Product design engineers are increasingly under pressure to complete product designs faster so innovative products can reach the market sooner. Performing up-front simulation as part of the product development process can accelerate designing optimized products and reduce costly physical prototypes. To successfully implement simulation early in the product development process, simulation software must be easy-to-use and cover all the necessary physics that impact product designs.
Join us for this webinar to discover how AIM delivers unparalleled ease-of-use for simulation driven product development, and learn how design engineers can benefit from using simulation early in the product development process.
This webinar will be held on August 10th from 1:00 pm – 2:00 pm PT
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 email@example.com and cite this post, or connect with me directly on LinkedIn.
“Why are there so many different software solutions in Additive Manufacturing and which ones do I really need?“
This was a question I was asked at lunch during the recently concluded RAPID 3D printing conference by a manager at an aerospace company. I gave her my thoughts as I was stuffing down my very average panini, but the question lingered on long after the conference was over – several weeks later, I decided to expand on my response in this blog post.
There are over 25 software solutions available (scheduling software for service technicians, etc.) and being used for different aspects of Additive Manufacturing (AM). To answer the question above, I found it best to classify these solutions into four main categories based on their purpose, and allow sub-categories to emerge as appropriate. This classification is shown in Figure 1 below – and each of the 7 sub-categories are discussed in more detail in this post.
1. Design Modeler
You need this if you intend to create or modify designs
Most designs are created in CAD software such as SOLIDWorks, CATIA and SpaceClaim (now ANSYS SpaceClaim). These have been in use long before the more recent rise in interest in AM and most companies have access to some CAD software internally already. Wikipedia has a comparison of different CAD software that is a good starting point to get a sense of the wide range of CAD solutions out there.
2. Build Preparation
You need this if you plan on using any AM technology yourself (as opposed to sending your designs outside for manufacturing)
Once you have a CAD file, you need to ensure you get the best print possible with the printer you have available. Most equipment suppliers will provide associated software with their machines that enable this. Stand-alone software packages do exist, such as the one developed by Materialise called Magics, which is a preferred solution for Stereolithography (SLA) and metal powder bed fusion in particular – some of the features of Magics are shown in the video below.
Scanning & File Transfer
3. Geometry Repair
You need this if you deal with low-quality geometries – either from scans or since you work with customers with poor CAD generation capabilities
Geomagic Design X is arguably the industry’s most comprehensive reverse engineering software which combines history-based CAD with 3D scan data processing so you can create feature-based, editable solid models compatible with your existing CAD software. If you are using ANSYS, their SpaceClaim has a powerful repair solution as well, as demonstrated in the video below.
Improving Performance Through Analysis
4. Topology Optimization
You need this if you stand to benefit from designing towards a specific objective like reducing mass, increasing stiffness etc. such as the control-arm shown in Figure 2
Of all the ways design freedom can be meaningfully exploited, topology optimization is arguably the most promising. The ability to now bring analysis up-front in the design cycle and design towards a certain objective (such as maximizing stiffness-to-weight) is compelling, particularly for high performance, material usage sensitive applications like aerospace. The most visible commercial solutions in the AM space come from Altair: with their Optistruct solution (for advanced users) and SolidThinking Inspire (which is a more user-friendly solution that uses Altair’s solver). ANSYS and Autodesk 360 Inventor also offer optimization solutions. A complete list, including freeware, can be availed of at this link.
5. Lattice Generation
You need this if you wish to take advantage of cellular/lattice structure properties for applications like such as lightweight structural panels, energy absorption devices, thermal insulation as well as medical applications like porous implants with optimum bone integration and stiffness and scaffolds for tissue engineering.
Broadly speaking, there are 3 different approaches that have been taken to lattice design software:
I will cover the differences between these approaches in detail in a future blog post. A general guideline is that the generative design approach taken by Autodesk’s Within is well suited to medical applications, while Lattice generation through topology optimization seems to be a sensible next step for those that are already performing topology optimization, as is the case with most aerospace companies pursuing AM technology. The infill/conformal approach is limiting in that it does not enable optimization of lattice structures in response to an objective function and typically involves a-priori definition of a lattice density and type which cannot then be modified locally. This is a fast evolving field – between new software and updates to existing ones, there is a new release on an almost quarterly, if not monthly basis – some recent examples are nTopology and the open source IntraLattice.
Below is a short video demo of Autodesk’s Within:
You need this if you do either topology optimization or lattice design, or need it for part performance simulation
Basic mechanical FE analysis solvers are integrated into most topology optimization and lattice generation software. For topology optimization, the digitally represented part at the end of the optimization typically has jarring surfaces that are smoothed and then need to be reanalyzed to ensure that the design changes have not shifted the part’s performance outside the required window. Beyond topology optimization & lattice design, analysis has a major role to play in simulating performance – this is also true for those seeking to compare performance between traditionally manufactured and 3D printed parts. The key challenge is the availability of valid constitutive and failure material models for AM, which needs to be sourced through independent testing, from the Senvol database or from publications.
7. Process Simulation
You need this if you would like to simulate the actual process to allow for improved part and process parameter selection, or to assess how changes in parameters influence part behavior
The real benefit for process simulation has been seen for metal AM. In this space, there are broadly speaking two approaches: simulating at the level of the part, or at the level of the powder.
Part Level Simulation: This involves either the use of stand-alone AM-specific solutions like 3DSIM and Pan Computing (acquired by Autodesk in March 2016), or the use of commercially available FE software such as ANSYS & ABAQUS. The focus of these efforts is on intelligent support design, accounting for residual stresses and part distortion, and simulating thermal gradients in the part during the process. ANSYS recently announced a new effort with the University of Pittsburgh in this regard.
Powder Level Simulation: R&D efforts in this space are led by Lawrence Livermore National Labs (LLNL) and the focus here is on fundamental understanding to explain observed defects and also to enable process optimization to accelerate new materials and process research
Part level simulation is of great interest for companies seeking to go down a production route with metal AM. In particular there is a need to predict part distortion and correct for it in the design – this distortion can be unacceptable in many geometries – one such example is shown in the Pan Computing (now Autodesk) video below.
A Note on Convergence
Some companies have ownership of more than one aspect of the 7 categories represented above, and are seeking to converge them either through enabling smooth handshakes or truly integrate them into one platform. In fact, Stratasys announced their GrabCAD solution at the RAPID conference, which aims to do some of this (minus the analysis aspects, and only limited to their printers at the moment – all of which are for polymers only). Companies like Autodesk, Dassault Systemes and ANSYS have many elements of the 7 software solutions listed above and while it is not clear what level of convergence they have in mind, all have recognized the potential for a solution that can address the AM design community’s needs. Autodesk for example, has in the past 2 years acquired Within (for lattice generation), netfabb (for build preparation) and Pan Computing (for simulation), to go with their existing design suite.
Conclusion: So what do I need again?
What you need depends primarily on what you are using AM technologies for. I recommend the following approach:
Identify which of the 4 main categories apply to you
Enumerate existing capabilities: This is a simple task of listing the software you have access to already that have capabilities described in the sub-categories
Assess gaps in software relative to meeting requirements
Develop an efficient road-map to get there: be aware that some software only make sense (or are available) for certain processes
In the end, one of the things AM enables is design freedom, and to quote the novelist Toni Morrison: “Freedom is not having no responsibilities; it is choosing the ones you want.” AT PADT, we work with design and analysis software as well as AM machines on a daily basis and would love to discuss choosing the appropriate software solutions for your needs in greater detail. Send us a note at firstname.lastname@example.org and cite this blog post, or contact 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.
PADT’s Ward Rand and Eric Miller were interviewed recently by Chris Gilfallan of “The Record Reporter” about what PADT does and how it impacts the local product development and intellectual property community. It ended up being a great overview and is aimed at helping the Arizona legal profession understand a bit more about what we do. If you have a subscription you can read the article here.
While much has been (justifiably) written about HP and XJet releasing new, potentially game-changing products at RAPID 2016, I wanted to write this post about some of the smaller, unexpected joys that I discovered. If I sound overly enthusiastic about the people and companies behind them, it is likely due to the fact that I wrote this on the flight back, staring out at the clouds and reflecting on what had been a wonderful trip: I own no locks, stocks or barrels in any of these companies.
1. Essentium Materials – Carbon Nanotubes and Microwaves to improve FDM mechanical properties
Over the past year, I have studied, written and made presentations about the challenges of developing models for describing Fused Deposition Modeling (FDM) given their complex and part-specific meso-structure. And while I worked on developing analytical and numerical techniques for extracting the best performance from parts in the presence of significant anisotropy, the team at Essentium has developed a process to coat FDM filaments with Carbon nanotubes and extrude them in the presence of microwave radiation. In the limited data they showed for test specimens constructed of unidirectional tool-paths, they demonstrated significant reduction in anisotropy and increase in strength for PLA. What I liked most about their work is how they are developing this solution on a foundation of understanding the contributions of both the meso-structure and inter-filament strength to overall part performance. Essentium was awarded the “RAPID Innovations award”, first among the 27 exhibitors that competed and are, in my opinion, addressing an important problem that is holding back greater expansion of FDM as a process in the production space.
2. Hyrel 3D – Maker meets Researcher meets The-Kid-in-All-of-Us
I only heard of Hyrel 3D a few days prior to RAPID, but neglected to verify if they were exhibiting at RAPID and was pleasantly surprised to see them there. Consider the options this 3D printer has that you would be hard pressed to find in several 3D printers combined: variable extrusion head temperatures (room temp to 450 C), sterile head options for biological materials, a 6W laser (yes, a laser), spindle tools, quad head dispensing with individual flow control and UV crosslinking options. Read that again slowly. This is true multiple degree-of-freedom material manipulation. What makes their products even more compelling is the direct involvement of the team and the community they are building up over time, particularly in academia, across the world, and the passion with which they engage their technology and its users.
3. Technic-Print: New Chemistry for Improved FDM Support Removal
If you manufacture FDM parts with soluble supports, keep reading. A chemist at Technic Inc. has developed a new solution that is claimed to be 400% faster than the current Sodium-Hydroxide solution we use to dissolve parts. Additionally, the solution is cited as being cleaner on the tank, leaving no residue, has a color indicator that changes the solution’s color from blue to clear. And finally, through an additional agent, the dissolved support material can be reclaimed as a clump and removed from the solution, leaving behind a solution that has a pH less than 9. Since PADT manufactures one of the most popular machines that are used to dissolve these supports that unbeknown to us, were used in the testing and development of the new solution, we had an enriching conversation with the lead chemist behind the solution. I was left wondering about the fundamental chemistry behind color changing, dissolution rates for supports and the reclaiming of support – and how these different features were optimized together to develop a usable end-solution.
4. Project Pan: Computationally Efficient Metal Powder Bed Fusion Simulation I presented a literature review at AMUG (another Additive Manufacturing conference) last month, on the simulation of the laser-based powder bed fusion. At the time, I thought I had captured all the key players between the work being done at Lawrence Livermore National Labs by Wayne King’s group, the work of Brent Stucker at 3DSIM and the many academics using mostly commercially available software (mostly ANSYS) to simulate this problem. I learned at RAPID that I had neglected to include a company called “Project Pan” in my review. This team emerged from Prof. Pan Michaleris’s academic work. In 2012, he started a company that was acquired by Autodesk two months ago. In a series of 3 presentations at RAPID, Pan’s team demonstrated their simulation techniques (at a very high level) along with experimental validation work they had done with GE, Honeywell and others through America Makes and other efforts. What was most impressive about their work was both the speed of their computations and the fact that this team actually had complex part experimental validations to back up their simulation work. What most users of the powder bed fusion need is information on temperatures, stresses and distortion – and within time frames of a few hours ideally. It seems to me that Pan and his team took an approach that delivers exactly that information and little else using different numerical methods listed on their site (novel Hex8 elements, an element activation method and intelligent mesh refinement) that were likely developed by Pan over the years in his academic career and found the perfect application, first in welding simulation and then in the powder bed fusion process. With the recent Autodesk acquisition, it will be interesting to see how this rolls out commercially. Details of some of the numerical techniques used in the code can be found at their website, along with a list of related publications.
5. FDA Participation: Regulating through education and partnership
On a different note from the above, I was pleasantly surprised by the presence of the FDA, represented by Matthew Di Prima, PhD. He taught part of a workshop I attended on the first day, took the time to talk to everyone who had an interest and also gave a talk of his own in the conference sessions, describing the details of the recently released draft guidance from the FDA on 3D printing in medical applications. It was good to connect the regulatory agency to a person who clearly has the passion, knowledge, intelligence and commitment to make a difference in the Additive Manufacturing medical community. Yes, the barriers to entry in this space are high (ISO certifications, QSR systems, 510(k) & Pre-Market Approvals) but it seems clear that the FDA, at least as represented by Dr. Di Prima, are doing their best to be a transparent and willing partner.
What really makes a trip to a conference like RAPID worth it are the new ideas, connections and possibilities you come away with that you may not stumble upon during your day job – and on that account, RAPID 2016 did not disappoint. As a line in one of my favorite song’s goes:
“We’ll never know, unless we grow.
There’s too much world outside the door.”
– Fran Healy (Travis, “Turn”).
PADT talks a lot about synergy as a key strength and a key element of the value we provide to our customers. Our three departments, Manufacturing, Services, and Sales, are in constant communication, always leveraging one another’s expertise to solve problems. Strong internal relationships — a consequence of being under the same roof — precipitate easy and abundant information and resource sharing. Communication, paradigm, alignment, synergy: clear as day.
But what does any of that mean?
When a PADT product development customer meets us for the first time, he or she may be shown a slide that looks like this:
Strong bilateral communication among the Product Development, 3D Printing, and Analysis groups means that the project is enriched by contributions from experts across several fields, multiplying the value we add in the development process. For instance, the product will likely someday run into a sticky problem without a clear solution. PADT can attack it from multiple angles, such as design adjustment, finite element analysis (FEA) optimization, and the iterative testing of 3D printed prototypes.
Ok, but still: what does any of that mean?
A longtime customer of PADT’s product development group recently ran into an urgent problem without a clear path to a solution. Their manufacturing partner called them and said that a particular subassembly in their design will cost three times more than expected, which would raise the price of the product above the maximum the market would bear. PADT was presented with the problem: how do we reduce the subassembly cost by 66% while maintaining overall performance, and how do we confidently select a solution in under a week?
PADT’s three engineering groups jumped in to help.
The Product Development group held a brainstorming session and came out with two adjustments to bring overall cost down. First, the subassembly of three bonded unique steel parts would be replaced by a single injection molded plastic part. This change reduces component cost to within the target, but also significantly reduces the final assembly’s structural integrity.
Secondly, a plastic stiffener truss was added between components to mitigate the reduction in overall stiffness. This change adds a little assembly cost, but also significantly increases the final assembly’s structural integrity, which had been weakened by the first change.
The Analysis group conducted a series of FEA simulations, first to determine the increased bending under load and second to select a material to balance the conflicting requirements for stiffness, strength, and cost. After multiple simulation iterations, it was determined that Product Development had selected a permissible path forward and that a glass-filled polypropylene provides the best combination of the three parameters.
The 3D Printing group then printed the new design for qualitative “look and feel” testing and quantitative force/deflection study. The group was able to closely match the properties of the selected material from their collection of printable filaments and top-shelf industrial printers, reproducing even the fine details — subtle fillets, radii — that boost strength but are missed with lower quality printers. Through prototype tests, it was determined that Analysis selected an appropriate material and Product Development selected an appropriate design.
In the end, PADT was able to confidently select a solution to the customer’s unique cost problem in under a week. Thanks to the synergy of three groups — Product Development, Analysis, and 3D Printing — the customer was able to stay on schedule and enter the market at a relevant price.
So how can PADT help my product?
PADT’s system for delivering services is a textbook example of synergy in action, and it represents a uniquely effective solution to your company’s product problems. Whether you’re in concept design or high-volume production, PADT will tailor-make a solution that fits your budget, schedule, and technical requirements.
Give us a call at 1–800–293-PADT or email email@example.com.
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 (firstname.lastname@example.org).
Numerical simulation has been the bulk of my career for 30 years now. I love simulation. It has had a huge positive impact on product development as well as many other industries. In “What is numerical simulation? And why should I care?” I evangelize a bit about my professional passion.
One of the more difficult things about being at the Additive Manufacturing Users Group (AMUG) is dealing with the fact that there is more to do than you can hope to accomplish in four and a half days. So I decided to focus on two themes: laser-based metal additive manufacturing (AM); and design & simulation for AM. In this post, I focus on the former and try to distill the trends I noticed across the laser-based metal AM system manufacturers that were present at the conference: Concept Laser, SLM, Renishaw, EOS and 3D Systems (listed here in the decreasing order of the time I spent at each supplier’s booth). While it is interesting to study how 5 different suppliers interpret the same technology and develop machines around it, it is not my objective to compare them here, but to extract common trends that most suppliers seem to be working on to push their machines to the next level. For the purposes of this post, I have picked the top-of-the-line machine that each supplier offers as an indication of the technology’s capabilities: they span a range of price points, so once again this is not meant to be a comparison.
As a point of observation, the 5 key trends I noticed turned out to be all really aspects of taking the technology from short run builds towards continuous production. This was not my intent, so I believe it is an accurate indication of what suppliers are prioritizing at this stage of the technology’s growth and see as providing key levers for differentiation.
1. Quality Monitoring
Most customers of AM machines that wish to use it for functional part production bemoan the lack of controls during manufacturing that allow them to assess the quality of a part and screen for excursionary behavior without requiring expensive post-processing inspection. Third party companies like Sigma Labs and Stratonics have developed platform-independent solutions that can be integrated with most metal AM systems. Metal AM suppliers themselves have developed a range of in-situ monitors that were discussed in a few presentations during AMUG, and they generally fall into the following categories:
Laser: Sensors monitor laser powder as well as temperature across the different critical components in the system
Oxygen Level: Sensors in the build chamber as well as in sieving stations track O2 levels to ensure the flushing of air with inert Argon or Nitrogen has been effective and that there are no leaks in the system
Live video: simple but useful, this allows users to get a live video stream of the top layer as it is being built and can help detection of recoater blade damage and part interaction
Meltpool: Concept Laser showed how its Meltpool monitoring system can be used to develop 2D and 3D plots that can be superimposed with the 3D CAD file to identify problematic areas – the video is also on YouTube and embedded below. SLM and EOS offer similar meltpool monitoring solutions.
Coater consistency: Concept Laser also described a monitor that captures before and after pictures to assess the consistency of the coater thickness across the build area – and this information is fed forward to adjust subsequent coater thicknesses in an intelligent manner.
Quality monitoring systems are still in their infancy with regard to what is done with the information generated, either in terms of feed forward (active) process control or even having high confidence in using the data to validate part quality. A combination of supplier development and academic and industry R&D is ongoing to get us to the next level.
2. Powder Handling
In a previous post, I touched upon the fire and explosion risks posed by metal powder handling. To lower the bar for an operator to gain access to a metal AM machine, one of the considerations is operator safety and the associated training they would need. Suppliers are constantly trying to improve the methods by which they can minimize powder handling. For a mechanical engineer, it is intriguing to see how reactive metal powders can be moved around in inert atmospheres using different strategies. The SLM 500HL uses a screw system to move the powder around in narrow tubes that stick out of the machine and direct the material to a sieving station after which they are returned to the feed area. The Renishaw RenAM 500M on the other hand uses a pneumatically driven recirculation system powered by Argon gas that is well integrated into the machine frame. Concept Laser also offers automated powder handling on the XLine 2000R, while EOS and 3DSystems do not offer this at the moment. Figure 2 below does not do justice to the level of complexity and thought that needs to go into this.
One of the limitations of automating powder handling is the ability to change materials, which is very hard to impossible to do with high enough confidence with these systems. As a result, their use is limited to cases where one machine can be dedicated to one material and efficiency gains of powder handling can be fully realized. The jury is still out on the long term performance of these systems, and I suspect this is one area that will continue to see improvements and refinements in subsequent model releases.
3. Multi-Laser Processing
In the quest for productivity improvement, one of the biggest gains comes from increasing the number and power of available lasers for manufacturing. In my previous experience with laser based systems (albeit not for this application), an additional laser can increase overall machine throughput by 50-80% (it does not double due to steps like the recoater blade movement that does not scale with the number of lasers).
The suppliers I visited at AMUG have very different approaches to this: SLM provides the widest range of customizable options for laser selection with their 500HL, which can accept either 2 or 4 lasers with power selection choices of 400W or 1000W (the 4 laser option was on display, YouTube video from the same machine in action is below) – the lasers of different powers can also be combined to have two 400W and two 1000W lasers. Concept Laser’s XLine 2000R allows for either 1 or 2 1000W lasers and their smaller, M2 machine that was showcased at AMUG has options for 1 or 2 lasers, with power selection of 200W or 400W. EOS, Renishaw and 3D Systems presently offer only single laser solutions: the EOS M 400 has one 1000W laser, Renishaw’s RenAM 500M has one 500W laser and the ProX DMP 320 from 3D Systems has one 500W laser.
There are a few considerations to be aware of when assessing a multi-laser machine: Each laser drives an increase in machine capital cost. But there is another point of note to remember when using multi-laser systems for manufacturing that centers around matching process outputs from different lasers: laser-to-laser variation can be a dominant source of overall process variation and can drive a need to calibrate, maintain and control both lasers as if they were independent machine systems. Additionally, development of a process on one particular laser power (100W, 400W, 500W, 1000W) may not scale easily to another and is something to remember when developing a long term strategy for metal AM that involves different kinds of machines, even if from the same supplier.
4. Software Integration
Renishaw spent a significant amount of time talking about their easy-to-use QuantAM software which is designed to integrate Renishaw process parameters and part processing information more tightly and allow for seamless process parameter development without needing third part software like Magics. Additive Industries announced in their presentation at AMUG that they had just signed an agreement with 3DSIM to integrate their support design software solution into their MetalFab1 machine. Software integration is likely to be an increasing trend especially around the following areas:
Improving support design methods and reducing its empirical nature and reducing the material, build time and support removal costs associated with them as well as eliminating the need for iterative builds
Increasing process options available to the user (for example for the outer skin vs the inner core, or for thick vs thin walls)
Simplifying the development of optimized process parameters for the user working on new materials
Integrating design and process optimization to increase effective part performance
In a future blog post, I will look specifically at the many design and simulation tools available around AM and how they are connected today even if not well-synergized.
5. Modular System Architectures
In a list of mostly evolutionary changes, this is the one area that struck me as being a step-change in how this technology will make an impact, even if it will be felt only by larger scale manufacturers. Concept Laser and Additive Industries are two companies that delivered presentations discussing how they were approaching the challenge of revolutionizing the technology for true production and minimizing the need for human touch. Common to both is the notion of modularity, allowing for stacking of printing, powder removal, heat treating and other stations. While Additive Industries are developing a flow resembling a series production line, Concept Laser have taken the more radical approach of having autonomous vehicles delivering the powder bed to the different stations, with travel channels for the vehicles, for the operator and for maintenance access (Figure 3). Both companies expect to have solutions out by the end of this year.
It is an interesting time to be a manufacturer of laser-based metal 3D printers, and an even more interesting time to be a consumer of this technology. The laser-material interaction fundamentals of the process are now fairly well-established. Competitors abound both in existing and emerging markets with machines that share many of the same capabilities. Alternative technologies (E-Beam melting, deposition and jetting) are making strides and may start to play in some applications currently dominated by laser-based technologies. A post early-adopter chasm may be around the corner. This will continuously drive the intense need to innovate and differentiate, and possibly also lead to a merger or two. And while most of the news coming out of conferences is justifiably centered around new process technologies (as was the case with Carbon’s CLIP and XJET’s metal nanoparticle jetting at AMUG this year), I think there is an interesting story developing in laser-based powder bed fusion and can’t wait to see what AMUG 2017 looks like!
The development of small modular nuclear reactors, or SMR’s, is a complex task that involves balancing the thermodynamic performance of the entire system. Flownex is the ideal tool for modeling pressure drop [flow] and heat transfer [temperature] for the connected components of a complete system in steady state and transient, sizing and optimizing pumps or compressors, pipes, valves, tanks, and heat exchangers.
To highlight this power and capability, PADT and Flownex will be exhibiting at the 2016 SMR conference in Atlanta where we will be available to discuss exciting new Flownex developments in system and subsystem simulations of SMRs. If you are attending this year’s event, please stop by the Flownex booth and say hello to experts from M-Tech and PADT.
If you are not able to make the conference or if you want to know more now, you can view more information from the new Flownex SMR brochure or this video:
Why is Flownex a Great Tool for SMR Design and Simulation?
These developments offer greatly reduced times for performing typical design tasks required for Small Modular Nuclear Reactor (SMR) projects including sizing of major components, calculating overall plant efficiency, and design for controllability
This task involves typical components like the reactor primary loop, intermediate loops, heat exchangers or steam generators and the power generation cycle. Flownex provides for various reactor fuel geometries, various reactor coolant types and various types of power cycles.
Flownex can also be used for determining plant control philosophy. By using a plant simulation model, users can determine the transient response of sensed parameters to changes in input parameters and based on that, set up appropriate pairings for control loops.
For passive safety system design Flownex can be used to optimize the natural circulation loops. The program can calculate the dynamic plant-wide temperatures and pressures in response to various accident scenarios, taking into account decay heat generation, multiple natural circulation loops, transient energy storage and rejection to ambient conditions.
Most histories of Additive Manufacturing (3D printing) trace the origins of the technology back to Charles Hull’s 1984 patent, the same year production began on the first of the Back to the Future movies. Which is something of a shock when you see 3D printing dotting the Gartner Hype Cycle like it was invented in the post-Seinfeld era. But that is not what this post is about.
When I started working on Additive Manufacturing (AM), I was amazed at the number of times I was returning to text books and class notes I had used in graduate school a decade ago. This led me to reflect on how AM is helping bring back to the forefront disciplines that had somehow lost their cool factor – either by becoming part of the old normal, or because they contained ideas that were ahead of their time. I present three such areas of research that I state, with only some exaggeration, were waiting for AM to come along.
Topology Optimization: I remember many a design class where we would discuss topology optimization, look at fancy designs and end with a conversation that involved one of the more cynical students asking “All that’s fine, but how are you going to make that?”. Cue the elegant idea of building up a structure layer-by layer. AM is making it possible to manufacture parts with geometries that look like they came right out of a stress contour plot. And firms such as ANSYS, Autodesk and Altair, as well as universities and labs are all working to improve their capabilities at the intersection of topology optimization and additive manufacturing.
Lattice Structures: One of the first books I came across when I joined PADT was a copy of Cellular Solids by Lorna Gibson and M.F. Ashby. Prof. Gibson’s examples of these structures as they occur in nature demonstrate how they provide an economy of material usage for the task at hand. Traditionally, in engineering structures, cellular designs are limited to foams or consistent shapes like sandwich panels where the variation in cell geometry is limited – this is because manufacturing techniques do not normally lend themselves well to building complex, three dimensional structures like those found in nature. With AM technologies however, cell sizes and structures can be varied and densities modified depending on the design of the structure and the imposed loading conditions, making this an exciting area of research.
Metallurgy: As I read the preface to my “Metallurgy for the Non-Metallurgist” text book, I was surprised to note the author openly bemoan the decline of interest in metallurgy, and subsequently, fewer metallurgists in the field. And I guess it makes sense: materials science is today mostly concerned with much smaller scales than the classical metallurgist trained in. Well, lovers of columnar grain growth and precipitation hardening can now rejoice – metallurgy is at the very heart of AM technology today – most of the projected growth in AM is in metals. The science of powder metallurgy and the microstructure-property-process relationships of the metal AM technologies are vital building blocks to our understanding of metal 3D printing. Luckily for me, I happen to possess a book on powder metallurgy. And it too, is from 1984.
Metal 3D printing involves a combination of complex interacting phenomena at a range of length and time scales. In this blog post, I discuss three of these that lie at the core of the laser fusion of metals: phase changes, residual stresses and solidification structure (see Figure 1). I describe each phenomenon briefly and then why understanding it matters. In future posts I will dive deeper into each one of these areas and review what work is being done to advance our understanding of them.
Phase changes describe the transition from one phase to another, as shown in Figure 2. All phases are present in the process of laser fusion of metals. Metal in powder form (solid) is heated by means of a laser beam with spot sizes on the order of tens of microns. The powder then melts to form a melt pool (liquid) and then solidifies to form a portion of a layer of the final part (solid). During this process, there is visible gas and smoke, some of which ionizes to plasma.
The transition from powder to melt pool to solid part, as shown in Figure 3, is the essence of this process and understanding this is of vital importance. For example, if the laser fluence is too high, defects such as balling or discontinuous welds are possible and for low laser fluence, a full melt may not be obtained and thus lead to voids. Selecting the right laser, material and build parameters is thus essential to optimize the size and depth of the liquid melt pool, which in turn governs the density and structure of the final part. Finally, and this is more true of high power lasers, excessive gas and plasma generation can interfere with the incident laser fluence to reduce its effectiveness.
Residual stresses are stresses that exist in a structure after it reaches equilibrium with its environment. In the laser metal fusion process, residual stresses arise due to two related mechanisms [Mercelis & Kruth, 2006]:
Thermal Gradient: A steep temperature gradient develops during laser heating, with higher temperatures on the surface driving expansion against the cooler underlying layers and thereby introducing thermal stresses that could lead to plastic deformation.
Volume Shrinkage: Shrinkage in volume in the laser metal fusion process occurs due to several reasons: shrinkage from a powder to a liquid, shrinkage as the liquid itself cools, shrinkage during phase transition from liquid to solid and final shrinkage as the solid itself cools. These shrinkage events occur to a greater extent at the top layer, and reduce as one goes to lower layers.
After cooling, these two mechanisms together have the effect of creating compressive stresses on the top layers of the part, and tensile stresses on the bottom layers as shown in Figure 4. Since parts are held down by supports, these stresses could have the effect of peeling off supports from the build plate, or breaking off the supports from the part itself as shown in Figure 4. Thus, managing residual stresses is essential to ensuring a built part stays secured on the base plate and also for minimizing the amount of supports needed. A range of strategies are employed to mitigate residual stresses including laser rastering strategies, heated build plates and post-process thermal stress-relieving.
Solidification structure refers to the material structure of the resulting part that arises due to the solidification of the metal from a molten state, as is accomplished in the laser fusion of metals. It is well known that the structure of a metal alloy strongly influences its properties and further, that solidification process history has a strong influence on this structure, as does any post processing such as a thermal exposure. The wide range of materials and processing equipment in the laser metal fusion process makes it challenging to develop a cohesive theory on the nature of structure for these metals, but one approach is to study this on four length scales as shown in Figure 5. As an example, I have summarized the current understanding of each of these structures specifically for Ti-6Al-4V, which is one of the more popular alloys used in metal additive manufacturing. Of greatest interest are the macro-, meso- and microstructure, all of which influence mechanical properties of the final part. Understanding the nature of this structure, and correlating it to measured properties is a key step in certifying these materials and structures for end-use application.
Phase changes, residual stresses and solidification structure are three areas where an understanding of the fundamentals is crucial to solve problems and explore new opportunities that can accelerate the adoption of metal additive manufacturing. Over the past decade, most of this work has been, and continues to be, experimental in nature. However, in the last few years, progress has been made in deriving this understanding through simulation, but significant challenges remain, making this an exciting area of research in additive manufacturing to watch in the coming years.
In part 1 of this two-part post, I reviewed the challenges in the constitutive modeling of 3D printed parts using the Fused Deposition Modeling (FDM) process. In this second part, I discuss some of the approaches that may be used to enable analyses of FDM parts even in presence of these challenges. I present them below in increasing order of the detail captured by the model.
Conservative Value: The simplest method is to represent the material with an isotropic material model using the most conservative value of the 3 directions specified in the material datasheet, such as the one from Stratasys shown below for ULTEM-9085 showing the lower of the two modulii selected. The conservative value can be selected based on the desired risk assessment (e.g. lower modulus if maximum deflection is the key concern). This simplification brings with it a few problems:
The material property reported is only good for the specific build parameters, stacking and layer thickness used in the creation of the samples used to collect the data
This gives no insight into build orientation or processing conditions that can be improved and as such has limited value to an anlayst seeking to use simulation to improve part design and performance
Finally, in terms of failure prediction, the conservative value approach disregards inter-layer effects and defects described in the previous blog post and is not recommended to be used for this reason
Orthotropic Properties: A significant improvement from an isotropic assumption is to develop a constitutive model with orthotropic properties, which has properties defined in all three directions. Solid mechanicians will recognize the equation below as the compliance matrix representation of the Hooke’s Law for an orthortropic material, with the strain matrix on the left equal to the compliance matrix by the stress matrix on the right. The large compliance matrix in the middle is composed of three elastic modulii (E), Poisson’s ratios (v) and shear modulii (G) that need to be determined experimentally.
Good agreement between numerical and experimental results can be achieved using orthotropic properties when the structures being modeled are simple rectangular structures with uniaxial loading states. In addition to require extensive testing to collect this data set (as shown in this 2007 Master’s thesis), this approach does have a few limitations. Like the isotropic assumption, it is only valid for the specific set of build parameters that were used to manufacture the test samples from which the data was initially obtained. Additionally, since the model has no explicit sense of layers and inter-layer effects, it is unlikely to perform well at stresses leading up to failure, especially for complex loading conditions. This was shown in a 2010 paper that demonstrated these limitations in the analysis of a bracket that itself was built in three different orientations. The authors concluded however that there was good agreement at low loads and deflections for all build directions, and that the margin of error as load increased varied across the three build orientations.
Laminar Composite Theory: The FDM process results in structures that are very similar to laminar composites, with a stack of plies consisting of individual fibers/filaments laid down next to each other. The only difference is the absence of a matrix binder – in the FDM process, the filaments fuse with neighboring filaments to form a meso-structure. As shown in this 2014 project report, a laminar approach allows one to model different ply raster angles that are not possible with the orthotropic approach. This is exciting because it could expand insight into optimizing raster angles for optimum performance of a part, and in theory reduce the experimental datasets needed to develop models. At this time however, there is very limited data validating predicted values against experiments. ANSYS and other software that have been designed for composite modeling (see image below from ANSYS Composite PrepPost) can be used as starting points to explore this space.
Hybrid Tool-path Composite Representation: One of the limitations of the above approach is that it does not model any of the details within the layer. As we saw in part 1 of this post, each layer is composed of tool-paths that leave behind voids and curvature errors that could be significant in simulation, particularly in failure modeling. Perhaps the most promising approach to modeling FDM parts is to explicitly link tool-path information in the build software to the analysis software. Coupling this with existing composite simulation is another potential idea that would help reduce computational expense. This is an idea I have captured below in the schematic that shows one possible way this could be done, using ANSYS Composite PrepPost as an example platform.
Discussion: At the present moment, the orthotropic approach is perhaps the most appropriate method for modeling parts since it is allows some level of build orientation optimization, as well as for meaningful design comparisons and comparison to bulk properties one may expect from alternative technologies such as injection molding. However, as the application of FDM in end-use parts increases, the demands on simulation are also likely to increase, one of which will involve representing these materials more accurately than continuum solids.
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.