Predictive Theoretical and Computational Approaches for Additive Manufacturing Proceedings of a Workshop (2016) / Chapter Skim
Currently Skimming:
3 Computational and Analytical Methods in Additive Manufacturing
3 Computational and Analytical Methods in Additive Manufacturing
Pages 33-58
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 33... ...
This includes multiscale modeling, computational materials, modeling topology optimization, verification and validation methods, and uncertainty quantification for AM processes and AM resulting materials. Anthony Rollett (Carnegie Mellon University)
|
From page 34... ...
• What are the drivers and the fundamental advancements needed for computational methods and optimization techniques? COMPUTATIONAL AND ANALYTICAL NEEDS IN ADDITIVE MANUFACTURING Anthony Rollett, Carnegie Mellon University Anthony Rollett began by adding two additional questions to the session topics that he believes should be considered: 1.
|
From page 35... ...
He commented that understanding simple trends in processing behavior can have a notable impact. Rollett illustrated this point using a beam power versus beam travel speed map for an electron beam process to infer process behavior.
|
From page 36... ...
. Prediction of porosity is a complex problem because the geometry of melt pools is complex; the pools overlap across layers, and there can be regions of unmelted material resulting in lack of fusion porosity.
|
From page 37... ...
, polymer powderbed fusion (Williams and Deckard, 1998) , residual stress modeling (Zaeh 1 Collaborators include Wayne King, Andy Anderson, Robert Ferencz, Neil Hodge, Chandrika Kamath, Saad Khairallah, Ibo Matthews, and Sasha Rubenchik.
|
From page 38... ...
Modeling metal AM processes is challenging because a broad range of length and time scales are covered. King explained that LLNL researchers use multiscale modeling approaches to provide key insights into AM metal processes that will inform performance simulations.
|
From page 39... ...
FIGURE 3-1 Multiscale modeling approaches for metal additive manufacturing. SOURCE: Wayne King, Lawrence Livermore National Laboratory, presentation to the workshop.
|
From page 40... ...
Experiments show the melt pool expansion exerts a forward push on powder and the nearby powder is consumed through capillary forces into the melt pool, similar to the simulation. However, non-local powder experiences inward force toward the melt pool, unconsumed cold powder is swept backward and upward, and molten droplets eject in both directions directly from the melt pool.
|
From page 41... ...
REVOLUTIONS IN DESIGN AND MANUFACTURING: TOPOLOGY OPTIMIZATION AND UNCERTAINTY QUANTIFICATION IN ADDITIVE MANUFACTURING Corbett Battaile, Sandia National Laboratories Corbett Battaile began by noting that his presentation would focus on design, specifically his team's work at Sandia National Laboratories.3 He explained that topology optimization is a way to determine an optimal shape to best achieve a set of desired objectives, performances, or specifications using a distribution of materials and set spatial constraints. Traditional manufacturing sets constraints on how manufacturing is approached, but these constraints are loosened for AM.
|
From page 42... ...
Using topology optimization, the design domain and function are first specified, topology optimization using finite element analysis determines the form that meets the function, and then an optimized design is established. Performance prioritization can then be done to define a Pareto suite of topologies evaluating the thermal mechanical properties of the proposed topologies.
|
From page 43... ...
DISCUSSION Following their presentations, Anthony Rollett, Wayne King, and Corbett Battaile participated in a panel discussion moderated by Steve Daniewicz from Mississippi State University. A participant noted that transferring topology optimization design back into CAD tools is often challenging.
|
From page 44... ...
He said that much of the promise is not just in taking a finite element analysis and doing a topology optimization on it but rather in integrating this capability with process modeling and material physics calculations. An audience member mentioned that reduced-order modeling holds great potential but has not been widely applied to AM yet.
|
From page 45... ...
For the part-scale model, a volumetric heat source is being considered. A participant asked if the ray tracing approach can be translated to particle size modeling using ALE3D.
|
From page 46... ...
Lastly, the participant asked if the shape of the laser beam impacts defect formation. King stated that a Gaussian laser beam is a poor choice because the boiling temperature will be reached at the center of the beam, and they are examining several other beam profiles.
|
From page 47... ...
He said an open challenge is tying the analysis and physics models across scales to do on-the-fly design. APPLICATION OF INTEGRATED COMPUTATIONAL MATERIALS ENGINEERING TO THE DESIGN AND DEVELOPMENT OF NEW HIGH-PERFORMANCE MATERIALS FOR ADDITIVE MANUFACTURING David Snyder, QuesTek Innovations David Snyder mentioned that computational methods and approaches for materials design and integration would be the focus of his presentation, specifically with respect to computational thermodynamics and mechanistic property modeling.
|
From page 48... ...
The third and final case study he discussed highlighted AM of highstrength aluminum, which is currently limited by hot tearing phenomena where cracks form during the build process. This phenomenon is driven by high residual stress and suboptimal solidification behavior.
|
From page 49... ...
Another issue discussed was select process-microstructure modeling needs, including the linkage between AM process models and solidification theory (e.g., columnar-to-equiaxed transition, cellular-to-dendritic transition, and transformation kinetics) , location-specific thermal history (e.g., input into solidification models and phase evolution models)
|
From page 50... ...
COMPUTATIONAL AND ANALYTICAL METHODS IN ADDITIVE MANUFACTURING: LINKING PROCESS TO MICROSTRUCTURE Gregory Wagner, Northwestern University Gregory Wagner began by discussing linking modeling and simulation of process to performance. He explained that the impacts of process parameters (e.g., laser power, scan speed, scan direction, material, powder size, and layer thickness)
|
From page 51... ...
Since large thermal gradients lead to complex microstructure evolution during manufacturing, the local thermal history is used to predict the microstructure. This concurrent multiscale method is approached with fairly simple isotropic phase field models that track the solidification front but will hopefully allow anisotropic microstructure and dendrite formation to be simulated.
|
From page 52... ...
Conservative level set methods are being used for simulations in multiphase fluid dynamics (Desjardins et al., 2008) and phase field models for fluid-gas interaction (e.g., Kim, 2012; the work of A
|
From page 53... ...
ADDITIVE MANUFACTURING CHALLENGES FOR COMPUTATIONAL SOLID MECHANICS Joe Bishop, Sandia National Laboratories Joe Bishop stated his presentation would focus on current challenges of computational modeling for solid mechanics, as well as how AM is an interesting application for solid mechanics. He noted that he would be drawing from several projects with many collaborators, including a project on mechanical response of AM stainless steel 304L across a wide range of strain rates,5 and the Predictive Performance Margins Project6 designed to provide a science-based foundation for design and analysis capabilities that links nanoscale mechanisms and microscopic variability to stochastic performance.
|
From page 54... ...
He gave an example of direct numerical simulation of multiscale modelng of an I-beam, where an equiaxed grain structure is modeled i directly within the engineering-scale finite element model. This model uses crystal-plasticity material models for each grain and can incorporate as-manufactured states (e.g., texture, residual stress)
|
From page 55... ...
Bishop also described the following long-term goals: • Error estimation in engineering quantities of interest for quantifying material model form error, discretization error, and homogenization error; • Process models for microstructure predictions (e.g., KMC, phase field) ; • Multiscale material models that represent microstructure explicitly (e.g., through concurrent homogenization with crystal-plasticity models)
|
From page 56... ...
A participant posed a question on how well inclusions can be modeled and if there is the possibility to simulate multiple material powders in the matrix. Snyder commented that thermodynamic predictions are used to define the oxide content and linking this with process modeling could help address geometric questions, but this has not yet been explored.
|
From page 57... ...
Bishop agreed, noting that data science approaches could help identify correlations that may be precursors to failure, for example. He suggested it is a way to get more out of simulations than is possible using only traditional analysis.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.