A dimensionless number for predicting universal processing parameter boundaries in metal powder bed additive manufacturing

Rankouhi, Behzad; Agrawal, Ankur; Pfefferkorn, Frank E.; Thoma, Dan J.

doi:10.1016/j.mfglet.2020.12.002

Cited by 45 publications

(30 citation statements)

References 24 publications

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“…The dimensionless number used in this study has been defined based on some commonly changed input process parameters as well as the physical properties of the materials. The term can be expressed as follows [27] :…”

Section: A Design Of Experiments Using a Dimensionless Numbermentioning

confidence: 99%

“…where, C p , P, k, v, and h are specific heat capacity, laser power, thermal conductivity, laser scanning speed, and hatch spacing, respectively. A detailed description of this dimensionless number can be found elsewhere [27] and has been briefly introduced in the introduction section. Materials with a wide range of thermophysical properties can be well-fitted with the following equation [27] :…”

Section: A Design Of Experiments Using a Dimensionless Numbermentioning

confidence: 99%

“…[26] A recently developed dimensionless number permits a priori prediction of process parameters in LPBF of various metals and alloys to achieve high density. [27] Although details of the strategy are available in Reference 27, a brief summary is supplied here. The dimensionless number, P 1 is defined based on some of the common process parameters as well as physical properties of the materials.…”

Section: Introductionmentioning

confidence: 99%

“…It is shown that for P 1 <61 lack-of-fusion porosity is to be expected, while P 1 > 146 marks the onset of keyhole porosity. [27] The relationship was developed by fitting the relative density to the dimensionless number for a wide class of materials. The dimensionless number P 1 and associated fitted relationship to relative density is incapable of predicting specific values for each input parameter, but rather a range of desirable values.…”

Section: Introductionmentioning

confidence: 99%

“…The dimensionless number-based analytical technique [27] can be used to rapidly define process parameters for an efficient (focused) design of experiments on a subset of process parameters, and high-throughput [15] experimental method can provide rapid printing of samples and measurement of material properties (i.e., density and hardness): together, they form the basis of a methodology (Figure 1) to rapidly, and cost-effectively, define LPBF process parameters for new materials. A process window with reduced defects can be defined in a short period of time.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A High-Throughput Method to Define Additive Manufacturing Process Parameters: Application to Haynes 282

Agrawal

Rankouhi

Magnin

et al. 2021

Metall Mater Trans A

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This paper demonstrates how an analytical and experimental method can be used to rapidly define the additive manufacturing settings for a new alloy where the process parameters were previously unknown. A nickel-based superalloy, Haynes 282, was chosen for the analysis. An experimental matrix of focused processing parameters was predicted with a dimensionless number and 100 samples were printed using the Laser Powder Bed Fusion technique. High-throughput measurements validated the predicted process conditions needed to achieve desired density and hardness. The whole process was completed in 16 hours. The new technique was confirmed with analytical processing maps adopted by the metal additive manufacturing community. With the predicted set of process parameters, a low-throughput analyses of conventional microstructural characterizations and tensile testing were used to test the predictions. The resultant as-fabricated microstructures have refined length scales of both microsegregation and secondary phase distributions. The mechanical properties were comparable within the predicted processing window and exhibited high strength and high ductility.

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Section: A Design Of Experiments Using a Dimensionless Numbermentioning

confidence: 99%

Section: A Design Of Experiments Using a Dimensionless Numbermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A High-Throughput Method to Define Additive Manufacturing Process Parameters: Application to Haynes 282

Agrawal

Rankouhi

Magnin

et al. 2021

Metall Mater Trans A

Self Cite

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A dimensionless analysis to select directed energy deposition process parameters for proper clad formation

Lim

Zhang

Huang

et al. 2022

Int J Adv Manuf Technol

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The growing interest in the directed energy deposition process to fabricate and repair thin wall structures has warranted a deeper understanding in the properties of the method's basic building block: clad formation. In this study, clads obtained by depositing stainless steel 316L (SS316L) powder with three different process parameters, namely laser power, laser traverse speed, and powder mass flow rate, were investigated. Repeatability was ensured through a wide sample range per parameter. From the data measurement, the clads have an average hardness close to the typical 200 Hv of SS316L materials, indicating that Hall-Petch effect is dominant. The study also shows that: (i) Laser power is the most significant factor to clad depth, but has little influence on clad thickness. (ii) Laser traverse speed is the dominant parameter to clad height. (iii) Powder mass flow rate tends to compensate depth reduction with thickness gain, resulting in no noticeable effect on clad height. Increasing laser power was observed to be the most effective way to prevent clads from forming with zero dilution, an indicator to how well the printed clad is bonded to the substrate. A dimensionless analysis was derived from the set of SS316L clads. Through validation with different stainless steel datasets and extrapolation to a larger parametric range, the analysis was demonstrated to be able to facilitate the selection of process parameters to meet given requirements on the clad dimensions. As its application is intuitive, the analysis has the potential to be adopted as a standard preprinting tool that will increase success rate, thus improving the manufacturing turnaround time.

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A new data-driven framework for prediction of molten pool evolution and lack of fusion defects in multi-track multi-layer laser powder bed fusion processes

Parsazadeh

Wu²,

Sharma³

et al. 2022

Int J Adv Manuf Technol

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This study proposes a new data-driven approach to predict the area fraction of lack of fusion and morphology of 3D-printed parts produced using the laser powder bed fusion process. Different processing parameters were considered to develop this methodology, such as laser power, laser speed, hatch spacing, and layer thickness. Melt pool geometrical parameters were numerically predicted using a mathematical model optimized using the Nelder-Mead optimization method and a machine learning model. The Gaussian process was later employed to further modify the layerwise deposition. The accuracy of the proposed methodology was then extended to predictions of the area fraction of lack of fusion and the morphological aspects of the melt pools in 3D-printed parts by comparing them with the experimental observations. The results revealed that this data-driven approach, which is independent of the thermophysical properties of the material, is capable of reasonably predicting the area fraction of lack-of-fusion formed during materials deposition using the laser powder bed fusion process.

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A dimensionless number for predicting universal processing parameter boundaries in metal powder bed additive manufacturing

Cited by 45 publications

References 24 publications

A High-Throughput Method to Define Additive Manufacturing Process Parameters: Application to Haynes 282

A High-Throughput Method to Define Additive Manufacturing Process Parameters: Application to Haynes 282

A dimensionless analysis to select directed energy deposition process parameters for proper clad formation

A new data-driven framework for prediction of molten pool evolution and lack of fusion defects in multi-track multi-layer laser powder bed fusion processes

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