Predictive Theoretical and Computational Approaches for Additive Manufacturing Proceedings of a Workshop (2016) / Chapter Skim
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4 Monitoring and Advanced Diagnostics to Enable Additive Manufacturing Fundamental Understanding
4 Monitoring and Advanced Diagnostics to Enable Additive Manufacturing Fundamental Understanding
Pages 59-80
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From page 59... ...
Such a capability will require a new set of in-process sensor tools to validate material quality, composition, and key performance parameters. Ade Makinde (GE Global Research Center)
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From page 60... ...
Process development, Makinde noted, depends on many different considerations with respect to materials, design, process machines, process planning, heat treatment, and post-processing. These factors are described below, and he cautioned that each of these factors contain uncertainties that need to be understood to avoid propagation throughout the whole system.
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From page 61... ...
Several types of sensors are needed to probe the AM process for errors: high resolution cameras and enhanced sensing for the melt pool, temperature, humidity and moisture, gas flow, and vibration, as well as methods to look into the powder bed, stress cracking detection, and packing density. While there have been several advances over recent years -- including improved control of thermal lensing and the melt pool, enhanced information about the melt pool and powder bed, improved powder delivery, and decreased cost of implementation of in situ systems -- Makinde highlighted the following in situ and diagnostics challenges in AM: • High data rate collection is needed.
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From page 62... ...
In situ measurements for design iteration, inputs, optimization, and quality can be validated with a high-fidelity physics model and then BHM can be used to manage and control variability for integration into design practices. This could improve the estimates of part life and the understanding of the interaction between the different stages of the process (e.g., p owder size and distribution, laser, process parameters, part orientation, support structure, material properties such as surface tension and viscosity, surface finish, distortion and residual stress, and microstructure)
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From page 63... ...
The first process he introduced was stereolithography, which is a photopolymer process useful for patterns because the liquid process results in a smooth surface finish. The fused deposition modeling process currently dominates the hobby market.
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From page 64... ...
Instead, structures are built on a plate using support structures to help control thermal warping. Heat treatment is used to anneal parts with support structures and then the supports are then machined off, and the parts are machine finished.
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From page 65... ...
The third application is for corrosion and wear-resistant coatings on mold making, offshore drilling, machine tool, and medical parts. Process parameters affect the quality of the final part, including powder deposition parameters (e.g., powder flow rate, shield gas flow rate, nozzle type, and powder shape, size, and type)
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From page 66... ...
Process parameters for electrospinning include electric field strength, flow rate, deposition speed, and evaporation rate. She explained that the online diagnostic requires high magnification and high temporal resolution of 2 Nuburu, Inc., for example, makes a high-power and affordable blue laser that could potentially be used for AM.
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From page 67... ...
near-field spinning. SOURCE: Jian Cao, Northwestern University, presentation of Martinez-Prieto et al.
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From page 68... ...
DISCUSSION Following their presentations, Ade Makinde, Joseph Beaman, and Jian Cao participated in a panel discussion moderated by Anthony DeCarmine from Oxford Performance Materials. An audience member began by elaborating on the case study Joe Bishop of Sandia National Laboratories presented on the AM titanium preform.
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From page 69... ...
Bishop and Makinde commented that multiple machine variability is challenging and approaches to minimize it are being pursued actively in industry, including approaches to account for variability in the design process. Cao noted that laser and sensor degradation are contributors to the variation.
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He suggested that modeling could be used to extrapolate findings from one part to a different geometry. Makinde stated that the emphasis needs to be on the entire process development to determine the correct parameter set for a specific build.
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From page 71... ...
He also mentioned model results for melt pool control where process modeling is able to replicate real-world behavior of the melt pool with and without closed-loop process control. Prior LENS research, Keicher explained, has focused on graded composition demonstration and process characterization modeling (e.g., a part heats up during the build and the heat flow changes, resulting in different microstructures and properties across the part)
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From page 72... ...
According to Keicher, the goal is model-based feedforward control to provide a path for end users to leverage predictive capabilities to accelerate development in AM. To facilitate this, a process simulator would take the predictive AM CAD modeling -- with the corresponding microstructural modeling, thermal and residual stress modeling, multi-material modeling, and multiphysics-based topological optimization -- and translate the results into a geometry for the model-based feedforward control tool.
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From page 73... ...
The design rules and process specs are lacking or nonexistent. He noted that AM complexity necessitates an integrated computational material science and engineering approach to address challenges, both temporal (e.g., complex energy input and resulting thermal history)
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From page 74... ...
These data were manually compiled with melt current data to better understand the pore volume fraction and average current throughout the cylinder. In conclusion, he emphasized that more integrated computational material science and engineering tools are needed for digital data management for AM.
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From page 75... ...
He described some of the hardware considerations, including checking positional axes to be within 10 micron resolution, determining laser focus and power calibration, and completing build platform leveling. There were also several control 9 Collaborators include EWI (Shawn Kelly, Mahdi Jamshidinia, Jake Marchal, Paul Boulware, Connie Reichert, Greg Firestone, and Lance Cronley)
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From page 76... ...
Both local and global sensors are evolving, Yang stated. Local sensors are currently collecting data at approximately 10 percent of the desired rate (or once every 10 melt pools)
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Keicher, Edwin Schwalbach, and Yu-Ping Yang participated in a panel discussion moderated by Slade Gardner from Lockheed Martin Space Systems Company. A participant asked if the panelists have advice on statistical approaches for selecting extreme values, spikes, or rare events in sensor data to better
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From page 78... ...
He mentioned that there is a potential in the future to use materials systems that are less sensitive to geometric orientation or can easily be remedied with post processing. A participant asked Yang if the melt pool monitoring technique to determine delamination can account for porosity.
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Keicher commented that Sandia is working to incorporate a suite of sensors into the process to improve capabilities. Gardner asked about limitations of sensors and whether advances are needed in sensor technology.
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