All Things Ansys 066: Simulation Automation & Optimization management with Ansys optiSLang

 

Published on: June 29th, 2020
With: Eric Miller & Josh Stout
Description:  

In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s systems application & support engineer Josh Stout to look at the optimization tool optiSLang. This tool helps automate simulation and optimization activities across various solution areas, such as autonomy, electrification, digital twins, and more, as well as how it enables users to capitalize on the benefits of enterprise simulation management.

If you would like to learn more, you can view the product brochure here: https://www.ansys.com/-/media/ansys/corporate/resourcelibrary/brochure/optislang-brochure.pdf.

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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Combining Simulation with Additive Manufacturing to Optimize Product Design – Webinar

Advatech Pacific, a Phoenix-based aerospace and defense contractor founded in 1995, works to change the way engineering is conducted for the better by incorporating innovative technologies into its customer’s workflow. Based on the success of previous projects, Advatech is a strong proponent of using high-end simulation software such as Ansys to identify and evaluate the fine details of massive multi-body mechanical systems, whether through simple static analyses or tightly-coupled multiphysics computations.

Implementing additive manufacturing as an additional way to improve system design presented opportunities to cut back on tooling costs and reduce lead time for several candidate turbine-engine parts. Doing so would also alleviate the challenge of reproducing complex castings, a problem made increasingly difficult by the fact that many of the original casting providers are no longer in business.

Join PADT’s Lead Mechanical Engineer Doug Oatis, and Advatech Pacific’s Engineering Manager Matt Humrick for a discussion on Ansys tools with regards to additive manufacturing & topology optimization, and how Advatech Pacific was able to use them to drastically improve the efficiency of their design and manufacturing process.

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A Simple Adjustment to Fix a Contact Convergence Problem in Ansys Mechanical

As I write this from home during the Covid-19 crisis, I want to assure you that PADT is conscious of many others working from home while using Ansys software as well.  We’re trying to help with those who may be struggling with certain types of models.  In this posting, I’ll talk about a contact convergence problem in Ansys Mechanical.  I’ll discuss steps we can take to identify the problem and overcome it, as well as a simple setting to make which dramatically helped in this case. 

The geometry in use here is a fairly simple assembly from an old training class.  It’s a wheel or roller held by a housing, which is in turn bolted to something to hold it in place.

A close up of a device

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The materials used are linear properties for structural steel.  The loading consists of a bearing load representing a downward force applied by some kind of strap or belt looped over the wheel, along with displacement constraints on the back surfaces and around the bolt holes, as shown in the image below.  The flat faces on the back side have a frictionless support applied (allows in plane sliding only), while the circular faces where bolt heads and washers would be are fully constrained with fixed supports.

A close up of a logo

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As is always the case in Ansys Mechanical, contact pairs are created wherever touching surfaces in the assembly are detected.  The default behavior for those contact pairs is bonded, meaning the touching surfaces can neither slide nor separate.  We will make a change to the default for the wheel on its shaft, though, changing the contact behavior from bonded to frictional.  The friction coefficient defined was 0.2.  This represents some resistance to sliding.  Unlike bonded contact in which the status of the contact pair cannot change during the analysis, frictional contact is truly nonlinear behavior, as the stiffness of the contact pair can change as deflection changes. 

This shows the basic contact settings for the frictional contact pair:

A screenshot of a cell phone

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At this point, we attempt a solve.  After a while, we get an error message stating, “An internal solution magnitude limit was exceeded,” as shown below.  What this means is that our contact elements are not working as expected, and part of our structure is trying to fly off into space.  Keep in mind in a static analysis there are no inertia effects, so an unconstrained body is truly unconstrained.

At this point, the user may be tempted to start turning multiple knobs to take care of the situation.  Typical things to adjust for contact convergence problems are adding more substeps, reducing contact stiffness, and possibly switching to the unsymmetric solver option when frictional contact is involved.  In this case, a simple adjustment is all it takes to get the solution to easily converge. 

Another thing we might do to help us is to insert a Contact Tool in the Connections branch and interrogate the initial contact status:

This shows us that our frictional contact region is actually not in initial contact but has a gap.  There are multiple techniques available for handling this situation, such as adding weak springs, running a transient solution (computationally expensive), starting with a displacement as a load and then switching to a force load, etc.  However, if we are confident that these parts actually SHOULD be initially touching but are not due to some slop in the CAD geometry, there is a very easy adjustment to handle this.

The Simple Adjustment That Gets This Model to Solve Successfully

Knowing that the parts should be initially in contact, one simple adjustment is all that is needed to close the initial gap and allow the simulation to successfully solve.  The adjustment is to set the Interface Treatment in the Contact Details for the contact region in question to Adjust to Touch:

This change automatically closes the initial gap and, in this case, allows the solution to successfully solve very quickly. 

For your models, if you are confident that parts should be in initial contact, you may also find that this adjustment is a great aid in closing gaps due to small problems in the CAD geometry.  We encourage you to test it out.

An Ansys optiSLang Overview and Optimization Example with Ansys Icepak

Ansys optiSLang is one of the newer pieces of software in the Ansys toolkit that was recently acquired along with the company Dynardo. Functionally, optiSLang provides a flexible top-level platform for all kinds of optimization. It is solver agnostic, in that as long as you can run a solver through batch files and produce text readable result files, you can use said solver with optiSLang. There are also some very convenient integrations with many of the Ansys toolkit solvers in addition to other popular programs. This includes AEDT, Workbench, LS-DYNA, Python, MATLAB, and Excel, among many others.

While the ultimate objective is often to simply minimize or maximize a system output according to a set of inputs, the complexity of the problem can increase dramatically by introducing constraints and multiple optimization goals. And of course, the more complicated the relationships between variables are, the harder it gets to adequately describe them for optimization purposes.

Much of what optiSLang can do is a result of fitting the input data to a Metamodel of Optimal Prognosis (MOP) which is a categorical description for the specific metamodels that optiSLang uses. A user can choose one of the included models (Polynomial, Moving Least Squares, and Ordinary Kriging), define their own model, and/or allow optiSLang to compare the resulting Coefficients of Prognosis (COP) from each model to choose the most appropriate approach.

The COP is calculated in a similar manner as the more common COD or R2 values, except that it is calculated through a cross-validation process where the data is partitioned into subsets that are each used only for the MOP calculation or the COP calculation, not both. For this reason, it is preferred as a measure for how effective the model is at predicting unknown data points, which is particularly valuable in this kind of MOP application.

This whole process really shows where optiSLang’s functionality shines: workflow automation. Not only does optiSLang intelligently select the metamodel based on its applicability to the data, but it can also apply an adaptive method for the improvement of the MOP. It will suggest an automatic sampling method based on the number of problem variables involved, which can then be applied towards refining either the global COP or the minimum local COP. The automation of this process means that once the user has linked optiSLang to a solver with appropriate inputs/outputs and defined the necessary run methodology for optimization, all that is left is to click a button and wait.

As an example of this, we will run through a test case that utilizes the ability to interface with Ansys EDT Icepak.

Figure 1: The EDT Icepak project model.

For our setup, we have a simple board mounted with bodies representative of 3 x 2 watt RAM modules, and 2 x 10 watt CPUs with attached heatsinks. The entire board is contained within an air enclosure, where boundary conditions are defined as walls with two parametrically positioned circular inlets/outlets. The inlet is a fixed mass flow rate surface and the outlet is a zero-pressure boundary. In our design, we permit the y and z coordinates for the inlet and outlet to vary, and we will be searching for the configuration that minimizes the resulting CPU and RAM temperatures.

The optiSLang process generally follows a series of drag-and-drop wizards. We start with the Solver Wizard which guides us through the options for which kind of solver is being used: text-based, direct integrations, or interfaces. In this case, the Icepak project is part of the AEDT interface, so optiSLang will identify any of the parameters defined within EDT as well as the resulting report definitions.  The Parametric Solver System created through the solver wizard then provides the interfacing required to adjust inputs while reading outputs as designs are tested and an MOP is generated.

Figure 2: Resulting block from the Solver wizard with parameters read in from EDT.

Once the parametric solver is defined, we drag and drop in a sensitivity wizard, which starts the AMOP study.  We will start with a total of 100 samples; 40 will be initial designs, and 60 will be across 3 stages of COP refinement with all parameter sets sampled according to the Advanced Latin Hypercube Sampling method.

Figure 3: Resulting block from the Sensitivity wizard with Advanced Latin Hypercube Sampling.

The results of individual runs are tabulated and viewable as the study is conducted, and at the conclusion, a description of the AMOP is provided with response surfaces, residual plots, and variable sensitivities. For instance, we can see that by using these first 100 samples, a decent metamodel with a COP of 90% is generated for the CPU temperature near the inlet. We also note that optiSLang has determined that none of the responses are sensitive to the ‘y’ position of the outlet, so this variable is automatically freed from further analysis.

Figure 4: MOP surface for the temperature of Chip1, resulting from the first round of sampling.

 If we decide that this CoP, or that from any of our other outputs, is not good enough for our purposes, optiSLang makes it very easy to add on to our study. All that is required is dragging and dropping a new sensitivity wizard onto our previous study, which will automatically load the previous results in as starting values. This makes a copy of and visually connects an output from the previous solver block to a new sensitivity analysis on the diagram, which we can then be adjusted independently.

For simplicity and demonstration’s sake, we will add on two more global refinement iterations of 50 samples each. By doing this and then excluding 8 of our 200 total samples that appear as outliers, our “Chip1” CoP can be improved to 97%.

Figure 5: A refined MOP generated by including a new Sensitivity wizard.

Now that we have an MOP of suitable predictive power for our outputs of interest, we can perform some fast optimization. By initially building an MOP based on the overall system behavior, we are now afforded some flexibility in our optimization criteria. As in the previous steps, all that is needed at this point is to drag and drop an optimization wizard onto our “AMOP Addition” system, and optiSLang will guide us through the options with recommendations based on the number of criteria and initial conditions.

In this case, we will define three optimization criteria for thoroughness: a sum of both chip temperatures, a sum of all RAM temperatures, and an average temperature rise from ambient for all components with double weighting applied to the chips. Following the default optimization settings, we end up with an evolutionary algorithm that iterates through 9300 samples in about 14 minutes – far and away faster than directly optimizing the Icepak project. What’s more, if we decide to adjust the optimization criteria, we’ll only need to rerun this ~14 minute evolutionary algorithm.

What we are most interested in for this example are the resulting Pareto fronts which give us a clear view of the tradeoffs between each of our optimization criteria. Each of the designs on this front can easily be selected through the interface, and their corresponding input parameters can be accessed.

Figure 6: Pareto front of the “Chipsum” and “TotalAve” optimization criteria.

Scanning through some of these designs also provides a very convenient way to identify which of our parameters are limiting the design criteria. Two distinct regions can be identified here: the left region is limited by how close we are allowing the inlet fan to be to the board, and the right region is limited by how close to the +xz corner of our domain the outlet vent can be placed. In a situation where these parameters were not physically constrained by geometry, this would be a good opportunity to consider relaxing parameter constraints to further improve our optimization criteria. 

As it is, we can now choose a design based on this Pareto front to verify with the full solver. After choosing a point in the middle of the “Limited by outlet ‘z’” zone, we find that our actual “ChipSum” is 73.33 vs. the predicted 72.78 and the actual “TotalAve” is 17.82 vs. the predicted 17.42. For this demonstration, we consider this small error as satisfactory, and a snapshot of the corresponding Icepak solution is shown below.

Figure 7: The Icepak solution of the final design. The inlet vent is aligned with the outlet side’s heatsink, and the outlet vent is in the corner nearest the heatsink. Primary flow through the far heatsink is maximized, while a strong recirculating flow is produced around the front heatsink.

The accuracy of these results is of course dependent not only on how thoroughly we constructed the MOP, but also the accuracy of the 3D solution; creating mesh definitions that remain consistently accurate through parameterized geometry changes can be particularly tricky. Though, with all of this considered, optiSLang provides a great environment for not only managing optimization studies, but displaying the results in such a way that you can gain an improved understanding of the interaction between input/output variables and their optimization criteria.

All Things Ansys 064: The Largest Virtual Engineering Simulation Trade Show Ever – Ansys Simulation World

 

Published on: June 1st, 2020
With: Eric Miller & Lynn Ledwith
Description:  

In this episode your host and Co-Founder of PADT, Eric Miller is joined by Ansys CMO Lynn Ledwith, for a look at their digital trade show, Simulation world taking place Wednesday June 10th through Thursday June 11th.

The largest engineering simulation virtual event in the world, this event is a free online conference designed to inspire and educate executives, engineers, R&D, and manufacturing professionals about the transformative powers of engineering simulation and Ansys.

If you would like to learn more, visit simulation-world.com or register for free via https://bit.ly/2XjxrCN

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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Fighting COVID-19 with Ansys Simulation – Webinar

Simulation has been and continues to be a powerful tool for helping to drive innovation in the medical industry. Everything from medical devices, to hospital equipment, and even pharmaceutical and clinical practices can benefit from the introduction of simulation technology. This is true now more than ever, as the we all are facing such turbulent times.

During the COVID-19 pandemic, Ansys is striving to combat the spread of the coronavirus, by backing the ongoing initiatives of customers and partners working in the medical sphere. In order to support healthcare professionals, policy makers, and communities around the world in this endeavor, Ansys is sharing key insights gained from their own analyse, along with those of partners and other collaborators, regarding how to prevent future spread, and treat those already effected by the virus.

Join PADT’s Co-founder and Principal engineer Eric Miller, along with Marc Horner, Principal Healthcare Engineer at Ansys, for a discussion on what the company is doing to combat the virus, as well as a look at some models that effectively illustrate how the tools are being used.

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Advanced Capabilities to Consider when Simulating Blow Molding in Ansys Polyflow or Discovery AIM

Ansys Polyflow is a Finite Element CFD solver with unique capabilities that enable simulation of complex non-Newtonian flows seen in the polymer processing industry. In recent releases, Polyflow has included templates to streamline two of its most common use cases: blow molding and extrusion. Similarly, Ansys Discovery AIM offers a modern user interface that guides users through blow molding and extrusion workflows while still using the proven Polyflow solver under the hood. It is not uncommon for engineers to be unsure about which tool to pursue for their specific application. In this article, I will focus on the blow molding workflow. More specifically, I will point out three features in Polyflow that have not yet been incorporated into Discovery AIM:

  1. The PolyMat curve fitting tool to derive viscoelasticity model input parameters from test data
  2. Automatic parison thickness mapping onto an Ansys Mechanical shell mesh
  3. Parison Programming to optimize parison thickness subject to final part thickness constraints

Keep in mind that either tool will get the job done in most applications, so let us first quickly review some of the core features of blow molding simulations that are common to Polyflow and AIM:

  • Parison/Mold contact detection
  • 3-D Shell Lagrangian automatic remeshing
  • Generalized Newtonian viscosity models
  • Temperature dependent and multi-mode integral viscoelastic models
  • Time dependent mold pressure boundary conditions
  •  Isothermal or non-isothermal conditions

For demonstration purposes, I modeled a sweet submarine toy in SpaceClaim. Unfortunately, I think it will float, but let’s move past that for now.  

Figure 1: Final Submarine shape (Left), Top View of Mold+ Parison (Top Left), Side View of Mold+Parison (Bottom Right)

At this point, you could proceed with Discovery AIM or with Polyflow without any re-work. I’lll proceed with the Polyflow Blow Molding workflow to point out the features currently only available in Polyflow.

PolyMat Curve Fitting Tool

With the blow molding template, you can select whether to treat the parison as isothermal or non-isothermal and whether to model it as general Newtonian or viscoelastic. Suppose we would like to model viscoelasticity with the KBKZ integral viscoelastic model because we were interested in capturing strain hardening as the parison is stretched. The inputs to the KBKZ model are viscosity and relaxation times for each mode. If they are known, the user can input the values directly. This is possible in Discovery AIM as well. However, the PolyMat tool is unique to Polyflow. PolyMat is a built-in curve fitting tool that helps generate input parameters for the various viscosity model available in Polyflow using material data. This is particularly useful when you do not explicitly have the inputs for a viscoelastic model, but perhaps you have other test data such as oscillatory and capillary rheometry data. In this case I have with the loss modulus, storage modulus and shear viscosity for a generic high density polyethylene (HDPE) material. For this material, four modes are enough to anchor the KBKZ model to the data as shown below. We can then load the viscosity/relaxation time into Polyflow and continue. 

Figure 2: Curve Fitting of G’(Ω),G’’(Ω),η() [Left], KBKZ Viscoelastic Model inputs (Right)

The main output of the simulation is the final parison thickness distribution. For this sweet submarine, the initial parison thickness is set to 3mm and the final thickness distribution is shown in the contour plot below.

Figure 3a: Animation of blow molding process

Figure 3b: Final Part Thickness Distribution

Thickness Mapping to Ansys Mechanical

The second Polyflow capability I’d like to point out is the ability to easily map the thickness distribution onto an Ansys mechanical shell mesh. You can map the thickness onto an Ansys Mechanical shell mesh by connecting the polyflow solution component to a structural model in workbench as shown below. The analogous work flow in AIM, would be to create a second simulation for the structural analysis, but you would be confined to specifying a constant thickness.

Figure 4: Polyflow – Ansys Mechanical Parison Thickness Mapping

In Ansys Mechanical, the mapping comes through within the geometry tree as shown below. The imported Data Transfer Summary is a good way to ensure the mapping behaves as expected. In this case we can see that 100% of the nodes were mapped and the thickness contours qualitatively match the Polyflow results in CFD -Post.

Figure 5: Imported Thickness in Ansys Mechanical

Figure 6: Thickness Data Transfer Summary

A force is applied normal to front face of the sail and simulated in Mechanical. The peak stress and deformation are shown below. The predicted stresses are likely acceptable for a toy, especially since my toy is a sweet submarine. Nonetheless, suppose that I was interested in reducing the deformation in the sail under this load condition by thickening the extruded parison. A logical approach would be to increase the initial parison thickness from 3mm to 4mm for example. Polyflow’s parison programming feature takes the guesswork out of the process. 

Figure 7: Clockwise from Top Left: Applied Load on Sail, Stress Distribution, total Deformation, Thickness Distribution

Parison Programming

Parison programming is an iterative optimization work flow within Polyflow for determining the extruded thickness distribution required to meet the final part thickness constraints. To activate it, you create a new post processor sub-task of type parison programming.   

Figure 8: Parison Programming Setup

The inputs to the optimization are straight forward. The only inputs that you typically would need to modify are the direction of optimization, width of stripes, and list of (X,h) pairs. The direction of optimization is the direction of extrusion which is X in this case. If the extruder can vary parison thickness along “stripes” of the parison, then Polyflow can optimize each stripe thickness. The list of (X,h) pairs serves as a list of constraints for the final part thickness where X is the location on the parison along the direction of extrusion and h is the final part thickness constraint.

Figure 9: Thickness Constraints for Parison Programming

In our scenario, the X,h pairs form a piecewise linear thickness distribution to constrain the area around the sail to have a 3.5mm thickness and 2mm everywhere else. After the simulation, Polyflow will write a csv file with to the output directory containing the initial thickness for each node for the next iteration. You will need to copy over the csv file from the output directory of iteration N to the input directory of iteration N+1. The good news is the optimization converges within 3-5 iterations.

Figure 10: Defining the Initial Thickness for the Next Parison Programming Iteration

Polyflow will print the parison strip thickness distribution for the next iteration in the .lst file. The plot below shows the thickness distribution from the first 3 iterations. Note from the charts below that the distribution converged by iteration 2; thus iteration 3 was not actually simulated. The optimized parison thickness distribution is also plotted in the contour plot below.

Figure 11: Optimized Parison Thickness (Top), Final Part Thickness (Bottom)

Figure 12: % of Elements At or Above Thickness Criteria

As a final check, we can evaluate how the modification to the parison thickness reduced the deformation of the submarine. The total deformation contour plot below confirms that the peak deformation decreased from 2mm to 0.8mm.

Figure 13: Total Deformation in Ansys Mechanical After Parison Programming

Summary

Ansys Discovery AIM is a versatile platform with an intuitive and modern user interface. While Aim has incorporated most of the blow molding simulation capabilities from Polyflow, some advanced functionality has not yet been brought into AIM. This article simulated the blow molding process of a toy submarine to demonstrate three capabilities currently only available in Polyflow: the PolyMat curve fitting tool, automatic parison thickness mapping to Ansys Mechanical, and parison programming. Engineers should consider whether any of these capabilities are needed in their application next time they are faced with the decision to create a blow mold simulation using Ansys Discovery AIM or Polyflow.

All Things Ansys 063: Fighting COVID-19 with Ansys Simulation

 

Published on: May 18th, 2020
With: Eric Miller, Thierry Marchal & Marc Horner
Description:  

In this episode your host and Co-Founder of PADT, Eric Miller is joined by two leaders in the Ansys response to COVID-19 – Thierry Marchal, Global Industry Director for Healthcare, Consumer Products & Construction, and Marc Horner, Principal Healthcare Engineer – for a discussion on what the company is doing to combat the spread of the virus, as well as give our listeners a more complete understanding regarding the specific applications that Ansys tools are being used for during this global pandemic.

If you would like to learn more about Ansys and their response to COVID-19, check out the following link: https://bit.ly/36bs8rR

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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Designing Better Rocket Engines with Ansys – Webinar

In 2017 Colorado based company Ursa Major Technologies put together an expert team of designers and engineers to realize its vision of providing the microsatellite industry with the best rocket engines in the business. Utilizing Ansys simulation software, additive manufacturing, and modernizing staged combustion, the company successfully designed and built two liquid oxygen and kerosene engines and has a third engine in development.

With Ansys, Ursa Major Technologies is accomplishing design goals faster and more efficiently than ever before. Using Finite Element Analysis (FEA), the company can run models with 30-40 unique parts to analyze entire turbo pumps in one simulation. Thrust analysis, which the company had previously done with 2D models, can now be done all in the Ansys CFX tool more cost-effectively.

Join PADT and Ursa Major Technologies for a brief overview of applications for Ansys in the aerospace industry, followed by an exploration of how they are using these simulation tools to better design and optimize the next generation of rocket engines.

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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!

All Things Ansys 062: Optimizing Materials Selection for Additive Manufacturing with Ansys Granta

 

Published on: May 4th, 2020
With: Eric Miller, Pam Waterman & Robert McCathren
Description:  

In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s Pam Waterman and Robert McCathren for a discussion on how Ansys Granta can be used to help optimize hardware selection for additive manufacturing. The Senvol Database details 1,000 AM machines and more than 850 compatible materials. Using this tool within Granta Selector, you can search and compare materials based on properties, type, or compatible machines.

If you would like to learn more about the Ansys tool and it’s applications for additive, check out our webinar on the topic here: https://bit.ly/2SAZN8G

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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Optimizing Materials Selection for Additive with ANSYS Granta – Webinar

There are hundreds of industrial AM machines and materials. New products come to market weekly, and picking the best option for a manufacturing or research project is a tough call. A wrong direction can be costly. This is where Ansys Granta and the Senvol Database come in handy. 

The Senvol Database details 1,000 AM machines and more than 850 compatible materials. Using this tool within Granta Selector, you can search and compare materials based on properties, type, or compatible machines. Identify and compare machines based on supported processes, manufacturer, required part size, cost, or compatible materials (and their properties). Quickly focus on the most likely routes to achieve project goals, save time and get new ideas as you research AM options.

Join PADT’s Application Engineer Robert McCathren for an overview of Ganta Material Selector, along with its importance and applications for those working with or interested in additive manufacturing.

Register Here

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!