Evaluating the Effect of Processing Parameters on Porosity in Electron Beam Melted Ti-6Al-4V via Synchrotron X-ray Microtomography

Cunningham, Ross; Narra, Sneha P.; Ozturk, Tugce; Beuth, Jack; Rollett, Anthony D.

doi:10.1007/s11837-015-1802-0

Cited by 127 publications

(54 citation statements)

References 24 publications

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“…2b should be attributed to the relatively large cavity depth 13, 13, 16, 30 . After the laser is turned off, the sudden absence of the laser beam creates a local negative pressure environment, which brings about the liquid metal flowing towards the center of the melt pool 13, 27 .…”

Section: Resultsmentioning

confidence: 99%

Real-time monitoring of laser powder bed fusion process using high-speed X-ray imaging and diffraction

Zhao

Fezzaa

Cunningham

et al. 2017

Sci Rep

498

244

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We employ the high-speed synchrotron hard X-ray imaging and diffraction techniques to monitor the laser powder bed fusion (LPBF) process of Ti-6Al-4V in situ and in real time. We demonstrate that many scientifically and technologically significant phenomena in LPBF, including melt pool dynamics, powder ejection, rapid solidification, and phase transformation, can be probed with unprecedented spatial and temporal resolutions. In particular, the keyhole pore formation is experimentally revealed with high spatial and temporal resolutions. The solidification rate is quantitatively measured, and the slowly decrease in solidification rate during the relatively steady state could be a manifestation of the recalescence phenomenon. The high-speed diffraction enables a reasonable estimation of the cooling rate and phase transformation rate, and the diffusionless transformation from β to α ’ phase is evident. The data present here will facilitate the understanding of dynamics and kinetics in metal LPBF process, and the experiment platform established will undoubtedly become a new paradigm for future research and development of metal additive manufacturing.

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Section: Resultsmentioning

confidence: 99%

Real-time monitoring of laser powder bed fusion process using high-speed X-ray imaging and diffraction

Zhao

Fezzaa

Cunningham

et al. 2017

Sci Rep

498

244

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“…The process variables of MAM machines not only influence the production costs, but also determine the process characteristics (e.g., melt pool dimensions) [51], material microstructure (e.g., grain size, porosity) [52], and material properties (e.g., strength, fatigue) [53,54]. Process and solidification mapping were developed by Gockel et al [51,55], Beuth et al [56,57], Montogomery et al [58], and Seifi et al [53] to predict and control the desired MAM process outcomes through melt pool and microstructure forming through production.…”

Section: Process Mappingmentioning

confidence: 99%

Concurrent Structure and Process Optimization for Minimum Cost Metal Additive Manufacturing

Ulu

Huang

Kara

et al. 2019

Journal of Mechanical Design

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Metals-additive manufacturing (MAM) is enabling unprecedented design freedom and the ability to produce significantly lighter weight parts with the same performance, offering the possibility of significant environmental and economic benefits in many different industries. However, the total production costs of MAM will need to be reduced substantially before it will be widely adopted across the manufacturing sector. Current topology optimization approaches focus on reducing total material volume as a means of reducing material costs, but they do not account for other production costs that are influenced by a part's structure such as machine time and scrap. Moreover, concurrently optimizing MAM process variables with a part's structure has the potential to further reduce production costs. This paper demonstrates an approach to use process-based cost modeling (PBCM) in MAM topology optimization to minimize total production costs, including material, labor, energy, and machine costs, using cost estimates from industrial MAM operations. The approach is demonstrated on various 3D geometries for the electron beam melting (EBM) process with Ti64 material. Concurrent optimization of the part structures and EBM process variables is compared to sequential optimization, and to optimization of the structure alone. The results indicate that, once process variables are considered concurrently, more cost effective results can be obtained with similar amount of material through a combination of (1) building high stress regions with lower power values to obtain larger yield strength and (2) increasing the power elsewhere to reduce the number of passes required, thereby reducing build time. In our case studies, concurrent optimization of the part's structure and MAM process parameters lead to up to 15% lower estimated total production costs and 21% faster build time than optimizing the part's structure alone.

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“…4. This workflow has been designed to serve as a generic template that is applicable to the broad class of microstructure evolution phenomena that are likely to be studied by a variety of techniques (these could include modeling techniques such as phase-field models [48], cellular automata [49], and level-set methods [50] or experimental techniques such as X-ray computed tomography [51,52]). The main steps are listed in blue boxes.…”

Section: Data Science Workflow For Extracting Process-structure Linkagesmentioning

confidence: 99%

“…Physically, this lack of interaction is due to the combined effects of the molten zone geometry and the overlap distance between successive passes of the heat source. The experimental analog of these synthetic phenomena is commonly referred to in the AM community as "lack-of-fusion" defects that result in porosity and regions of unmelted powder inclusions [27,38,51]. In Fig.…”

Section: Case Study: Application To Additive Manufacturing Datasetsmentioning

confidence: 99%

Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data

Popova

Rodgers

Gong

et al. 2017

Integr Mater Manuf Innov

104

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A novel data science workflow is developed and demonstrated to extract process-structure linkages (i.e., reduced-order model) for microstructure evolution problems when the final microstructure depends on (simulation or experimental) processing parameters. This workflow consists of four main steps: data pre-processing, microstructure quantification, dimensionality reduction, and extraction/validation of process-structure linkages. Methods that can be employed within each step vary based on the type and amount of available data. In this paper, this data-driven workflow is applied to a set of synthetic additive manufacturing microstructures obtained using the Potts-kinetic Monte Carlo (kMC) approach. Additive manufacturing techniques inherently produce complex microstructures that can vary significantly with processing conditions. Using the developed workflow, a lowdimensional data-driven model was established to correlate process parameters with the predicted final microstructure. Additionally, the modular workflows developed and presented in this work facilitate easy dissemination and curation by the broader community.

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Evaluating the Effect of Processing Parameters on Porosity in Electron Beam Melted Ti-6Al-4V via Synchrotron X-ray Microtomography

Cited by 127 publications

References 24 publications

Real-time monitoring of laser powder bed fusion process using high-speed X-ray imaging and diffraction

Real-time monitoring of laser powder bed fusion process using high-speed X-ray imaging and diffraction

Concurrent Structure and Process Optimization for Minimum Cost Metal Additive Manufacturing

Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data

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