looking plot, but doesn’t tell us much without
knowing more about the simulation. The
model is of a plastic bottle, subject to a force load tending to ‘crush’ the
bottle from top to bottom. There is a
slight off center load as well, so that the force is not purely in the downward
direction.
The bottle is constrained with a fixed support on the bottom
flat surface, and contact elements between the outer surface of the bottle and
a fixed surface representing a table or floor.
This is to prevent the bottle from deflecting below the plane of that
surface.
The material used is a polyethylene plastic, from the ANSYS Granta
Materials Data for Simulation add-on, which is a great tool to get access to
hundreds of materials for ANSYS simulations.
The geometry of the bottle was created in SpaceClaim as a surface body and
meshed with shell elements in ANSYS Mechanical.
The solution was run as nonlinear static, with large
deflection effects turned on. Automatic
Time Stepping was manually activated with a starting and minimum number of
substeps set to 200 and a maximum number of substeps set to 1000.
With these settings, the solution ran to about 92% of the
full load, where it failed to solve after bisecting to the maximum number of
substeps (minimum ‘time’ step). The
force convergence plots showed the bisections and failed convergence attempts
started at about iteration 230 and ‘time’ 0.92.
(If you are not familiar with the convergence plots from a
Newton-Raphson method solution, please see our Focus archives for an article on
the topic – look for the link to the GST Plot:
http://www.padtinc.com/blog/wp-content/uploads/oldblog/PADT_TheFocus_08.pdf).
Even though our solution has not converged, it is probably
helpful to view the deformation results for substeps which did converge (at
partial load) as well as the unconverged results which will be written as the
last set of results.
This plot shows the total deformation at the last converged
substep (time value 0.92):
This plot shows the unconverged solution, ‘extrapolated’ to
time 1.0:
From the unconverged
deformation plot we can see that the top of the bottle is tending to experience
very large deformations. It’s not
surprizing that convergence difficulties are being encountered.
One of the techniques we can utilize to get past this
problem is the Semi-Implicit method in ANSYS Mechanical. As of 2019 R2, this needs to be activated
using a Mechanical APDL command object, but it can be as simple as adding a
single word within the Static Structural branch:
SEMIIMPLICIT
There are some optional fields on that command, but
minimally just the one word command is needed.
Once the semi-implicit method is activated, if the solver
detects the default implicit solver is having trouble, it automatically
switches to the semi-implicit solving scheme.
Like a traditional explicit solver, the semi-implicit method can better
handle very large deformation, transitory-like effects. The method can switch back to implicit if
conditions warrant for a more efficient solution and in fact can switch back
and forth between the two schemes.
The solver output will tell us if the semi-implicit scheme has been activated:
EQUIL ITER 26 COMPLETED. NEW TRIANG MATRIX. MAX DOF INC= 0.9526
NONLINEAR DIAGNOSTIC DATA HAS BEEN WRITTEN TO FILE: file.nd004
DISP CONVERGENCE VALUE = 0.3918 CRITERION= 1.448 <<< CONVERGED
LINE SEARCH PARAMETER = 0.4113 SCALED MAX DOF INC = 0.3918
FORCE CONVERGENCE VALUE = 44.44 CRITERION= 0.9960
MOMENT CONVERGENCE VALUE = 3.263 CRITERION= 0.1423
Writing NEWTON-RAPHSON residual forces to file: file.nr001
>>> TRANSITIONING TO SEMI-IMPLICIT METHOD
NONLINEAR DIAGNOSTIC DATA HAS BEEN WRITTEN TO FILE: file.nd001
EQUIL ITER 1 COMPLETED. NEW TRIANG MATRIX. MAX DOF INC= 0.8788E-04
NONLINEAR DIAGNOSTIC DATA HAS BEEN WRITTEN TO FILE: file.nd002
*** LOAD STEP 1 SUBSTEP 185 COMPLETED. CUM ITER = 284
*** TIME = 0.920010 TIME INC = 0.100000E-04
Kinetic Energy = 0.2157 Potential Energy = 60.59
*** AUTO STEP TIME: NEXT TIME INC = 0.10000E-04 UNCHANGED
NONLINEAR DIAGNOSTIC DATA HAS BEEN WRITTEN TO FILE: file.nd003
There are some ‘symptoms’ of the switch from implicit to
explicit. The most obvious is probably
that the force convergence plot will stop updating.
Changing the Solution Output to the Solver Output will show
the explicit scheme being used in that case.
The telltale is the information on Response Frequency and Period (the
example shown is a static structural solution).
Deformation plot trackers and contact trackers continue to
work as expected during the solution, however.
Using the semi-implicit method, the solution was able to
successfully converge to the full load, and converged results are available at
the last time point:
We also used the new keyframe animation technique to animate the results time history.
The semi-implicit method is well documented within the
Mechanical APDL 2019 R2 Help, in the Advanced Analysis Guide, chapter 3 on
Semi-Implicit Method. We suggest
reviewing that information to get a much better handle on the technique.
We hope this is helpful in getting your nonlinear solutions
to converge the full value of applied loads.
It was a special week for PADT when we received two awards for our activities in the Arizona business community. Every time anyone at PADT, or the company as a whole is recognized, it reflects the long term commitment our employees have made to our community, their focus on our customers, and their continued effort to simply be good at what they do.
The details for these two awards are given in the press release below. You can see a list of the fifteen awards PADT has received since 2002 here.
A big thank you to everyone who contributed to our success over the years. We are humbled by this type of recognition, so we don’t have much else to say.
The official press release is available as HTML or PDF.
PADT Recognized for its Contribution to Arizona’s Tech Community with Two Awards: Top Tech Exec by Phoenix Business Journal and Special AZBio Award
PADT Co-founder Eric Miller Honored in the CEO Category by the Phoenix Business Journal and PADT’s 25Years of Contributions Recognized by AZBio
TEMPE,
Ariz., Oct. 8, 2019 ─ PADT, a leader in numerical simulation, product development, and 3D printing,
is honored to announce that Principal and Co-founder, Eric Miller, is a winner
of the Phoenix Business Journal’s 2019 Top Tech Exec Award in the CEO category. On the heels of that honor, the state’s
bioscience industry organization, AZBio, recognized the contributions PADT has made to
the local medical technology community over the past 25 years with a special
trophy presented at the 2019 AZBio
Awards ceremony on October 2,
2019.
PBJ Top Tech Exec Award
“Thank
you to the Phoenix Business Journal for recognizing our dedication to
Phoenix’s innovation landscape,” said Miller. “We work hard to ensure the
success of our clients, and I’m extremely proud of the company my co-founders and
I have built together here at PADT. When our clients and employees are happy,
there’s no limit to what we can achieve.”
Each
year, the Phoenix Business Journal recognizes individuals for their
involvement and influence in the technology industry with the AZ Top Tech Exec
Awards. Miller was selected for his role in establishing PADT as an integral
part of the local manufacturing ecosystem, providing market-leading numerical
simulation tools from ANSYS, Inc., and the 3D printing systems from Stratasys
across the Southwest. PADT also assists
companies through engineering consulting for design, test, and simulation and
is the state’s largest provider of 3D printing services. Companies come to PADT
for the tools and consulting they need to design and manufacture better
products.
In addition to PADT’s
extensive business victories, Miller was also chosen as a result of his continuous
involvement in the Phoenix technology community. Miller is an angel investor
with Arizona Tech Investors, and he frequently lends his time to advise
entrepreneurs. He is also an active mentor for ACA’s Venture Ready Program and
was recently named Vice-Chair of the Arizona Technology Council’s Board of Directors.
He is also on the board of directors of BioAccel and is a regular guest
contributor of articles on technology, business, and the local tech scene to
the Phoenix Business Journal.
“Congratulations to Eric Miller and all of the 2019 Top Tech Execs,” said Ray Schey, market president and publisher, Phoenix Business Journal. “Eric is one of the key contributors to the Valley’s incredible growth in the technology sector. This recognition is acknowledgement of his, and the work of the other talented tech execs, in the industry. Arizona is being noticed both nationally and internationally for its innovation and growth in technology.”
AZBio
Recognition
During
the 2019 AZBio Awards, the Arizona Bioindustry Association recognized PADT’s 25th
anniversary with a special award in recognition of the contribution the company
has made to Arizona’s biotechnology community.
It was a special honor for PADT to receive the iconic double-double
helix trophy that PADT has been 3-D printing for the AZBio awards for eight
years.
“Since
it was founded in Arizona in 1994, PADT’s dedicated employees have provided
their knowledge of innovation and enthusiasm for collaboration to advance our
common goal of making Arizona a top-tier bioscience state,” said Joan
Koerber-Walker, president and CEO, AZBio. “It was a privilege to recognize this
trail-blazing company that’s grown to be the largest of its kind in the
Southwest for its contributions.”
Although
PADT provides products and services to companies across industries, the Bioscience
sector has been a special focus of the company because of its
disproportionately positive impact on the overall community. PADT’s involvement
in groups like AZBio is way for the company to amplify its impact.
“PADT
Co-founder Mark Johnson started our special commitment to the medical device industry
and our contributions to the bioscience community early in the company’s
history,” said Rob Rowan, director of Engineering, PADT. “Although Mark is
unfortunately no longer with us, we continue to execute on his vision of bringing
aerospace quality engineering to the medical sector, helping companies large
and small translate their innovative ideas into viable products that improve patient
outcomes across applications.”
About
Eric Miller
As a Co-founder of PADT in 1994, Miller was able to pursue his
interests in simulation, 3D printing, operations, and small business
management. He is often called upon to write and speak on simulation, design, and 3D Printing as well as on
startups and the high-tech sector. In addition, Miller is a board member for
several tech-related organizations and Vice-Chair of the Arizona Technology
Council. He holds a B.S. in Mechanical Engineering from UC Berkeley.
About PADT
PADT is an engineering product and services
company that focuses on helping customers who develop physical products by
providing Numerical Simulation, Product Development, and 3D Printing solutions.
PADT’s worldwide reputation for technical excellence and experienced staff is
based on its proven record of building long-term win-win partnerships with
vendors and customers. Since its establishment in 1994, companies have relied
on PADT because “We Make Innovation Work.” With over 90 employees, PADT
services customers from its headquarters at the Arizona State University
Research Park in Tempe, Arizona, and from offices in Torrance, California,
Littleton, Colorado, Albuquerque, New Mexico, Austin, Texas, and Murray, Utah,
as well as through staff members located around the country. More information
on PADT can be found at www.PADTINC.com.
In this episode, your host and Co-Founder of PADT, Eric Miller is joined by PADT’s simulation support & application engineer Doug Oatis for a discussion on what is new in ANSYS 2019 R3 with regards to tools and applications for topology optimization and additive manufacturing.
If you would like to learn more about what’s new in this latest release, check out our webinar on the topic here: https://www.brighttalk.com/webcast/15747/372133?
If you have any questions, comments, or would like to suggest a topic for the next episode, shoot us an email at podcast@padtinc.com we would love to hear from you!
Electromagnetic models, especially those covering a
frequency bandwidth, require a frequency dependent definition of dielectric
materials. Material definitions in ANSYS Electronics Desktop can include
frequency dependent curves for use in tools such as HFSS and Q3D. However, there
are 5 different models to choose from, so you may be asking: What’s the
difference?
In this blog, I will cover each of the options in detail. At
the end, I will also show how to activate the automatic setting for applying a
frequency dependent model that satisfies the Kramers-Kronig conditions for
causality and requires a single frequency definition.
Background
Recalling that the dielectric properties of material are coming from the material’s polarization
(1)
where D is the electric flux density, E is the electric field intensity, and P is the polarization vector. The material polarization can be written as the convolution of a general dielectric response (pGDR) and the electric field intensity.
(2)
The dielectric polarization spectrum is characterized by three dispersion relaxation regions α, β, and γ for low (Hz), medium (KHz to MHz) and high frequencies (GHz and above). For example, in the case of human tissue, tissue permittivity increases and effective conductivity decreases with the increase in frequency [1].
Fig. 1. α, β and γ regions of dielectric permittivity
Each of these regions can be modeled with a relaxation time constant
(3)
where τ is the relaxation time.
(4)
The well-known Debye expression can be found by use of spectral representation of complex permittivity (ε(ω)) and it is given as:
(5)
(6)
where ε∞ is the permittivity at frequencies where ωτ>>1, εs is the permittivity at ωτ>>1, and j2=-1. The magnitude of the dispersion is ∆ε = εs-ε∞.
The multiple pole Debye dispersion equation has also been used to characterize dispersive dielectric properties [2]
(7)
In particular, the complexity of the structure and
composition of biological materials may cause that each dispersion region be
broadened by multiple combinations. In that case a distribution parameter is introduced
and the Debye model is modified to what is known as Cole-Cole model
(8)
where αn, the distribution parameter, is a measure of broadening of the dispersion.
Gabriel et. al [3] measured a number of human tissues in the range of 10 Hz – 100 GHz at the body temperature (37℃). This data is freely available to the public by IFAC [4].
Frequency Dependent Material Definition in HFSS and Q3D
In HFSS you can assign conductivity either directly as bulk
conductivity, or as a loss tangent. This provides flexibility, but you should
only provide the loss once. The solver uses the loss values just as they are
entered.
To define a user-defined material choose Tools->Edit Libraries->Materials (Fig. 2). In Edit Libraries window either find your material from the library or choose “Add Material”.
Fig. 2. Edit Libraries screen shot.
To add frequency dependence information, choose “Set
Frequency Dependency” from the “View/Edit
Material” window, this will open “Frequency Dependent Material Setup
Option” that provides five different ways of defining materials properties
(Fig. 3).
Fig. 3. (Left) View/Edit Material window, (Right) Frequency Dependent Material Setup Option.
Before choosing a method of defining the material please
note [5]:
The Piecewise Linear and Frequency Dependent Data Points models apply to both the electric and magnetic properties of the material. However, they do not guarantee that the material satisfies causality conditions, and so they should only be used for frequency-domain applications.
The Debye, Multipole Debye and Djordjevic-Sarkar models apply only to the electrical properties of dielectric materials. These models satisfy the Kramers-Kronig conditions for causality, and so are preferred for applications (such as TDR or Full-Wave SPICE) where time-domain results are needed. They also include an automatic Djordjevic-Sarkar model to ensure causal solutions when solving frequency sweeps for simple constant material properties.
HFSS and Q3D can interpolate the property’s values at the desired frequencies during solution generation.
Piecewise Linear
This option is the simplest way to define frequency dependence. It divides the frequency band into three regions. Therefore, two frequencies are needed as input. Lower Frequency and Upper Frequency, and for each frequency Relative Permittivity, Relative Permeability, Dielectric Loss Tangent, and Magnetic Loss Tangent are entered as the input. Between these corner frequencies, both HFSS and Q3D linearly interpolate the material properties; above and below the corner frequencies, HFSS and Q3D extrapolate the property values as constants (Fig. 4).
Fig. 4. Piecewise Linear Frequency Dependent Material Input window.
Once these values are entered, 4 different data sets are created ($ds_epsr1, $ds_mur1, $ds_tande1, $ds_tandm1). These data sets now can be edited. To do so choose Project ->Data sets, and choose the data set you like to edit and click Edit (Fig. 5). This data set can be modified with additional points if desired (Fig. 6).
Fig. 5. (Left) Project data set selection, (right) defined data set for the material.
Fig. 6. A sample data set.
Frequency Dependent
Frequency Dependent material definition is similar to
Piecewise Linear method, with one difference. After selecting this option, Enter Frequency Dependent Data Point opens
that gives the user the option to use which material property is defined as a
dataset, and for each one of them a dataset should be defined. The datasets can
be defined ahead of time or on-the-fly. Any number of data points may be
entered. There is also the option of importing or editing frequency dependent
data sets for each material property (Fig. 7).
Fig. 7. This window provides options of choosing which material property is frequency dependent and enter the data set associated with it.
Djordjevic-Sarkar
This model was developed initially for FR-4, commonly used
in printed circuit boards and packages [6]. In fact, it uses an infinite
distribution of poles to model the frequency response, and in particular the
nearly constant loss tangent, of these materials.
(9)
where ε∞ is the permittivity at very high frequency, is the conductivity at low (DC) frequency, j2=-1, ωA is the lower angular frequency (below this frequency permittivity approaches its DC value), ωB is the upper angular frequency (above this frequency permittivity quickly approaches its high-frequency permittivity). The magnitude of the dispersion is ∆ε = εs-ε∞.
Both HFSS and Q3D allow the user to enter the relative permittivity and loss tangent at a single measurement frequency. The relative permittivity and conductivity at DC may optionally be entered. Writing permittivity in the form of complex permittivity [7]
(10)
(11)
Therefore, at the measurement frequency one can separate real and imaginary parts
(12)
(13)
where
(14)
Therefore, the parameters of Djordjevic-Sarkar can be extracted, if the DC conductivity is known
(15)
If DC conductivity is not known, then a heuristic approximation is De = 10 ε∞ tan δ1.
The window shown in Fig. 8 is to enter the measurement values.
Fig. 8. The required values to calculate permittivity using Djordjevic-Sarkar model.
Debye Model
As explained in the background section single pole Debye model is a good approximation of lossy dispersive dielectric materials within a limited range of frequency. In some materials, up to about a 10 GHz limit, ion and dipole polarization dominate and a single pole Debye model is adequate.
(16)
(17)
(18)
(19)
(20)
The Debye parameters can be calculated from the two measurements [7]
(21)
Both HFSS and Q3D allow you to specify upper and lower measurement frequencies, and the loss tangent and relative permittivity values at these frequencies. You may optionally enter the permittivity at high frequency, the DC conductivity, and a constant relative permeability (Fig. 9).
Fig. 9. The required values for Single Pole Debye model.
Multipole Debye Model
For Multipole Debye Model multiple frequency measurements are required. The input window provides entry points for the data of relative permittivity and loss tangent versus frequency. Based on this data the software dynamically generates frequency dependent expressions for relative permittivity and loss tangent through the Multipole Debye Model. The input dialog plots these expressions together with your input data through the linear interpolations (Fig. 10).
Fig. 10. The required values for Multipole Debye model.
Cole Cole Material
Model
The Cole Cole Model is not an option in the material
definition, however, it is possible to generate the frequency dependent
datasets and use Frequency Dependent option to upload these values. In fact ANSYS
Human Body Models are built based on the data from IFAC
database and Frequency Dependent option.
Visualization
Frequency-dependent properties can be plotted in a few
different ways. In View/Edit Material dialog right-click and
choose View Property vs. Frequency. In addition, the dialogs for each of
the frequency dependent material setup options contain plots displaying
frequency dependence of the properties.
You can also double-click the material property name to view
the plot.
Automatically use
causal materials
As mentioned at the beginning, there is a simple automatic method for applying a frequency dependent model in HFSS. Select the menu item HFSS->Design Setting, and check the box next to Automatically use casual materials under Lossy Dielectrics tab.
Fig. 11. Causal material can be enforced in HFSS Design Settings.
This option will automatically apply the Djordjevic-Sarkar
model described above to objects with constant material permittivity greater
than 1 and dielectric loss tangent greater than 0. Keep in mind, not only is
this feature simple to use, but the Djordjevic-Sarkar model satisfies the
Kramers-Kronig conditions for causality which is particularly preferred for wideband
applications and where time-domain results will also be needed. Please note
that if the assigned material is already frequency dependent,
automatic creation of frequency dependent lossy materials is ignored.
If you would like more information or have any questions
about ANSYS products please email info@padtinc.com
W. D. Hurt, “Multiterm Debye dispersion
relations for permittivity of muscle,” IEEE Trans. Biomed. Eng, vol. 32,
pp. 60-64, 1985.
S. Gabriel, R. W.
Lau, and C. Gabriel. “The dielectric properties of biological tissues:
III. Parametric models for the dielectric spectrum of tissues.” Physics
in Medicine & Biology, vol. 41, no. 11, pp. 2271, 1996.
A. R. Djordjevic, R. D. Biljic, V. D.
Likar-Smiljani, and T. K. Sarkar, “Wideband
frequency-domain characterization of FR-4 and time-domain
causality,” IEEE Trans. on Electromagnetic Compatibility,
vol. 43, no. 4, p. 662-667, Nov. 2001.
Engineers, educators, and enthusiasts gathered on the green lawn of beside the Platte River at the Blind Faith Brewing to talk about Additive Manufacturing. Over 170 attendees (and two dogs) met each other, caught up with old colleagues, and shared their AM journey during the breaks and listened to 13 presenters and panelists. 12 antipasto platters and 30 pizzas were consumed, and 298 beers or sodas were imbibed. By the numbers and by type of interaction we saw, a successful event all around.
This was the fourth annual gathering, hosted by PADT and sponsored by our partners at this brewery. We could not have put this event on without the support of Stratasys, ANSYS, ZEISS, and Desktop Metal. We also want to thank our promotional partners, Women in 3D Printing and Space for Humanity who both brought new people to our community. Carbon, Visser and a student project with Ball Aerospace did their part as exhibitors.
Check out the Slideshow at the end of this post to get a visual snapshot of the day.
We want to thank the true stars of our event, the speakers and panelists who shared their knowledge and experience that turned a great gathering into a learning experience.
We started the morning off with an inspirational keynote from Dr. Robert Zubrin. A visionary in the space community and long term champion of going to Mars, Dr. Zubrin shared with us his observations about the new space race with his talk: “The Case for Space: How the Revolution in Spaceflight Opens Up a Future of Limitless Possibilities.” He left the packed audience energized and ready to do our part in this next step in humanities exploration of the universe. He stayed after to talk with people and sign copies of his book, which you can find here.
We then heard from user David Waller of Ball Aerospace on his experience with their Desktop Metal system. He went over the testing, lessons learned, and usage of their Studio system. It was a great in-depth look at someone implementing a new technology. There is a lot of interest around this lower-cost approach to producing metal parts, and the audience was full of questions.
Sticking with the Desktop Metal technology, PADT’s very own Pamela Waterman talked about how PADT is using our in-house Zeiss Optical Scanning hardware and software to inspect the parts we are making with our Desktop Metal System. She shared what we have learned about following the design guidelines that are developing for this technology and how scanning is a fast and accurate way to determine the final geometry created in the three-step process of building a green part, debinding, and sintering.
Next up was Christopher Robinson form ANSYS, Inc. to talk about recent additions to the ANSYS Additive products. He shared how customers are using simulation to design parts for metal powder bed fusion AM and then model the build process to predict and avoid failures as well as compensate for the distortion inherent in the process. The key takeaway was that simulation is the solution for getting parts built right the first time.
After a short break, and some AM trivia that won some PADT25 T-Shirts for people who knew the history of 3D Printing, we heard all about the new V650 Flex Stereolithography system that Stratasys recently introduced. Yes, Stratasys now makes and sells an SL system and it is literally a dream machine designed by people with decades of AM and Stereolithography experience. Learn more about this open and powerful system here.
Another AM technology was up next when Nick Jacobson spoke about Voxel Printing with PolyJet technologies. He discussed how he varies materials and colors spacially to create unique and realistic replicas for medicine and engineering. He also showed how the voxel-based geometry he creates can be used to create Virtual Reality representations of objects. Much of their work revolves around the visualization of hearts for adults and children to improve surgery planning. While we had been focused on space at the start of the afternoon, he reminded us of the immediate and life saving medical applications of AM.
And then we moved back to space with a presentation from Lockheed Martin‘s Brian Kaplun on how they are using AM to create parts that will fly on the Orion Spacecraft. Making production parts with 3D Printing has been a long-term goal for the whole industry, and Lockheed Martin has done the long and hard work of design, test, and putting processes in place to make this dream a reality. One of the biggest takeaways of his talk was how once the Astronauts saw a few AM parts in the capsule, they started asking of its use to redesign other tools and components. The ultimate end-users, they saw the value of lightweight and strong parts that could be made without the limitations of traditional manufacturing.
We finished up the day, after another break and some more trivia, with a fascinating panel on AM at Colorado’s leading Universities. We were lucky to have Ray Huff from Wohlers Associates moderate a distinguished group of deans, directors, and professors from four outstanding but different institutions:
Martin Dunn PhD, Dean of Engineering, CU Denver
Jenifer Blacklock PHD, Mechanical Engineering Professor – Colorado School of Mines
David Prawel PhD, Director, Idea-2-Product 3D Printing Lab, Colorado State University
Matt Gordon, PhD, Chair, Mechanical Engineering, University of Denver
Their wide-ranging discussion covered their education and research around AM. A common theme was industry cooperation. Each school shared how they use AM to help students not just make things, but also understand how parts are made. The discussion was fantastic and ended far too soon, which is always an indicator of a great group of experts.
And that sums up our great day, leaving out several hundred side conversations that went on. Check out this slide show to get a feel for how energetic and interesting the afternoon was.
As everyone left, some reluctantly and after one more beer, the common comment was that they can’t wait to get together again with everyone. We hope that next year we will have more speakers and participants and continue to support the growth of Additive Manufacturing in Colorado.
A quick note about the location: You are not wrong if you remember a different name for the three previous events. St. Patricks’s is now Blind Faith and the new owners could have not been more welcoming. Plus, they have more Belgian’s, which I am a big fan of.
ANSYS offers a complete simulation workflow for additive manufacturing (AM) that allows you to transition your R&D efforts for metal additive manufacturing into a successful manufacturing operation. This best-in-class solution for additive manufacturing enables simulation at every step in your AM process. It will help you optimize material configurations and machine and parts setup before you begin to print. As a result, you’ll greatly reduce — and potentially eliminate — the physical process of trial-and- error testing.
Through the use of ANSYS tools such as Additive Prep, Print, and Science, paired with topology optimization capabilities in ANSYS Mechanical Workbench, the need for physical process of trial-and-error testing has been greatly reduced.
Join PADT’s Simulation Support and Application Engineer Doug Oatis for an exploration of the ANSYS tools that help to optimize additive manufacturing, and what new capabilities are available for them when upgrading to ANSYS 2019 R3. This presentation includes updates regarding:
Level-set based topology optimization
The export of build files directly to AM machines
Switching between viewing STL supports, mesh, or element densities
Multiple support being made in a single simulation (volume-less & solid supports)
If this is your first time registering for one of our Bright Talk webinars, simply click the link and fill out the attached form. We promise that the information you provide will only be shared with those promoting the event (PADT).
You will only have to do this once! For all future webinars, you can simply click the link, add the reminder to your calendar and you’re good to go!
Eric Miller, Joe Woodward, Doug Oatis, & Ted Harris
Description:
In this episode, your host and Co-Founder of PADT, Eric Miller is joined by PADT’s simulation support manager Ted Harris, specialist mechanical engineer Joe Woodward, and simulation support & application engineer Doug Oatis for a discussion on what is new in ANSYS 2019 R3 with regards to the mechanical solver, element, and contact enhancements.
If you would like to learn more about what’s new in this latest mechanical release, check out our webinar on the topic here: https://www.brighttalk.com/webcast/15747/371263
If you have any questions, comments, or would like to suggest a topic for the next episode, shoot us an email at podcast@padtinc.com we would love to hear from you!
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