Predictive Theoretical and Computational Approaches for Additive Manufacturing Proceedings of a Workshop (2016) / Chapter Skim
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5 Additive Manufacturing Scalability, Implementation, Readiness, and Transition
5 Additive Manufacturing Scalability, Implementation, Readiness, and Transition
Pages 81-102
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From page 81... ...
, Anthony DeCarmine (Oxford Performance Materials) , Rainer Hebert (University of Connecticut)
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• Can software be developed, integrated with precision engineering, and integrated into engineering work flow? • Are there drivers to integrate computational simulation and advanced optimization methodologies to enable unique AM design?
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He referenced the many facets that go into an AM workflow, including the geometry design, computational tools and interfaces development, material design, and process modeling and control tools. These complex factors are illustrated in Figure 5-1.
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Geometry design for AM is challenging. Shin explained that most CAD systems are currently based on boundary representations and tools for topology optimization with local material properties that are lacking.
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By better utilizing multiscale modeling, such as atomistic modeling, to achieve critical material properties at various conditions, the challenge of doing material design and prediction capability with numerous simulations for optimization is eliminated. The path for utilizing fundamental results for AM and scaling them for use in productions, Shin explained, relies on new design tools for AM.
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This includes national level consortia for AM process modeling (that can be divided into process specific ones) and a repository or database for material selection, properties, or response surfaces.
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He noted that macroscale residual stress measurements can also be used as validation methods while microstructure characterization and microscale residual stresses can run into unexpected problems. Also, mechanical behavior of the final part after any post-build processing could be assessed by exploring the microstructure characterization and microscale residual stresses and by utilizing mechanical testing (e.g., tensile, fatigue, and fracture)
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From page 88... ...
, and overhang geometry. A DIFFERENT PERSPECTIVE ON SCALABILITY AND PUBLIC-PRIVATE PARTNERSHIP Anthony DeCarmine, Oxford Performance Materials Anthony DeCarmine opened by explaining that drivers to integrate computational simulation and advanced optimization methodologies to enable unique AM design exist; however, the promise of AM cannot be properly realized without the fusion of mature simulation systems and optimizers to foster migration from conventional design methodologies to a new paradigm.
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Shin, Lyle E Levine, and Anthony DeCarmine participated in a panel discussion following their presentations, which was moderated by Slade Gardner from Lockheed Martin Space Systems Company.
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A university participant asked about the lack of topology optimization accounting for local properties. Shin noted that topology optimization currently focuses on geometric optimization, but AM allows for materials to be tailored; therefore, materials and material properties can be optimized as well.
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A national laboratory participant agreed that Levine's suggestion of having a benchmark for computational fluid dynamics would advance material development. Wing Kam Liu agreed and emphasized that this should be an international and collaborative effort among academia, industry, and government.
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. Also, the practical hands-on experiences were conducted as team projects with computational materials colleagues,2 focusing on density functional theory calculations and molecular dynamics simulations.
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. Applicable simulations include powder flow, Lattice-Boltzmann, density functional theory, molecular dynamics modeling, solidification modeling, and phase-field modeling.
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The modeling of processing aspects is also needed, including more thermodynamics and kinetics theory and Lattice-Boltzmann simulations. Macro-level heat flow theory would benefit from modeling of processing aspects such as powder raking and heat flow fluid dynamics and theory.
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Precision engineering and software markets are likely contributors with software companies offering simulation software for traditional processing and manufacturing of analysis equipment (e.g., thermal, optical, microstructure)
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Surface roughness, he noted, is a function of the build layer thickness, the powder size distribution, the randomness of the powder spreading, the laser beam diameter, the hatch spacing, and the laser power. Developing software architecture and databases for AM model development relies on establishing software requirements for the melt pool (e.g., modeling the power size distribution; powder spreading; laser and powder interaction; computational fluid dynamics for melting and solidification, heat transfer, and Marangoni forces; defect generation such as porosity and micro cracking; and microscale residual stresses)
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He stressed that location-specific material properties need to be integrated into current finite element codes and analysis and that manufacturing groups need to be engaged in future developments. COMPUTATIONAL SIMULATION AND ADVANCED OPTIMIZATION: THE KEY ROLE OF PUBLIC-PRIVATE PARTNERSHIPS IN SCALABILITY Tahany El-Wardany, United Technologies Research Center Tahany El-Wardany began with an overview of United Technologies Research Center (UTRC)
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However, El-Wardany described some near- and long-term development and integration efforts that are still needed, including the following: • Material models -- Near term: powder characteristics and representation -- Longer term: physical properties, thermal mechanical behavior, metallurgy and rheology, and layout of functional grading in materials • Design -- Near term: part geometry, support structure -- Longer term: no support structure • Process physics -- Near term: multiphysics simulation of AM process, energy source representation and interaction parameters -- Longer term: possible onset and propagation of defects, part spe cific control of defects, interfacial characteristics • Processing of geometric model -- Near term: slicing and path generation, optimizing process through designed experiments -- Longer term: tailoring of process characteristics for desired properties
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From page 99... ...
These partnerships stand to benefit from AM advancements, El-Wardany noted, especially with respect to developing the mechanistic understanding of materials behavior during layered manufacturing to enable unique design optimization, generating new commercial off-the-shelf tools that can be applied for microstructure and mechanical property prediction, developing preliminary design curves for new materials with minimum experimental cost, and linking materials and process models to support probabilistic design capabilities.
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Concurrent hybrid processes can be implemented to include process monitoring, online inspection, feedback control, and virtual manufacturing workflow optimization. DISCUSSION Following their presentations, Rainer Hebert, Alonso Peralta-Duran, and Tahany El-Wardany participated in a joint panel discussion moderated by Anthony DeCarmine, Oxford Performance Materials.
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An online participant asked about priorities for the process parameters. El-Wardany said the power velocity ratio (or power density function of the velocity)
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