Fasteners are one of the most common and fundamental engineering components we encounter.
Proper design of fasteners is so fundamental, every Mechanical Engineer takes a University course in which the proper design of these components is covered (or at least a course in which the required textbook does so).
With recent increases in computational power and ease in creating and solving finite element models, engineers are increasingly tempted to simulate their fasteners or fastened joints in order to gain better insights into such concerns as thread stresses
In what follows, PADT’s Alex Grishin demonstrates a basic procedure for doing so, assess the cost/benefits of doing so, and to lay the groundwork for some further explorations in part 2.
David Mastel, Joe Woodword, Manoj Mahendran, Matt Sutton, Michael Griesi, Tom Chadwick, Ted Harris, Eric Miller
In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s David Mastel, Joe Woodword, Manoj Mahendran, Matt Sutton, Michael Griesi, Tom Chadwick, and Ted Harris, for a discussion on what is new and improved in the recently released ANSYS 19.
In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s Senior CFD Engineer Tom Chadwick, and Simulation Support Manager Ted Harris for a discussion on convergence with both FEA and CFD solutions, as well as a look at some of their favorite hidden gems in the ANSYS tools set. Learn about some beneficial ANSYS capabiliites you may not be aware of!
This is the fourth installment in our review of all the different products and services PADT offers our customers. As we add more, they will be available here. As always, if you have any questions don’t hesitate to reach out to email@example.com or give us a call at 1-800-293-PADT.
The A in PADT actually stands for Analysis. Back in 1994 when the company was started, computer modeling for mechanical engineering was called Analysis. It was such an important part of what we wanted to achieve that we put it in the name. Unfortunately, Analysis was a bit to generic so the industry switched to Numerical Simulation, or simply simulation. In the 23 years since we started, analysis… sorry, simulation, has been not just a foundation for what PADT does for our customers, it has become a defacto tool in product development. Through it all there has been a dedicated group here that is focused on providing the best simulation as a service to customers around the world.
Driving Designs with Simulation
Many companies know about PADT with regards to simulation because we are an ANSYS Elite Channel Partner – selling and supporting the entire suite of ANSYS simulation tools in the Southwestern US. The success of simulation in the design and development of physical products is a direct result of the fact that these fantastic tools from ANSYS can be used to drive the design of products. This can be done in-house by companies designing the products, or outsourced to experts. And that is where PADT has come in for hundreds of customers around the world. The expertise we use to support and train on ANSYS products derives directly from our real world experience providing CFD, structural, thermal, electromagnetic, and multiphysics simulations to help those customers drive their product development.
For those not familiar with simulation, or who only use the basic tools embedded in CAD software as a quick check, understanding why it is so important hinges on understand what it really is. Numerical Simulation is a methodology where a physical product is converted into a computer model that represents its physical behavior. This behavior can be many different physics: stresses, vibration, fluid flow, temperature flow, high frequency electromagnetic radiation, sloshing of liquids, deformation during impact, piezoelectric response, heating from static electromagnetic waves, cooling from air flow. The list goes on and on. Pretty much anything you studied in physics can be modeled using a numerical simulation.
The process of doing the simulation consists of taking the physical object and breaking it into discrete chunks, often very small relative to the size of the object, so that equations can easily be written for each chunk that describes the physical behavior of that chunk relative to the chunks around it. Imagine writing equations for the fluid flow in a complicated valve housing, very hard to do. But if you break it up into about one million small polyhedrons, you can write an equation for flow in and out of each polyhedron. These equations are then assembled into a giant matrix and solved using linear algebra. That is why we need such large computers. We mostly use the world’s leading software for this, from ANSYS, Inc.
More than Building and Running Models
Knowing how to build and run finite element and CFD models is key to providing simulation as a service. PADT’s team averages over 18 years of experience and few people come close to their knowledge on geometry preparation, meshing, setting up loads and boundary conditions, leveraging the advantages of each solver, and post processing. That is a good starting point. But what really sets PADT apart is the understanding of how the simulation fits into product development, and how the information gathered from simulation can and should be used. Instead of providing a number or a plot, PADT’s experienced engineers deliver insight into the behavior of the products being simulated.
How each project is conducted is also something that customers keep coming back for. Nothing is ever “thrown over the wall” our passed through a “black box.” From quote through delivery of final report, PADT’s engineers work closely with the customer’s engineers to understand requirements, get to the heart of what the customer is looking for, and deliver useful and actionable information. And if you have your own in-house simulation team, we will work closely with them to help them understand what we did so they can add it to their capabilities. In fact, one of the most popular simulation services offered by PADT is automation of the simulation process with software tools written on top of ANSYS products. This is a fantastic way to leverage PADT’s experience and knowledge to make your engineers more efficient and capable.
Unparalleled Breadth and Depth
Based on feedback from our customers, the other area where PADT really stands out is in the incredible breadth and depth of capability offered. Whereas most service providers specialize in one type of simulation or a single industry, more than twenty years of delivering high-end simulation to evaluate hundreds of products has given PADT’s team a unique and special level of understanding and expertise. From fluid flow in aerospace cooling systems to electromagnetics for an antenna in a smart toy, a strong theoretical understanding is combined with knowledge about the software tools to apply the right approach to each unique problem.
No where is this breadth and depth exemplified than with PADT’s relationship with ANSYS, Inc. Since the company was founded, PADT engineers have worked closely with ANSYS development and product management to understand these powerful tools better and to offer their advice on how to make them better. And each time ANSYS, Inc. develops or acquires a new capability, that same team steps up and digs deep into the functionality that has been added. And when necessary, adding new engineers to the team to offer our customers the same expert access to these new tools.
The best way to understand why hundreds of companies, many of them large corporations that are leaders in their industry, come to PADT from around the world for their simulation services needs is to talk to us about your simulation services needs. Regardless of the industry or the physics, our team is ready to help you drive your product development with simulation. Contact us now to start the discussion.
Meshing is one of the most important aspects of a simulation process and yet it can be one of the most frustrating and difficult to get right. Whether you are using CAD based simulation tools or more powerful flagship simulation tools, there are different approaches to take when it comes to meshing complicated assemblies for structural or thermal analysis.
ANSYS has grown into the biggest simulation company globally by acquiring powerful technologies, but more importantly, integrating their capabilities into a single platform. This is true for meshing as well. Many of ANSYS’ acquisitions have come with several strong meshing capabilities and functionalities and ANSYS Workbench integrates all of that into what we call Workbench Meshing. It is a single meshing tool that incorporates a variety of global and local mesh operations to ensure that the user not only gets a mesh, but gets a good quality mesh without needing to spend a lot of time in the prep process. We’ll take a look at a couple examples here.
This is a Tractor Axle assembly that has 58 parts including bolts, gaskets and flanges. The primary pieces of the assembly also has several holes and other curved surfaces. Taking this model into Workbench Meshing yielded a good mesh even with default settings. From here by simply adding a few sizing controls and mesh methods we quickly get a mesh that is excellent for structural analysis.
Tractor Axle Geometry
Tractor Axle Default Mesh
Tractor Axle Refined Mesh
The assembly below, which is a model from Grabcad of a riveting machine, was taken directly into Workbench Meshing and a mesh was created with no user input. As you can see the model has 5,282 parts of varying sizes, shapes and complexity. Again without needing to make any adjustments, Workbench Meshing is able to mesh this entire geometry with 6.6 million elements in only a few minutes on a laptop.
Riveting Machine Default Mesh
Riveting Machine Default Mesh
The summary of the meshing cases are shown below:
# of Parts
# of Elements
# of Nodes
Tractor Axle Refined
5 Body Sizings
2 Local Mesh Methods
Characteristics of a robust meshing utility are:
Easy to use with enough power under the hood
Able to handle complex geometry and/or large number of parts
Quick and easy user specified mesh operations
Fast meshing time
ANSYS Meshing checks all of these boxes completely. It has a lot of power under the hood to handle large and/or complex geometry but makes it simple and easy for users to create a strong quality mesh for FEA analysis.
With thoroughly engineered components including the use of Finite Element Analysis (FEA), thermodynamics, heat transfer, and Computational Fluid Dynamics (CFD), PADT Startup Spotlight Velox Motorsports strives to produce aftermarket parts that can effectively outperform the factory components.
Join Velox Co-Owners Eric Hazen and Paul Lucas for a discussion on what they use ANSYS simulation software for and how they have benefited from it’s introduction into their manufacturing process.
This webinar will focus on two projects within which the engineers at Velox have see the impact of ANSYS, including:
Using Finite Element Analysis (FEA) to reverse engineer a Subaru fork, find the cause of failure and develop an improved replacement part.
Using Computational Fluid Dynamics (CFD) to rub a shape sensitivity study on Nissan GT R strakes, and develop a replacement that increases down-force without significantly increasing drag.
Fast, easy to use lightweighting for structural analysis is now only a few clicks away thanks to the introduction of Topology Optimization in ANSYS 18.
Engineers who use Finite Element Analysis (FEA) can reduce weight, materials, and cost without switching tools or environments. Along with this, Topology Optimization frees designers from constraints or preconceptions, helping to produce the best shape to fulfill their project’s requirements.
Topology Optimization also works hand-in-hand with Additive Manufacturing; a form of 3D printing where parts are designed, validated, and then produced by adding layers of material until the full piece is formed. Pairing the two simply allows users to carry out the trend of more efficient manufacturing through the entirety of their process.
Join PADT’s simulation support manager Ted Harris for a live presentation on the full
benefits of introducing Topology Optimization into your manufacturing process. This webinar will cover:
A brief introduction into the background of Topology Optimization and Additive Manufacturing, along with an overview of it’s capabilities
An explanation of the features available within this tool and a run through of it’s user interface and overall usage
An in-depth look at some of the intricacies involved with using the tool as well as the effectiveness of it’s design workflow
In support of the ANSYS Startup Program, PADT is proud to introduce the PADT Startup Spotlight.
We here at PADT are firm believers in the opinion that today’s startup companies are tomorrow’s industry leaders and thus should be give every possible opportunity to thrive and succeed.
As a result we are offering full access to our promotional capabilities in order to help startup companies developing physical prototypes to grow and develop in a competitive environment.
We will look through those startups that have purchased the ANSYS Startup Package through PADT, and select one to feature and promote, that we believe clearly represents the drive and entrepreneurial spirit that is key in order to succeed in today’s day and age.
Presenting the first Startup Spotlight:
Since their inception in 2014, Velox Motorsports has always been focused on speed; whether that be the speed of the NASCAR teams they have worked with or the desire their customers have for speed, which drives their competitiveness and fuels the demand for their products.
They even show a passion for speed in the company’s name (Velox), which translates from Latin to “swift or speed”.
It’s all the rage. “Big Data!” fixes everything. There is a lot of hype around the value of knowing so much about so many things. The problem is very few people have figured out what to do with that data. But leading technology companies like GE are using a proven tool to get value from all that great data. In “How do you get value out of Big Data? Simulation!” I look at how numerical simulation can be used to create digital twins of what your products are doing in the real world, delivering huge benefits today.
A few months ago, I did a post on the Technology Trends in Laser-based Metal Additive Manufacturing where I identified 5 key directions that technology was moving in. In this post, I want to do the same, but for a different technology that we also use on a regular basis at PADT: Fused Deposition Modeling (FDM).
1. New Materials with Improved Properties
Many companies have released and are continuously developing composite materials for FDM. Most involve carbon fibers and are discussed in this review. Arevo Labs and Mark Forged are two of many companies that offer composite materials for higher performance, the table below lists their current offerings (CF = Carbon Fiber, CNT = Carbon Nano Tubes). Virtual Foundry are also working on developing a metal rich filament (with about 89% metal, 11% binder polymer), which they claim can be used to make mostly-metal parts for non-functional purposes using existing FDM printers and a heat treatment to vaporize the binder. In short, while ABS and PLA dominate the market, there is a wide range of materials commercially available and this list is growing each year.
2. Improved Properties through Process Enhancements
Even with newer materials, a fundamental problem in FDM is the anisotropy of the parts and the fact that the build direction introduces weak interfaces. However, there are several efforts underway to improve the mechanical properties of FDM parts and this is an exciting space to follow with many approaches to this being taken. Some of these involve explicitly improving the interfacial strength: one of the ways this can be achieved is by pre-heating the base layer (as being investigated by Prof. Keng Hsu at the Arizona State University using lasers and presented at the RAPID 2016 conference). Another approach is being developed by a company called Essentium who combine microwave heating and CNT coated filaments as shown in the video below.
Taking a very different approach, Arevo labs has developed a 6-axis robotic FDM process that allows for conformal deposition of carbon fiber composites and uses an FEA solver to generate optimized toolpaths for improved properties.
3. Faster & Bigger
A lot of press has centered around FDM printers that make bigger parts and at higher deposition rates: one article discusses 4 of these companies that showcased their technologies at an Amsterdam trade show. Among the companies that showcased their technologies at RAPID was 3D Platform, that showed a $27,000 3D printer for FDM with a 1m x 1m x 0.5m printing platform. Some of the key questions for large form factor printers is if and how they deal with geometries needing supports and enabling higher temperature materials. Also, while FDM is well suited among the additive technologies for high throughput, large size prints, it does have competition in this space: Massivit is one company that in the video below shows the printing of a structure 5.6 feet tall in a mere 5 hours using what they call “Gel Dispensed Printing” that reduces the need for supports.
4. Bioprinting Applications
Micro-extrusion through syringes or specialized nozzles is one of the key ways bioprinting systems operate – but this is technically not “fused” deposition in that it may not involve thermal modification of the material during deposition. However, FDM technology is being used for making scaffolds for bio-printing with synthetic, biodegradable or bio-compatible polymers such as PCL and PLGA. The idea is these scaffolds then form the structure for seeding cells (or in some cases the cells are bioprinted as well onto the scaffold). This technology is growing fast and something we are also investigating at PADT – watch this space for more updates.
5. Material Modeling Improvements
Modeling FDM is an important part of being able to use simulation/analysis to design better processes and parts for functional use. This may not get a lot of press compared to the items above, but is a particular interest of mine and I believe is a critical piece of the puzzle going to true part production with FDM. I have written a few blog posts on the challenges, approaches and a micromechanics view of FDM printed structures and materials. The idea behind all of these is to represent FDM structures mathematically with valid and accurate models so that their behavior can be predicted and designs truly optimized. This space is also growing fast, the most recent paper I have come across in this space is from the University of Wisconsin-Madison that was published May 12, 2016.
Judging by media hype, metal 3D printing and 3D bioprinting are currently dominating the media spotlight – and for good reasons. But FDM has many things going for it: low cost of entry and manufacturing, user-friendliness and high market penetration. And the technology growth has no sign of abating: the most recent, 2016 Wohlers report assesses that there are over 300 manufacturers of FDM printers, though rumor on the street has it that there are over a thousand manufacturers coming up – in China alone. And as the 5 trends above show, FDM has a lot more to offer the world beyond being just the most rapidly scaling technology – and there are people working worldwide on these opportunities. When a process is as simple and elegant as extruding material from a hot nozzle, usable innovations will naturally follow.
Have you ever looked at the mechanical properties in an FDM material datasheet (one example shown below for Stratasys ULTEM-9085) and wondered why properties were prescribed in the non-traditional manner of XZ and ZX orientation? You may also have wondered, as I did, whatever happened to the XY orientation and why its values were not reported? The short (and unfortunate) answer is you may as well ignore the numbers in the datasheet. The longer answer follows in this blog post.
Mesostructure has a First Order Effect on FDM Properties
In the context of FDM, mesostructure is the term used to describe structural detail at the level of individual filaments. And as we show below, it is the most dominant effect in properties.
Consider this simple experiment we did a few months ago: we re-created the geometry used in the tensile test specimens reported in the datasheets and printed them on our Fortus 400mc 3D printer with ULTEM-9085. While we kept layer thickness identical throughout the experiment (0.010″), we modified the number of contours: from the default 1-contour to 10-contours, in 4 steps shown in the curves below. We used a 0.020″ value for both contour and raster widths. Each of these samples was tested mechanically on an INSTRON 8801 under tension at a displacement rate of 5mm/min.
As the figure below shows, the identical geometry had significantly different load-displacement response – as the number of contours grew, the sample grew stiffer. The calculated modulii were in the range of 180-240 kpsi. These values are lower than those reported in datasheets, but closer to published values in work done by Bagsik et al (211-303 kpsi); datasheets do not specify the meso-structure used to construct the part (number of contours, contour and raster widths etc.). Further, it is possible to modify process parameters to optimize for a certain outcome: for example, as suggested by the graph below, an all-contour design is likely to have the highest stiffness when loaded in tension.
Can we Borrow Ideas from Micromechanics Theory?
The above result is not surprising – the more interesting question is, could we have predicted it? While this is not a composite material, I wondered if I could, in my model, separate the contours that run along the boundary from the raster, and identify each as it’s own “material” with unique properties (Er and Ec). Doing this allows us to apply the Rule of Mixtures and derive an effective property. For the figure below, the effective modulus Eeff becomes:
Eeff = f.Ec + (1-f).Er
where f represents the cross-sectional area fraction of the contours.
With four data points in the curve above, I was able to use two of those data points to solve the above equation simultaneously and derive Er and Ec as follows:
Er = 182596 psi Ec = 305776 psi
Now the question became: how predictive are these values of experimentally observed stiffness for other combinations of raster and contours? In a preliminary evaluation for two other cases, the results look promising.
So What About the Orientation in Datasheets?
Below is a typical image showing the different orientations data are typically attributed to. From our micromechanics argument above, the orientation is not the correct way to look at this data. The more pertinent question is: what is the mesostructure of the load-bearing cross-section? And the answer to the question I posed at the start, as to why the XY values are not typically reported, is apparent if you look at the image below closely and imagine the XZ and XY samples being tested under tension. You will see that from the perspective of the load-bearing cross-section, XY and XZ effectively have the similar (not the same) mesostructure at the load-bearing cross-sectional area, but with a different distribution of contours and rasters – these are NOT different orientations in the conventional X-Y-Z sense that we as users of 3D printers are familiar with.
The point of this preliminary work is not to propose a new way to model FDM structures using the Rule of Mixtures, but to emphasize the significance of the role of the mesostructure on mechanical properties. FDM mesostructure determines properties, and is not just an annoying second order effect. While property numbers from datasheets may serve as useful insights for qualitative, comparative purposes, the numbers are not extendable beyond the specific process conditions and geometry used in the testing. As such, any attempts to model FDM structure that do not account for the mesostructure are not valid, and unlikely to be accurate. To be fair to the creators of FDM datasheets, it is worth noting that the disclaimers at the bottom of these datasheets typically do inform the user that these numbers “should not be used for design specifications or quality control purposes.”
If you would like to learn more and discuss this, and other ideas in the modeling of FDM, tune in to my webinar on June 28, 2016 at 11am Eastern using the link here, or read more of my posts on this subject below. If you are reading this post after that date, drop us a line at firstname.lastname@example.org and cite this post, or connect with me directly on LinkedIn.
Joining Two of PADT’s Favorite Things: Simulation and 3D Printing
Recent advances in Additive Manufacturing (3D Printing) have removed barriers to manufacturing certain geometry because of constraints in traditional manufacturing methods. Although you can make almost any shape, how do you figure out what shape to make. Using ANSYS products you can apply topological optimization to come up with a free-form shape that best meets your needs, and that can be made with Additive Manufacturing.
A few months ago we presented some background information on how to drive the design of this type of part using ANSYS tools to a few of our customers. It was a well received so we cleaned it up a bit (no guarantee there all the typos are gone) and recorded the presentation. Here it is on YouTube
Let us know what you think and if you have any questions or comments, please contact us.
As I showed in a prior blog post, Fused Deposition Modeling (FDM) is increasingly being used to make functional plastic parts in the aerospace industry. All functional parts have an expected performance that they must sustain during their lifetime. Ensuring this performance is attained is crucial for aerospace components, but important in all applications. Finite Element Analysis (FEA) is an important predictor of part performance in a wide range of indusrties, but this is not straightforward for the simulation of FDM parts due to difficulties in accurately representing the material behavior in a constitutive model. In part 1 of this article, I list some of the challenges in the development of constitutive models for FDM parts. In part 2, I will discuss possible approaches to addressing these challenges while developing constitutive models that offer some value to the analyst.
It helps to first take a look at the fundamental multi-scale structure of an FDM part. A 2002 paper by Li et. al. details the multi-scale structure of an FDM part as it is built up from individually deposited filaments all the way to a three-dimensional part as shown in the image below.
This multi-scale structure, and the deposition process inherent to FDM, make for 4 challenges that need to be accounted for in any constitutive modeling effort.
Anisotropy: The first challenge is clear from the above image – FDM parts have different structure depending on which direction you look at the part from. Their layered structure is more akin to composites than traditional plastics from injection molding. For ULTEM-9085, which is one of the high temperature polymers available from Stratasys, the datasheets clearly show a difference in properties depending on the orientation the part was built in, as seen in the table below with some select mechanical properties.
Toolpath Definition: In addition to the variation in material properties that arise from the layered approach in the FDM process, there is significant variation possible within a layer in terms of how toolpaths are defined: this is essentially the layout of how the filament is deposited. Specifically, there are at least 4 parameters in a layer as shown in the image below (filament width, raster to raster air gap, perimeter to raster air gap and the raster angle). I compiled data from two sources (Stratasys’ data sheet and a 2011 paper by Bagsik et al that show how for ULTEM 9085, the Ultimate Tensile Strength varies as a function of not just build orientation, but also as a function of the parameter settings – the yellow bars show the best condition the authors were able to achieve against the orange and gray bars that represent the default settings in the tool. The blue bar represents the value reported for injection molded ULTEM 9085.
Layer Thickness:Most FDM tools offer a range of layer thicknesses, typical values ranging from 0.005″ to 0.013″. It is well known that thicker layers have greater strength than thinner ones. Thinner layers are generally used when finer feature detail or smoother surfaces are prioritized over out-of-plane strength of the part. In fact, Stratasys’s values above are specified for the default 0.010″ thickness layer only.
Defects: Like all manufacturing processes, improper material and machine performance and setup and other conditions may lead to process defects, but those are not ones that constitutive models typically account for. Additionally and somewhat unique to 3D printing technologies, interactions of build sheet and support structures can also influence properties, though there is little understanding of how significant these are. There are additional defects that arise from purely geometric limitations of the FDM process, and may influence properties of parts, particularly relating to crack initiation and propagation. These were classified by Huang in a 2014 Ph.D. thesis as surface and internal defects.
Surface defects include the staircase error shown below, but can also come from curve-approximation errors in the originating STL file.
Internal defects include voids just inside the perimeter (at the contour-raster intersection) as well as within rasters. Voids around the perimeter occur either due to normal raster curvature or are attributable to raster discontinuities.
Thus, any constitutive model for FDM that is to accurately predict a part’s response needs to account for its anisotropy, be informed by the specifics of the process parameters that were involved in creating the part and ensure that geometric non-idealities are comprehended or shown to be insignificant. In my next blog post, I will describe a few ways these challenges can be addressed, along with the pros and cons of each approach.
I had a very cool music teacher back in 6th or 7th grade in the 1970’s in upstate New York. Today we’d probably say she was eclectic. In that class we listened to and discussed fairly recent songs in addition to general music studies. Two songs I remember in particular are ‘Hurdy Gurdy Man’ by Donovan and ‘Pinball Wizard’ by The Who. If you’re not familiar with Pinball Wizard, it’s from The Who’s rock opera Tommy, and is about a deaf, mute, blind young man who happens to be adept at the game of pinball. Yes, he is a Pinball Wizard. This sing popped into my head recently when we had some customer questions here at PADT regarding the pinball region concept as it pertains to ANSYS contact regions.
I’m not sure if the developers at ANSYS, Inc. had this song in mind when they came up with the nomenclature for the 17X (latest and greatest) series of contact elements in ANSYS, but regardless, you too can be a pinball wizard when it comes to understanding contact elements in ANSYS Mechanical and MAPDL.
In this current entry we will go more in depth on the pinball region, also known as the pinball radius. The pinball region is involved with the distance from contact element to target element in a given contact region. Outside the pinball region, ANSYS doesn’t bother to check to see if the elements on opposite sides of the contact region are touching or not. The program assumes they are far away from each other and doesn’t worry about any additional calculations for the most part.
Here is an illustration. The gray elements on the left represent the contact body and the red elements on the right represent the target body (assuming asymmetric contact). Target elements outside the pinball radius will not be checked for contact. The contact and target elements actually ‘coat’ the underlying solid elements so they are shown as dashed lines slightly offset from the solid elements for the sake of visibility. Here the pinball radius is displayed as a dashed blue circle, centered on the contact elements, with a radius of 2X the depth of the underlying solid elements.
So, outside the pinball region, we know ANSYS doesn’t check to see if the contact and target are actually in contact. It just assumes they are far away and not in contact. What about what happens if the contact and target are inside the pinball region? The answer to that question depends on which contact type we have selected.
For frictionless contact (aka standard contact in MAPDL) and frictional contact, the program will then check to see if the contact and target are truly touching. If they are touching, the program will check to see if they are sliding or possibly separating. If they are touching and penetrating, the program will check to see if the penetration exceeds the allowable amount and will make adjustments, etc. In other words, for frictionless and frictional contact, if the contact and target elements are close enough to be inside the pinball region, the program will make all sorts of checks and adjustments to make sure the contact behavior is adequately captured.
The other scenario is for bonded and no separation contact. With these contact types, the program’s behavior when the contact and target elements are within the pinball region is different. For these types, as long as the contact and target are close enough to be within the pinball region, the program considers the contact region to be closed. So, for bonded and no separation, your contact and target elements do not need to be line on line touching in order for contact to be recognized. The contact and target pairs just need to be inside the pinball region. This can be good, in that it allows for some ‘slop’ in the geometry to be automatically ignored, but it also can have a downside if we have a curved surface touching a flat surface for example. In that case, more of the curved surface may be considered in contact than would be the case if the pinball region was smaller. This effect is shown in the image below. Reducing the pinball radius to an appropriate smaller amount would be the fix for eliminating this ‘overconstraint’ if desired.
There is a default value for the pinball region/radius. It can be changed if needed. We’ll add more details in a moment. First, why is it called the “pinball” region? I like to think it’s because when it’s visualized in the Mechanical window, it looks like a blue pinball from an actual pinball arcade game, but I’ll admit that the ANSYS terminology may predate the Mechanical interface. The image below shows what I mean. The blue balls are the different pinball radii for different contact regions.
Note that you don’t see the pinball region displayed as shown in the above image unless you have manually changed the pinball size in Mechanical. The pinball region can be changed in the Mechanical window in the details view for each contact region by changing Pinball Region from Program Controlled to Radius, like this:
In MAPDL, the pinball radius value can be changed by defining or editing the real constant labeled PINB.
By now you’re probably wondering what is the default value for the pinball radius? The good news is that it is intelligently decided by the program for each contact region. The default is always a scale factor on the depth of the underlying elements of each contact region. In the first pinball region image shown near the beginning of this article, the example plot shows the pinball region/radius as two times the depth of the underlying elements.
The table below summarizes the default pinball radius values for most circumstances for 2D and 3D solid element models. More detailed information is available in the ANSYS Help.
Default Pinball Radius Values
Large Deflection Off
Large Deflection On
Frictionless and Frictional
1* Underlying Element Depth
2*Underlying Element Depth
Bonded and No Seperation
0.25*Underlying Element Depth
0.5*Underlying Element Depth
Rigid-Flexible Contact: Typically the Default Values are Doubled
Summing it all up: we have seen how the default values are calculated and also how to change them. We have seen what they look like as blue balls in a plot of contact regions in Mechanical if the pinball radius has been explicitly defined. We also discussed what the pinball radius does and how it’s different for frictionless/frictional contact and bonded/no separation contact.
You should be well on your way to becoming a pinball wizard at this point.
Does performing simulation in ANSYS make you think of certain songs, or are there songs you like to listen to while working away on your simulations an addition to The Who’s “Pinball Wizard” and Peter Gabriel’s “I Have the Touch”? If so, we’d love to hear about your song preferences in the comments below.