Linking process, structure, property, and performance for metal-based additive manufacturing: computational approaches with experimental support

Smith, Jacob W.; Xiong, Wei; Yan, Wentao; Lin, Stephen; Cheng, Puikei; Kafka, Orion L.; Wagner, Gregory J.; Cao, Jian; Liu, Wing Kam

doi:10.1007/s00466-015-1240-4

Cited by 212 publications

(96 citation statements)

References 79 publications

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“…Despite the significant research efforts on the parameter optimization/control in AM process, fabricating a defect‐free part with uniform microstructure has not been fully achieved yet . Overcoming these challenges demands a thorough understanding of the relationships among process parameters, thermal history, solidification, resultant microstructure, and the mechanical behaviour of AM parts, which is still an open issue …”

Section: Introductionmentioning

confidence: 99%

Fatigue life prediction of additively manufactured material: Effects of surface roughness, defect size, and shape

Yadollahi

Mahtabi

Khalili

et al. 2018

Fatigue Fract Eng Mat Struct

182

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In this paper, the effects of process‐induced voids and surface roughness on the fatigue life of an additively manufactured material are investigated using a crack closure‐based fatigue crack growth model. Among different sources of damage under cyclic loadings, fatigue because of cracks originated from voids and surface discontinuities is the most life‐limiting failure mechanism in the parts fabricated via powder‐based metal additive manufacturing (AM). Hence, having the ability to predict the fatigue behaviour of AM materials based on the void features and surface texture would be the first step towards improving the reliability of AM parts. Test results from the literature on Inconel 718 fabricated via a laser powder bed fusion (L‐PBF) method are analysed herein to model the fatigue behaviour based on the crack growth from semicircular/elliptical surface flaws. The fatigue life variations in the specimens with machined and as‐built surface finishes are captured using the characteristics of voids and surface profile, respectively. The results indicate that knowing the statistical range of defect size and shape along with a proper fatigue analysis approach provides the opportunity of predicting the scatter in the fatigue life of AM materials. In addition, maximum valley depth of the surface profile can be used as an appropriate parameter for the fatigue life prediction of AM materials in their as‐built surface condition.

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Section: Introductionmentioning

confidence: 99%

Fatigue life prediction of additively manufactured material: Effects of surface roughness, defect size, and shape

Yadollahi

Mahtabi

Khalili

et al. 2018

Fatigue Fract Eng Mat Struct

182

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“…The fabrication parameters for a qualified functionally graded component are traditionally determined through a long and costly experimental trial-and-error process. Effective numerical heat transfer modeling of the process has become a powerful tool for understanding and optimizing the EBM process [5], thereby reducing the extent of required experimental studies.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale modeling of electron beam melting of functionally graded materials

et al. 2016

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a b s t r a c tElectron Beam Melting (EBM) is a promising powder-based metal Additive Manufacturing (AM) technology. This AM technique is opening new avenues for Functionally Graded Materials (FGMs). However, the manufacturing process, which is largely driven by the rapidly evolving temperature field, poses a significant challenge for accurate experimental measurement. In this study, we develop a novel multiscale heat transfer modeling framework to investigate the EBM process of fabricating FGMs. Our heat source model mechanistically describes heating phenomena based on simulation of micro-scale electron-material interactions. It is capable of accounting for the material properties and electron beam properties that depend on experimental setup. The heat source model is utilized in the thermal evolution model of individual powder particles at the meso-scale to elucidate the melting and coalescing processes for mixed powder particles of different materials and different sizes. Another meso-scale simulation is conducted to evaluate the effective thermal conductivity of the original powder bed for the macro-scale model. A macro-scale heat transfer model is developed, in which the coalescence state is tracked to determine the effective material properties of the powder bed. Predictions of molten pool size are compared against published experimental results for validation.

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“…[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.…”

mentioning

confidence: 99%

“…[4][5][6][7][8] Modeling and simulation of AM processes is particularly challenging, since it involves multiple time and space scales.…”

mentioning

confidence: 99%

“…[1][2][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.…”

mentioning

confidence: 99%

See 1 more Smart Citation

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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Linking process, structure, property, and performance for metal-based additive manufacturing: computational approaches with experimental support

Cited by 212 publications

References 79 publications

Fatigue life prediction of additively manufactured material: Effects of surface roughness, defect size, and shape

Fatigue life prediction of additively manufactured material: Effects of surface roughness, defect size, and shape

Multi-scale modeling of electron beam melting of functionally graded materials

Integrated Computational and Experimental Methods for Additive Manufacturing

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