If you are an ANSYS RSM (Remote Solve Manager) user, you’ll find some changes in version 18.0. Most of the changes, which are improvements to the installation and configuration process, are under the hood from a user standpoint. One key change for users, though, is how you monitor a running job. This short entry shows how to do it in version 18.0.
Rather than bring up the RSM monitor window from the Start menu as was done in prior version, in 18.0 we launch the RSM job monitor directly from the Workbench window, by clicking on Jobs > Open Job Monitor… as shown here:
When a solution has been submitted to RSM for solution on a remote cluster or workstation, it will show up in the resulting Job Monitor window, like this:
Hopefully this saves some effort in trying to figure out where to monitor jobs you have submitted to RSM. Happy solving!
How can the mechanical behavior of cellular structures (honeycombs, foams and lattices) be modeled?
This is the second in a two-part post on the modeling aspects of 3D printed cellular structures. If you haven’t already, please read the first part here, where I detail the challenges associated with modeling 3D printed cellular structures.
The literature on the 3D printing of cellular structures is vast, and growing. While the majority of the focus in this field is on the design and process aspects, there is a significant body of work on characterizing behavior for the purposes of developing analytical material models. I have found that these approaches fall into 3 different categories depending on the level of discretization at which the property is modeled: at the level of each material point, or at the level of the connecting member or finally, at the level of the cell. At the end of this article I have compiled some of the best references I could find for each of the 3 broad approaches.
1. Continuum Modeling
The most straightforward approach is to use bulk material properties to represent what is happening to the material at the cellular level [1-4]. This approach does away with the need for any cellular level characterization and in so doing, we do not have to worry about size or contact effects described in the previous post that are artifacts of having to characterize behavior at the cellular level. However, the assumption that the connecting struts/walls in a cellular structure behave the same way the bulk material does can particularly be erroneous for AM processes that can introduce significant size specific behavior and large anisotropy. It is important to keep in mind that factors that may not be significant at a bulk level (such as surface roughness, local microstructure or dimensional tolerances) can be very significant when the connecting member is under 1 mm thick, as is often the case.
The level of error introduced by a continuum assumption is likely to vary by process: processes like Fused Deposition Modeling (FDM) are already strongly anisotropic with highly geometry-specific meso-structures and an assumption like this will generate large errors as shown in Figure 1. On the other hand, it is possible that better results may be had for powder based fusion processes used for metal alloys, especially when the connecting members are large enough and the key property being solved for is mechanical stiffness (as opposed to fracture toughness or fatigue life).
2. Cell Level Homogenization
The most common approach in the literature is the use of homogenization – representing the effective property of the cellular structure without regard to the cellular geometry itself. This approach has significantly lower computational expense associated with its implementation. Additionally, it is relatively straightforward to develop a model by fitting a power law to experimental data [5-8] as shown in the equation below, relating the effective modulus E* to the bulk material property Es and their respective densities (ρ and ρs), by solving for the constants C and n.
While a homogenization approach is useful in generating comparative, qualitative data, it has some difficulties in being used as a reliable material model in analysis & simulation. This is first and foremost since the majority of the experiments do not consider size and contact effects. Secondly, even if these were considered, the homogenization of the cells only works for the specific cell in question (e.g. octet truss or hexagonal honeycomb) – so every new cell type needs to be re-characterized. Finally, the homogenization of these cells can lose insight into how structures behave in the transition region between different volume fractions, even if each cell type is calibrated at a range of volume fractions – this is likely to be exacerbated for failure modeling.
3. Member Modeling
The third approach involves describing behavior not at each material point or at the level of the cell, but at a level in-between: the connecting member (also referred to as strut or beam). This approach has been used by researchers [9-11] including us at PADT  by invoking beam theory to first describe what is happening at the level of the member and then use that information to build up to the level of the cells.
This approach, while promising, is beset with some challenges as well: it requires experimental characterization at the cellular level, which brings in the previously mentioned challenges. Additionally, from a computational standpoint, the validation of these models typically requires a modeling of the full cellular geometry, which can be prohibitively expensive. Finally, the theory involved in representing member level detail is more complex, makes assumptions of its own (e.g. modeling the “fixed” ends) and it is not proven adequately at this point if this is justified by a significant improvement in the model’s predictability compared to the above two approaches. This approach does have one significant promise: if we are able to accurately describe behavior at the level of a member, it is a first step towards a truly shape and size independent model that can bridge with ease between say, an octet truss and an auxetic structure, or different sizes of cells, as well as the transitions between them – thus enabling true freedom to the designer and analyst. It is for this reason that we are focusing on this approach.
Continuum models are easy to implement and for relatively isotropic processes and materials such as metal fusion, may be a good approximation of stiffness and deformation behavior. We know through our own experience that these models perform very poorly when the process is anisotropic (such as FDM), even when the bulk constitutive model incorporates the anisotropy.
Homogenization at the level of the cell is an intuitive improvement and the experimental insights gained are invaluable – comparison between cell type performances, or dependencies on member thickness & cell size etc. are worthy data points. However, caution needs to be exercised when developing models from them for use in analysis (simulation), though the relative ease of their computational implementation is a very powerful argument for pursuing this line of work.
Finally, the member level approach, while beset with challenges of its own, is a promising direction forward since it attempts to address behavior at a level that incorporates process and geometric detail. The approach we have taken at PADT is in line with this approach, but specifically seeks to bridge the continuum and cell level models by using cellular structure response to extract a point-wise material property. Our preliminary work has shown promise for cells of similar sizes and ongoing work, funded by America Makes, is looking to expand this into a larger, non-empirical model that can span cell types. If this is an area of interest to you, please connect with me on LinkedIn for updates. If you have questions or comments, please email us at email@example.com or drop me a message on LinkedIn.
Our work on 3D printed honeycomb modeling that started as a Capstone project with students from ASU in September 2015 (described in a previous blog post), was published in a peer-reviewed paper released last week in the proceedings of the SFF Symposium 2016. The full title of the paper is “A Validated Methodology for Predicting the Mechanical Behavior of ULTEM-9085 Honeycomb Structures Manufactured by Fused Deposition Modeling“. This was the precursor work that led to a us winning an 18-month award to pursue this work further with America Makes.
Download the whole paper at the link below:
Abstract ULTEM-9085 has established itself as the Additive Manufacturing (AM) polymer of choice for end-use applications such as ducts, housings, brackets and shrouds. The design freedom enabled by AM processes has allowed us to build structures with complex internal lattice structures to enhance part performance. While solutions exist for designing and manufacturing cellular structures, there are no reliable ways to predict their behavior that account for both the geometric and process complexity of these structures. In this work, we first show how the use of published values of elastic modulus for ULTEM-9085 honeycomb structures in FE simulation results in 40- 60% error in the predicted elastic response. We then develop a methodology that combines experimental, analytical and numerical techniques to predict elastic response within a 5% error. We believe our methodology is extendable to other processes, materials and geometries and discuss future work in this regard.
What is Topological Optimization? If you’re not familiar with the concept, in finite element terms it means performing a shape optimization utilizing mesh information to achieve a goal such as minimizing volume subject to certain loads and constraints. Unlike parameter optimization such as with ANSYS DesignXplorer, we are not varying geometry parameters. Rather, we’re letting the program decide on an optimal shape based on the removal of material, accomplished by deactivating mesh elements. If the mesh is fine enough, we are left with an ‘organic’ sculpted shape elements. Ideally we can then create CAD geometry from this organic looking mesh shape. ANSYS SpaceClaim has tools available to facilitate doing this.
Topological optimization has seen a return to prominence in the last couple of years due to advances in additive manufacturing. With additive manufacturing, it has become much easier to make parts with the organic shapes resulting from topological optimization. ANSYS has had topological optimization capability both in Mechanical APDL and Workbench in the past, but the capabilities as well as the applications at the time were limited, so those tools eventually died off. New to the fold are ANSYS ACT Extensions for Topological Optimization in ANSYS Mechanical for versions 17.0, 17.1, and 17.2. These are free to customers with current maintenance and are available on the ANSYS Customer Portal.
In deciding to write this piece, I decided an interesting example would be the brace that is part of all curved saxophones. This brace connects the bell to the rest of the saxophone body, and provides stiffness and strength to the instrument. Various designs of this brace have been used by different manufacturers over the years. Since saxophone manufacturers like those in other industries are often looking for product differentiation, the use of an optimized organic shape in this structural component could be a nice marketing advantage.
This article is not intended to be a technical discourse on the principles behind topological optimization, nor is it intended to show expertise in saxophone design. Rather, the intent is to show an example of the kind of work that can be done using topological optimization and will hopefully get the creative juices flowing for lots of ANSYS users who now have access to this capability.
That being said, here are some images of example bell to body braces in vintage and modern saxophones. Like anything collectible, saxophones have fans of various manufacturers over the years, and horns going back to production as early as the 1920’s are still being used by some players. The older designs tend to have a simple thin brace connecting two pads soldered to the bell and body on each end. Newer designs can include rings with pivot connections between the brace and soldered pads.
Hopefully those examples show there can be variation in the design of this brace, while not largely tampering with the musical performance of the saxophone in general. The intent was to pick a saxophone part that could undergo topological optimization which would not significantly alter the musical characteristics of the instrument.
The first step was to obtain a CAD model of a saxophone body. Since I was not able to easily find one freely available on the internet that looked accurate enough to be useful, I created my own in ANSYS SpaceClaim using some basic measurements of an example instrument. I then modeled a ‘blob’ of material at the brace location. The idea is that the topological optimization process will remove non-needed material from this blob, leaving an optimized shape after a certain level of volume reduction.
In ANSYS Mechanical, the applied boundary conditions consisted of frictionless support constraints at the thumb rest locations and a vertical displacement constraint at the attachment point for the neck strap. Acceleration due to gravity was applied as well. Other loads, such as sideways inertial acceleration, could have been considered as well but were ignored for the sake of simplicity for this article. The material property used was brass, with values taken from Shigley and Mitchell’s Mechanical Engineering Design text, 1983 edition.
This plot shows the resulting displacement distribution due to the gravity load:
Now that things are looking as I expect, the next step is performing the topological optimization.
Once the topological optimization ACT Extension has been downloaded from the ANSYS Customer Portal and installed, ANSYS Mechanical will automatically include a Topological Optimization menu:
I set the Design Region to be the blog of material that I want to end up as the optimized brace. I did a few trials with varying mesh refinement. Obviously, the finer the mesh, the smoother the surface of the optimized shape as elements that are determined to be unnecessary are removed from consideration. The optimization Objective was set to minimize compliance (maximize stiffness). The optimization Constraint was set to volume at 30%, meaning reduce the volume to 30% of the current value of the ‘blob’.
After running the solution and plotting Averaged Node Values, we can see the ANSYS-determined optimized shape:
What is apparent when looking at these shapes is that the ‘solder patch’ where the brace attaches to the bell on one end and the body on the other end was allowed to be reduced. For example, in the left image we can see that a hole has been ‘drilled’ through the patch that would connect the brace to the body. On the other end, the patch has been split through the middle, making it look something like an alligator clip.
Another optimization run was performed in which the solder pads were held as surfaces that were not to be changed by the optimization. The resulting optimized shape is shown here:
Noticing that my optimized shape seemed on the thick side when compared to production braces, I then changed the ‘blob’ in ANSYS SpaceClaim so that it was thinner to start with. With ANSYS it’s very easy to propagate geometry changes as all of the simulation and topological optimizations settings stay tied to the geometry as long as the topology of those items stays the same.
Here is the thinner chunk after making a simple change in ANSYS SpacClaim:
And here is the result of the topological optimization using the thinner blob as the starting point:
Using the ANSYS SpaceClaim Direct Modeler, the faceted STL file that results from the ANSYS topological optimization can be converted into a geometry file. This can be done in a variety of ways, including a ‘shrink wrap’ onto the faceted geometry as well as surfaces fit onto the facets. Another option is to fit geometry in a more general way in an around the faceted result. These methods can also be combined. SpaceClaim is really a great tool for this. Using SpaceClaim and the topological optimization (faceted) result, I came up with three different ‘looks’ of the optimized part.
Using ANSYS Workbench, it’s very easy to plug the new geometry component into the simulation model that I already had setup and run in ANSYS Mechanical using the ‘blob’ as the brace in the original model. I then checked the displacement and stress results to see how they compared.
First, we have an organic looking shape that is mostly faithful to the results from the topological optimization run. This image is from ANSYS SpaceClaim, after a few minutes of ‘digital filing and sanding’ work on the STL faceted geometry output from ANSYS Mechanical.
This shows the resulting deflection from this first, ‘organic’ candidate:
The next candidate is one where more traditional looking solid geometry was created in SpaceClaim, using the topological optimization result as a guide. This is what it looks like:
This is the same configuration, but showing it in place within the saxophone bell and body model in ANSYS SpaceClaim:
Next, here is the deformation result for our simple loading condition using this second geometry configuration:
The third and final design candidate uses the second set of geometry as a starting point, and then adds a bit of style while still maintaining the topological optimization shape as an overall guide. Here is this third candidate in ANSYS SpaceClaim:
Here are is the resulting displacement distribution using this design:
This shows the maximum principal stress distribution within the brace for this candidate:
Again, I want to emphasize that this was a simple example and there are other considerations that could have been included, such as loading conditions other than acceleration due to gravity. Also, while it’s simple to include modal analysis results, in the interest of brevity I have not included them here. The main point is that topological optimization is a tool available within ANSYS Mechanical using the ACT extension that’s available for download on the customer portal. This is yet another tool available to us within our ANSYS simulation suite. It is my hope that you will also explore what can be done with this tool.
Regarding this effort, clearly a next step would be to 3D print one or more of these designs and test it out for real. Time permitting, we’ll give that a try at some point in the future.
The CUBE machines that I used in this ANSYS Test Case represent a fine balance based on price, performance and ANSYS HPC licenses used.
Click Here for more information on the engineering simulation workstations and clusters designed in-house at PADT, Inc.. PADT, Inc. is happy to be a premier re-seller and dealer of Supermicro hardware.
ANSYS Benchmark Test Case Information.
ANSYS HPC Licensing Packs required for this benchmark
I used (2) HPC Packs to unlock all 32 cores.
Please contact your local ANSYS Software Sales Representative for more information on purchasing ANSYS HPC Packs. You too may be able to speed up your solve times by unlocking additional compute power!
What is a CUBE? For more information regarding our Numerical Simulation workstations and clusters please contact our CUBE Hardware Sales Representative at SALES@PADTINC.COM Designed, tested and configured within your budget. We are happy to help and to listen to your specific needs.
Figure 1 – ANSYS benchmark data from three excellent machines.
Static Nonlinear Structural
Number of Degrees of Freedom
Click Here for more information on the ANSYS Mechanical test cases. The ANSYS website has great information pertaining to the benchmarks that I am looking into today.
Pro Tip –>Lastly, please check out this article by Greg Corke one of my friends at ANSYS, Inc. I am using the ANSYS benchmark data fromthe Lenovo Thinkstation P910 as a baseline for my benchmark data. Enjoy Greg’s article here!
The CPU Information
The benchmark data is derived off of the running through the BGA (sp-5) ANSYS test case. CPU’s and how they perform with one of the very latest ANSYS releases, ANSYS Release 17.1.
When running on a machine with a Linux operating system, it is not uncommon for users to want to run from the command line or with a shell script. To do this you need to know where the actual executable files are located. Based on a request from a customer, we have tried to coalesce the major ANSYS product executables that can be run via command line on Linux into a single list:
In this post, I discuss six challenges that make the modeling of 3D printed cellular structures (such as honeycombs and lattices) a non-trivial matter. In a following post, I will present how some of these problems have been addressed with different approaches.
At the outset, I need to clarify that by modeling I mean the analytical representation of material behavior, primarily for use in predictive analysis (simulation). Here are some reasons why this is a challenging endeavor for 3D printed cellular solids – some of these reasons are unique to 3D printing, others are a result of aspects that are specific to cellular solids, independent of how they are manufactured. I show examples with honeycombs since that is the majority of the work we have data for, but I expect that these ideas apply to foams and lattices as well, just with varying degrees of sensitivity.
1. Complex Geometry with Non-Uniform Local Conditions
I state the most well-appreciated challenge with cellular structures first: they are NOT fully-dense solid materials that have relatively predictable responses governed by straightforward analytical expressions. Consider a dogbone-shaped specimen of solid material under tension: it’s stress-strain response can be described fairly well using continuum expressions that do not account for geometrical features beyond the size of the dogbone (area and length for stress and strain computations respectively). However, as shown in Figure 1, such is not the case for cellular structures, where local stress and strain distributions are non-uniform. Further, they may have variable distributions of bending, stretching and shear in the connecting members that constitute the structure. So the first question becomes: how does one represent such complex geometry – both analytically and numerically?
2. Size Effects
A size effect is said to be significant when an observed behavior varies as a function of the size of the sample whose response is being characterized even after normalization (dividing force by area to get stress, for example). Here I limit myself to size effects that are purely a mathematical artifact of the cellular geometry itself, independent of the manufacturing process used to make them – in other words this effect would persist even if the material in the cellular structure was a mathematically precise, homogeneous and isotropic material.
It is common in the field of cellular structure modeling to extract an “effective” property – a property that represents a homogenized behavior without explicitly modeling the cellular detail. This is an elegant concept but introduces some practical challenges in implementation – inherent in the assumption is that this property, modulus for example, is equivalent to a continuum property valid at every material point. The reality is the extraction of this property is strongly dependent on the number of cells involved in the experimental characterization process. Consider experimental work done by us at PADT, and shown in Figure 2 below, where we varied both the number of axial and longitudinal cells (see inset for definition) when testing hexagonal honeycomb samples made of ULTEM-9085 with FDM. The predicted effective modulus increases with increasing number of cells in the axial direction, but reduces (at a lower rate) for increasing number of cells in the longitudinal direction.
This is a significant challenge and deserves a full form post to do justice (and is forthcoming), but the key to remember is that testing a particular cellular structure does not suffice in the extraction of effective properties. So the second question here becomes: what is the correct specimen design for characterizing cellular properties?
3. Contact Effects
In the compression test shown in the inset in Figure 2, there is physical contact between the platen and the specimen that creates a local effect at the top and bottom that is different from the experience of the cells closer the center. This is tied to the size effect discussed above – if you have large enough cells in the axial direction, the contribution of this effect should reduce – but I have called it out as a separate effect here for two reasons: Firstly, it raises the question of how best to design the interface for the specimen: should the top and bottom cells terminate in a flat plate, or should the cells extend to the surface of contact (the latter is the case in the above image). Secondly, it raises the question of how best to model the interface, especially if one is seeking to match simulation results to experimentally observed behavior. Both these ideas are shown in Figure 3 below. This also has implications for product design – how do we characterize and model the lattice-skin interface? As such, independent of addressing size effects, there is a need to account for contact behavior in characterization, modeling and analysis.
4. Macrostructure Effects
Another consideration related to specimen design is demonstrated in an exaggerated manner in the slowed down video below, showing a specimen flying off the platens under compression – the point being that for certain dimensions of the specimen being characterized (typically very tall aspect ratios), deformation in the macrostructure can influence what is perceived as cellular behavior. In the video below, there is some induced bending on a macro-level.
5. Dimensional Errors
While all manufacturing processes introduce some error in dimensional tolerances, the error can have a very significant effect for cellular structures – a typical industrial 3D printing process has tolerances within 75 microns (0.003″) – cellular structures (micro-lattices in particular) very often are 250-750 microns in thickness, meaning the tolerances on dimensional error can be in the 10% and higher error range for thickness of these members. This was our finding when working with Fused Deposition Modeling (FDM), where on a 0.006″ thick wall we saw about a 10% larger true measurement when we scanned the samples optically, as shown in Figure 4. Such large errors in thickness can yield a significant error in measured behavior such as elastic modulus, which often goes by some power to the thickness, amplifying the error. This drives the need for some independent measurement of the manufactured cellular structure – made challenging itself by the need to penetrate the structure for internal measurements. X-ray scanning is a popular, if expensive approach. But the modeler than has the challenge of devising an average thickness for analytical calculations and furthermore, the challenge of representation of geometry in simulation software for efficient analysis.
6. Mesostructural Effects
The layerwise nature of Additive Manufacturing introduces a set of challenges that are somewhat unique to 3D Printed parts. Chief among these is the resulting sensitivity to orientation, as shown for the laser-based powder bed fusion process in Figure 5 with standard materials and parameter sets. Overhang surfaces (unsupported) tend to have down-facing surfaces with different morphology compared to up-facing ones. In the context of cellular structures, this is likely to result in different thickness effects depending on direction measured.
For the FDM process, in addition to orientation, the toolpaths that effectively determine the internal meso-structure of the part (discussed in a previous blog post in greater detail) have a very strong influence on observed stiffness behavior, as shown in Figure 6. Thus orientation and process parameters are variables that need to be comprehended in the modeling of cellular structures – or set as constants for the range of applicability of the model parameters that are derived from a certain set of process conditions.
Modeling cellular structures has the above mentioned challenges – most have practical implications in determining what is the correct specimen design – it is our mission over the next 18 months to address some of these challenges to a satisfactory level through an America Makes grant we have been awarded. While these ideas have been explored in other manufacturing contexts, much remains to be done for the AM community, where cellular structures have a singular potential in application.
In future posts, I will discuss some of these challenges in detail and also discuss different approaches to modeling 3D printed cellular structures – they do not always address all the challenges here satisfactorily but each has its pros and cons. Until then, feel free to send us an email at firstname.lastname@example.org citing this blog post, or connect with me on LinkedIn so you get notified whenever I write a post on this, or similar subjects in Additive Manufacturing (1-2 times/month).
There is nothing better than seeing the powerful and interesting way that other engineers are using the same tools you use. That is why ANSYS, Inc. and PADT teamed up on Thursday to hold an “ANSYS Arizona Innovation Conference” at ASU SkySong where users could come to share and learn.
The day kicked off with Andy Bauer from ANSYS welcoming everyone and giving them an update on the company and some general overarching direction for the technology. Then Samir Rida from Honeywell Aerospace gave a fantastic keynote sharing how simulation drive the design of their turbine engines. As a former turbine engine guy, I found it fascinating and exciting to see how accurate and detailed their modeling is.
Next up was my talk on the Past, Present, and Future of simulation for product development. The point of the presentation was to take a step back and really think about what simulation is, what we have been doing, and what it needs to look at in the future. We all sort of agreed that we wanted voice activation and artificial intelligence built in now. If you are interested, you can find my presentation here: padt-ansys-innovation-az-2016.pdf.
After a short break ANSYS’s Sara Louie launched into a discussion on some of the new Antenna Systems modeling capabilities, simulating multiple physics and large domains with ANSYS products. The ability to model the entire interaction of an antenna including large environments was fascinating.
Lunchtime discussions focused on the presentations in the morning as well as people sharing what they were working on.
The afternoon started with a review by Hoang Vinh of ANSYS of the ANSYS AIM product. This was followed by customer presentations. Both Galtronics and ON Semiconductor shared how they drive the design of their RF systems with ANSYS HFSS and related tools. Then Nammo Talley shared how they incorporated simulation into their design process and then showed an example of a projectile redesign from a shoulder launched rocket that was driven by simulation in ANSYS CFX. They had the added advantage of being able to show something that blows up, always a crowd pleaser.
Another break was followed by a great look at how Ping used CFD to improve the design of one of their drivers. They used simulation to understand the drag on the head through an entire swing and then add aerodynamic features that improved the performance of the club significantly. Much of the work is actually featured in an ANSYS Advantage article.
We wrapped things up with an in depth technical look at Shock and Vibration Analysis using ANSYS Mechanical and Multiphysics PCB Analysis with the full ANSYS product suite.
The best part of the event was seeing how all the different physics in ANSYS products were being used and applied in different industries. WE hope to have similar events int he future so make sure you sign up for our mailings, the “ANSYS – Software Information & Seminars” list will keep you in the loop.
After three years on the market and signs that sales were increasing year over year, we decided it was time to go through our popular training book “Introduction to the ANSYS Parametric
Design Language (APDL)” and make some updates and reformat it so that it can be published as a Kindle e-book. The new Second Edition includes two additonal chapters: APDL Math and Using APDL with ANSYS Mechanical. The fact that we continue to sell more of these useful books is a sign that APDL is still a vibrant and well used language, and that others out there find power in its simplicity and depth.
This book started life as a class that PADT taught for many years. Then over time people asked if they could buy the notes. And then they asked for a real book. The bulk of the content came from Jeff Strain with input from most of our technical staff. Much of the editing and new content was done by Susanna Young and Eric Miller.
Here is the Description from Amazon.com:
The definitive guide to the ANSYS Parametric Design Language (APDL), the command language for the ANSYS Mechanical APDL product from ANSYS, Inc. PADT has converted their popular “Introduction to APDL” class into a guide so that users can teach themselves the APDL language at their own pace. Its 14 chapters include reference information, examples, tips and hints, and eight workshops. Topics covered include:
– User Interfacing
– Program Flow
– Retrieving Database Information
– Arrays, Tables, and Strings
– Importing Data
– Writing Output to Files
– Menu Customization
– APDL Math
– Using APDL in ANSYS Mechanical
At only $75.00 it is an investment that will pay for itself quickly. Even if you are an ANSYS Mechanical user, you can still benefit from knowing APDL, allowing you to add code snippets to your models. We have put some images below and you can also learn more here or go straight to Amazon.com to purchase the paperback or Kindle versions.
ANSYS Mechanical allows you to specify settings for load steps one at a time. Most users don’t know that you can change settings for any combination of load steps using the selection of the load step graph. PADT’s Joe Woodward shows you how in this short but informative video.
Some of you have probably already noticed, but ANSYS Mechanical licenses have some changes at version 17. First, the license that for years has been known as ANSYS Mechanical is now known as ANSYS Mechanical Enterprise. Further, ANSYS, Inc. has enabled significantly more functionality with this license at version 17 than was available in prior versions. Note that the license task in the ANSYS license files, ‘ansys’ has not changed.
16.2 and Older (task)
ANSYS Mechanical (ansys)
ANSYS Mechanical Enterprise (ansys)
The 17.0 ANSYS License Manager unlocks additional capability with this license, in addition to the existing Mechanical structural/thermal abilities. Previously, each of these tools used to be an additional cost. The change includes other “Mechanical-” licenses: e.g. Mech-EMAG, Mech CFD. The new tools enabled with ANSYS Mechanical Enterprise licenses at version 17.0 are:
Rigid Body Dynamics
Composite PrepPost (ACP)
ANSYS Customization Suite
Additionally, at version 17.1 these tools are included as well:
These changes do not apply to the lower level licenses, such as ANSYS Structural and Professional. In fact, these licenses are moving to ‘legacy’ mode at version 17. Two newer products now slot below Mechanical Enterprise. These newer products are ANSYS Mechanical Premium and ANSYS Mechanical Pro. We won’t explain those products here, but your local ANSYS provider can give you more information on these two if needed.
Getting back to the additional capabilities with Mechanical Enterprise, these become available once the ANSYS 17.0 and/or the ANSYS 17.1 license manager is installed. This assumes you have a license file that is current on TECS (enhancements and support). Also, a new license task is needed to enable Simplorer Entry.
Ignoring Simplorer Entry for the moment, once the 17.0/17.1 license manager is installed, the single Mechanical Enterprise license task (ansys) now enables several different tools. Note that:
Multiple tool windows can be open at once
g. ANSYS Mechanical and SpaceClaim
Only one can be “active” at a time
If solving, can’t edit geometry in SpaceClaim
Capabilities are then available in older versions, where applicable, once the 17.0/17.1 license manager is installed
Here is a very brief summary of these newly available capabilities:
Runs in the Mechanical window
Can calculate fatigue lives for ‘simple’ products (linear static analysis)
Constant amplitude, proportional loading
Variable amplitude, proportional loading
Constant amplitude, non-proportional loading
Constant amplitude, proportional loading
Activated by inserting the Fatigue Tool in the Mechanical Solution branch
Postprocess fatigue lives as contour plots, etc.
Requires fatigue life data as material properties
Rigid Body Dynamics:
Runs in the Mechanical window
ANSYS, Inc.-developed solver using explicit time integration, energy conservation
Use when only concerned about motion due to joints and contacts
To determine forces and moments
Activated via Rigid Dynamics analysis system in the Workbench window
Runs in the Mechanical window
Utilizes the Autodyn solver
For highly nonlinear, short duration structural transient problems
Drop test simulations, e.g.
Activated via Explicit Dynamics analysis system in the Workbench window
Composite PrepPost (ACP):
Tools for preparing composites models and postprocessing composites solutions
Define composite layup
Fiber Directions and Orientations
Optimize composite design
Activated via ACP (Pre) and ACP (Post) component systems in the Workbench window
Over the past two academic semesters (2015/16), I had the opportunity to work closely with six senior-year undergraduate engineering students from the Arizona State University (ASU), as their industry adviser on an eProject (similar to a Capstone or Senior Design project). The area we wanted to explore with the students was in 3D printed lattice structures, and more specifically, address the material modeling aspects of these structures. PADT provided access to our 3D printing equipment and materials, ASU to their mechanical testing and characterization facilities and we both used ANSYS for simulation, as well as a weekly meeting with a whiteboard to discuss our ideas.
While there are several efforts ongoing in developing design and optimization software for lattice structures, there has been little progress in developing a robust, validated material model that accurately describes how these structures behave – this is what our eProject set out to do. The complex internal meso- and microstructure of these structures makes them particularly sensitive to process variables such as build orientation, layer thickness, deposition or fusion width etc., none of which are accounted for in models for lattice structures available today. As a result, the use of published values for bulk materials are not accurately predictive of true lattice structure behavior.
In this work, we combined analytical, experimental and numerical techniques to extract and validate material parameters that describe mechanical response of lattice structures. We demonstrated our approach on regular honeycomb structures of ULTEM-9085 material, made with the Fused Deposition Modeling (FDM) process. Our results showed that we were able to predict low strain responses within 5-10% error, compared to 40-60% error with the use of bulk properties.
This work is to be presented in full at the upcoming RAPID conference on May 18, 2016 (details at this link) and has also been accepted for full length paper submission to the SFF Symposium. We are also submitting a research proposal that builds on this work and extends it into more complex geometries, metals and failure modeling. If you are interested in the findings of this work and/or would like to collaborate, please meet us at RAPID or send us an email (email@example.com).
With the introduction of the new ANSYS Mechanical Enterprise, many add-on products that had to be purchased separate, are now included. In these webinars PADT’s engineers will provide an overview of the key applications that users now have easy access to.
Each product will be reviewed by one of PADT’s engineers. The will share the functionality of each tool, discuss some lessons we have learned in using and supporting each tool, and provide a short demonstration. Each session will have time for Questions and Answers.
Sign up for the one you want, or all three. Everyone that registers will receive a link to the recording and to a copy of the slides. So register even if you can not make the specific dates.
Here are the times and links to register:
Overview of ANSYS Rigid Body Dynamics (RBD) and ANSYS Explicit STR
May 19, 2016 (Thu)
11:00 am MST & PDT / 12:00 pm MDT