Additive Manufacturing - Low Budget Simulation Tools for Design Evaluation

Finite Element (FE) simulation is an engineering discipline that is applied in various ways in industries such as aerospace, automotive, power train, customer goods and bio-engineering. It is commonly used to augment experimental testing to save time and money at different stages in the product development process. More recently, FE simulation is gaining ground in the field of Additive Manufacturing to optimize and verify designs, or to simulate the AM process itself.

Compared to traditional manufacturing procedures, AM allows for great design freedom to produce complex and organic shapes enabling designers and engineers produce designs that are very light while meeting structural integrity requirements. To check requirements for service of very complex designs produced by AM is often an impossibility based on experience and becomes quite expensive if one has to do several test prints for experiments. Here, FE simulation comes in handy to check a designs’ suitability for service before a single cent was spent on the manufacturing process.

In most mid- to large-size companies it is quite common to have access to commercial FE tools to optimize and check the suitability for service of designs. However, in the Additive Manufacturing service industry access to commercial simulation software may be limited or not possible due to a various reasons. But what can one really do if they want use FE simulation to improve their designs without having the opportunity to use commercial FE software? 

Open Source Software has nowadays spread globally for pretty much all application needs, and luckily, also for Finite Element simulation. There are various websites that list comprehensively available Open Source FE software such as the one from opennovation. For structural simulation, and considering my Abaqus background, I have found the Open Source FE software Calculix very convenient and helpful to address a broad range of my simulation needs. To illustrate some of Calculix’s structure-mechanical simulation functionality, I took the winner design of the GE Bracket challenge for Additive Manufacturing and generated a FE model to determine the von Mises stress distribution. In addition to that, I ran the same model with Abaqus to demonstrate eventual differences in results between the Open Source and the commercial FE software.

1.       In a first step I used the open source meshing software NETGEN to create the FE mesh with 2nd order tetrahedral elements and to specify surfaces that will be used for defining boundary conditions.

2.       Then I exported the model as INP file (Abaqus nomenclature) and manually created a job file to apply boundary conditions and define analysis steps. Generating the job file manually may require some exercise. However, Calculix comes with decent documentation and examples that will help you to better understand the syntax.

3.       After preparing the model and the job file I ran the analysis and generated von Mises color plots.

The following plots show the von Mises stresses for the first three loading scenarios:

     

The maximum von Mises stresses in the Calculix version (images left) and the Abaqus version (images right) are as follows:

Calculix [MPa]	Abaqus [MPa]	Difference [%]
887	886	0.11
777	793	2.02
558	557	0.18

The comparison of the von Mises stresses and their location showed that there is almost no difference between the results of the Open Source and the commercial software. The biggest difference occurs in the 2^nd load case and is about 2%.

So, what is the conclusion about this exercise? In this article I wanted to raise awareness that there are accessible, alternative options to commercial FE software tools if you want to optimize and check the suitability for service of designs e.g. via maximum stress levels. There are of course downsides to free software tools; commonly they are not as well supported as commercial ones, they may not include the latest technology and methodologies or include other issues that may raise the end-users’ concerns. Nevertheless, I found that for structure-mechanical applications Open Source software such as Calculix works very well and presents a great, low-cost alternative to commercial software to investigate the integrity of designs. 

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Simulation Based Structural Integrity Assessment of 3D Printed Designs (Part 1)

In the Additive Manufacturing (AM) industry structural simulation engineers are increasingly confronted with the question “Can you simulate how long the part will last under given loading?". This is a reasonable question to be addressed to the engineer, however, in case of 3D printed parts it is much harder to answer than for parts from traditional manufacturing industries such as milling, welding, forging or casting. The reason why it is harder to answer is that there is a complete lack of standardized assessment strategies for 3D printed structures that allow for prediction of structural integrity behavior.

Structural integrity assessment strategies are tools which allow for static and fatigue life prediction. Besides a common mistaken belief that structural life behavior is solely simulated, it must be clarified that the structural integrity is assessed based on simulation results via assessment criteria. For structure mechanical problems that means one has to perform a simulation first to determine maximum stresses and then use a proper assessment criteria to evaluate the stresses. Structure mechanical assessment criteria are commonly developed based on extensive experimental tests of materials on micro-, macro-, feature and component  size level under all kinds of static and dynamic loading scenarios. Statistical models are then used to further investigate the likelihood of failure for various combinations of loads, material conditions and designs and design features, and based on that, mathematical equations are developed that will predict the maximum allowable stresses in the structure. The complete compilation of all formulas, tables and figures to predict the allowable stresses is referred as assessment guideline. Such assessment guidelines can be developed for any field in industry and quite a broad range of commercial, publicly available assessment guidelines exists for traditional industries. Quite often guidelines are developed for specific applications such as reinforced composites or welded steel and aluminum structures. Some commercial guidelines are for example the FKM Guideline, DVS1608 or DIN 15018. However, currently there is no publicly accesable structural integrity assessment guide available for additively manufactured components. Thus, for now, any serious static and fatigue strength prediction for 3D printed components is based on extensive tests and proper structural integrity prediction models that industrial AM players develop themselves.

There are a few practical reasons why a general structural integrity assessment guideline has not been developed for AM designs yet. First of all, AM is still a fairly new technology and recently found its way to manufacturing of structurally critical components. Ten years ago there was simply no need for such guidelines. Then, the output of 3D printing is not standardized yet. There is still a considerably strong variability in part quality with regard to material properties (e.g E-Modulus) and structural strength (e.g UTS,  fatigue limit, elongation at break) depending on the AM machine, it's setting during the print, and post printing treatment. Even though the research has been fundamentally progressed in the last five years and provided better understanding of machine settings, post printing treatments and their effect on printed designs, yet there is no global standard that links the machine settings with the strength of 3D printed designs.

The fact that the growing AM marked is increasingly pushing into high-end applications in automotive-, powertrain- and aerospace industry, will cause an increasing assessment strategy demand, where structural assessment methodologies for traditional manufacturing processes have been well established and widely used.

Simulation Of Thermal Stresses During Metal Additive Manufacturing

3D Printing of metallic parts has gained increasing attention over the last decade. Similar to prior established non-metallic 3D printing procedures [1-3], such as Stereolithography, Selective Laser Sintering (SLS) or Fused Deposition Modeling (FDM), Selective Laser Melting (SLM), Electronic-beam Melting (EBM) and Direct Metal Laser Sintering (DMLS) of metals offer all the advantages of Additive Manufacturing (AM) centered around manufacturability of very complex shapes.
  However, metal AM comes with additional challenges one has to face when preparing the design for printing. One of the biggest challenges is to create support structures which are necessary to:

• hold the part in place during printing (similar to SLS, FDM, etc.)

• facilitate heat transfer into the base plate

• prevent thermal warping due to thermal stresses

Schematic illustration of the localized melting process. Right: The laser hits and melts the powder causing thermal expansion in the peripheral volumes of the melting pool.  Left: After laser exposure the melted material cools down and solidifies causing the material to shrink and leaving residual stresses in the printed line.

To prevent the deformation due to thermal stresses, supports must be placed to :

• conduct the high temperatures more efficiently into the base plate. This will reduce the localized temperature gradients which directly effect the remaining residual stresses

• counteract the thermal warping to avoid global part distortion and to prevent contact of the part with the re-coater.

Schematic illustration of the part warping after support removal. Right: During the print the support structure holds the cantilever in place, counteracts the thermal warping and conducts heat to the base plate. Left: After support structure removal, the cantilever flanges warp due to the residual stresses ("spring-back"-effect) .

Residual stresses induced by a localized, rapid heating process (laser melts powder), and a fairly rapid cooling down process (solidification of the material) force the material to rapidly expand and contract, respectively, leaving residual stresses in each printed line [4]. For each line and each layer, the residual stresses are accumulated and will eventually cause a global deformation of the part. This "warping" may become significant enough so that the underlying support structure may yield and fail to hold the part in place, or the deformed part may touch and damage the re-coater. Both phenomena will eventually lead to printing failure. 

Finding the right support configuration to counteract is not an easy process. Often due to the complex shapes of the part designs, it is hard to predict where and how the part will deform solely based on experience. Thus, there is a need for tools that assist in the decision making process for finding the proper support structure.
  A promising technology to predict and optimize support structures is the application of thermo-mechanical simulations often realized via Finite Element Method (FEM). These tools facilitate different model techniques to predict the deformation via the manufacturing process and provide feedback to the engineers to optimize the support structures and to eventually reduce the risk of printing failure. The model techniques that are commonly being employed can be categorized in one, or in a combination of the following:

• Inherent Strain (IS) Method - fast, less accurate.   This method commonly facilitates prior analysis of a hatching-scale model to determine residual plastic strains which are then applied to the individual hatching regions in a layer by layer fashion. This is a technique that has been adapted from welding simulation and modified for SLM simulation applications and appears as promising and fast solution [5].

• Volume by Volume (VbV) or Layer by Layer (LbL) Method - slower than IS, more accurate than IS.   Here a representative Surface Heat Flux (SHFLX) or Body Heat Flux (BHFLX) are applied to a target volume (e.g.hatching volume) or an entire layer for a representative time. The magnitude of the heat fluxes and the application time depend on the machine specific laser power and scanning speed. Since this method requires transient thermo-mechanical simulation models, it is more accurate than the IS method but also slower due to the higher computational load. This method has found its application from research projects into industry and can be used with any advanced FE simulation code. It has great potential not only due to its ability to predict deformation but also to reveal volumes with higher thermal load and greater risk of accumulating thermal stresses [6,7]

• Detailed Microscopic Thermo-Mechanical Modeling - most accurate, very slow.   This method is probably the most accurate approach to predict the thermal stresses during SLM. For such models, the thermo-mechanical process is simulated for each individual laser track. Due to the very fine resolution, this simulation method requires immense computational power and thus has not yet found its way into commercial applications. Nevertheless, this is the most desired approach for simulating metal AM since it allows full insight into the microscopic accumulation of the residual stresses and their effect of global part warping. [8,9,11]

However, one of the biggest challenges that metal AM simulation applications is facing, is the right balance between model complexity, which will determine the model accuracy, and simulation time to provide feedback to the engineers for further optimization of support structures. To win the race and establish a fundamental position on the AM simulation market, commercial and non-commercial software providers are forced to speed up the simulation time of their products, without compromising the accuracy of the prediction of the AM process. Facilitating of Graphic Processing Units (GPU) [10,11] or implementing adapted FE approaches with advanced mesh facilitation [12,13] appear as promising approaches for that.

References:
[1]  Kruth, J-P., Ming-Chuan Leu, and T. Nakagawa. "Progress in additive manufacturing and rapid prototyping." CIRP Annals-Manufacturing Technology 47.2 (1998): 525-540.
[2] Frazier, William E. "Metal additive manufacturing: a review." Journal of Materials Engineering and Performance 23.6 (2014): 1917-1928.
[3] Guo, Nannan, and Ming C. Leu. "Additive manufacturing: technology, applications and research needs." Frontiers of Mechanical Engineering 8.3 (2013): 215-243.
[4] Mercelis, Peter, and Jean-Pierre Kruth. "Residual stresses in selective laser sintering and selective laser melting." Rapid Prototyping Journal 12.5 (2006): 254-265.
[5] Keller, Nils, and Vasily Ploshikhin. "New method for fast predictions of residual stress and distortion of am parts." Solid Freefrom Fabrication(2014): 1229-1237.
[6] Keller, N., et al. "Thermo-mechanical Simulation of Additive Layer Manufacturing of Titanium Aerospace structures." LightMAT Conference. Vol. 3. No. 5. 2013.
[7] Neugebauer, Fabian, et al. "Simulation of selective laser melting using process specific layer based meshing." Proc. Fraunhofer Direct Digital Manufacturing Conf.(DDMC 2014), Axel Demmer, Aachen, Germany. 2014.
[8] Kruth, Jean-Pierre, et al. "Selective laser melting of iron-based powder."Journal of Materials Processing Technology 149.1 (2004): 616-622.
[9] Fu, C. H., and Y. B. Guo. "3-DIMENSIONAL FINITE ELEMENT MODELING OF SELECTIVE LASER MELTING TI-6AL-4V ALLOY."
[10] Keller N, Ploshikhin V (2014) New method for fast predictions of residual stress and distortion of AM parts. Solid Freeform Fabrication Symposium, Austin, Texas
[11] Megahed, Mustafa, et al. "Metal additive-manufacturing process and residual stress modeling." Integrating Materials and Manufacturing Innovation 5.1 (2016): 1.
[12] Zeng, K., et al. "Comparison of 3DSIM thermal modelling of selective laser melting using new dynamic meshing method to ANSYS." Materials Science and Technology 31.8 (2015): 945-956.
[13] Pal, Deepankar, et al. "An integrated approach to additive manufacturing simulations using physics based, coupled multiscale process modeling."Journal of Manufacturing Science and Engineering 136.6 (2014): 061022.