Extreme‐Value Statistics Reveal Rare Failure‐Critical Defects in Additive Manufacturing

Boyce, Brad Lee; Bradley, Salzbrenner,; Rodelas, Jeffrey; Swiler, Laura Painton; Madison, Jonathan D; Jared, Bradley Howell; Shen, Yu‐Lin

doi:10.1002/adem.201700102

Cited by 76 publications

(39 citation statements)

References 47 publications

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“…Details regarding these matters could provide additional insight into the processing window thereby better informing the true state of the manufactured material. As an example, Energy Dispersive Spectroscopy (EDS) revealed elevated levels of both oxygen and nitrogen in the asbuilt parts along with a few other anomalies 41,42 . Without a clear line of sight to the feedstock material and the layer by layer build strategy, it is unclear whether these elemental and phase composition anomalies were precipitated by the initial powder or occurred as a result of the additive process.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

“…As mentioned previously, from mechanical testing, yield strength, ultimate strength, elongation to failure and modulus were evaluated and have been documented in detail 41,42 . For the sake of this discussion, the correlation of pre-existing defects with mechanical response will be limited to yield stress as these represented the highest correlations observed.…”

Section: Correlation Of Defect Metrics With Mechanical Responsementioning

confidence: 99%

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Corroborating tomographic defect metrics with mechanical response in an additively manufactured precipitation-hardened stainless steel

Madison

Underwood

Swiler

et al. 2018

AIP Conference Proceedings

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bhjared@sandia.gov, f) jmrodel@sandia.gov, g) bsalzbr@sandia.govAbstract. The intrinsic relation between structure and performance is a foundational tenant of most all materials science investigations. While the specific form of this relation is dictated by material system, processing route and performance metric of interest, it is widely agreed that appropriate characterization of a material allows for greater accuracy in understanding and/or predicting material response. However, in the context of additive manufacturing, prior models and expectations of material performance must be revisited as performance often diverges from traditional values, even among well explored material systems. This work utilizes micro-computed tomography to quantify porosity and lack of fusion defects in an additively manufactured stainless steel and relates these metrics to performance across a statistically significant population using high-throughput mechanical testing. The degree to which performance in additively manufactured stainless steel can and cannot be correlated to detectable porosity will be presented and suggestions for performing similar experiments will be provided.

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Section: Correlation Of Defect Metrics With Mechanical Responsementioning

confidence: 99%

Corroborating tomographic defect metrics with mechanical response in an additively manufactured precipitation-hardened stainless steel

Madison

Underwood

Swiler

et al. 2018

AIP Conference Proceedings

Self Cite

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“…Pores and powder balling can emerge due to either excessive or insufficient laser power for a given scan speed. The “keyhole effect” occurs when too high power is used at low scan speeds, and the heat source penetrates deep into the material [42,44,45]. These pores can become sources of stress concentration in the part and can lead to crack propagation and failure during processing, or even under more alarming circumstances such as during part operation (see Figure 2 ).…”

Section: Metallic Am – Towards Processing Simulationmentioning

confidence: 99%

Invited review article: Metal-additive manufacturing—Modeling strategies for application-optimized designs

Bandyopadhyay

Traxel

2018

Additive Manufacturing

128

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Next generation, additively-manufactured metallic parts will be designed with application-optimized geometry, composition, and functionality. Manufacturers and researchers have investigated various techniques for increasing the reliability of the metal-AM process to create these components, however, understanding and manipulating the complex phenomena that occurs within the printed component during processing remains a formidable challenge—limiting the use of these unique design capabilities. Among various approaches, thermomechanical modeling has emerged as a technique for increasing the reliability of metal-AM processes, however, most literature is specialized and challenging to interpret for users unfamiliar with numerical modeling techniques. This review article highlights fundamental modeling strategies, considerations, and results, as well as validation techniques using experimental data. A discussion of emerging research areas where simulation will enhance the metal-AM optimization process is presented, as well as a potential modeling workflow for process optimization. This review is envisioned to provide an essential framework on modeling techniques to supplement the experimental optimization process.

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“…[13][14][15][16][17] The recent 3rd Sandia Fracture Challenge focused on metal AM is an interesting example of the challenges involved in predicting the performance of AM components, even provided with significant a priori information of the as-built structure.…”

mentioning

confidence: 99%

“…[10][11][12] Significant challenges remain, however, in material/structural qualification and development of process-structureproperties-performance linkages. [13][14][15][16][17] The recent 3rd Sandia Fracture Challenge focused on metal AM is an interesting example of the challenges involved in predicting the performance of AM components, even provided with significant a priori information of the as-built structure.…”

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confidence: 99%

Integrated Computational and Experimental Methods for Additive Manufacturing

Bishop

Clarke

Wagner

2018

JOM

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Additive manufacturing (AM) holds the promise of revolutionizing current paradigms in engineering design, manufacturing, and material and structural performance.1-3 However, full realization of the possibilities of metal-based AM will require significant advances in both the process and performance modeling of additively manufactured materials and structures. [4][5][6][7][8] Modeling and simulation of AM processes is particularly challenging, since it involves multiple time and space scales.9 Furthermore, metal-based AM involves multiple physics, and is thus multidisciplinary in nature, requiring interdisciplinary research efforts. Fortunately, the unique manufacturing process of AM affords the possibility of tight integration of in situ measurements and computational modeling.10-12 Significant challenges remain, however, in material/structural qualification and development of process-structureproperties-performance linkages. [13][14][15][16][17] The recent 3rd Sandia Fracture Challenge focused on metal AM is an interesting example of the challenges involved in predicting the performance of AM components, even provided with significant a priori information of the as-built structure. 18With the goal of fostering communication and collaborations between the AM computational and experimental communities, a conference was recently held at the Colorado School of Mines, on September 6-8, 2017. 19 The conference brought together researchers from both the computational mechanics and materials science research communities involved in advancing the state of the art in process and performance modeling of additively manufactured (AM) materials and structures. There were over 31 invited speakers from academia, industry, and national laboratories, organized in a single-track session to foster communication. A central theme of the conference was integration of computational modeling and experiments, leveraging the unique manufacturing aspects of AM, to improve overall modeling predictivity and enable process optimization. This conference was jointly sponsored by the U. While the conference did not have published proceedings, presenters were invited to submit articles for a special topic in JOM. The following three articles have been assembled from this process. The first article by Roehling et al. describes the use of single-track laser-melting experiments to explore process parameters and their effects on microstructure. Experimental results were used to benchmark a phase-field model that simulated the rapid solidification in conditions relevant for AM. The second paper by Lindwall et al. models the creation of bulk metallic glasses using a powder-bed fusion process. The study evaluates computational approaches that can be used to increase the computational speed while maintaining accurate temperatures in critical regions. The accurate prediction of temperature history is particularly important in ensuring that reheated material does not become devitrified. The third paper by Donoso and Peters uses a combined discrete-contin...

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Extreme‐Value Statistics Reveal Rare Failure‐Critical Defects in Additive Manufacturing

Cited by 76 publications

References 47 publications

Corroborating tomographic defect metrics with mechanical response in an additively manufactured precipitation-hardened stainless steel

Corroborating tomographic defect metrics with mechanical response in an additively manufactured precipitation-hardened stainless steel

Invited review article: Metal-additive manufacturing—Modeling strategies for application-optimized designs

Integrated Computational and Experimental Methods for Additive Manufacturing

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