Analytical and Numerical Temperature Prediction in Direct Metal Deposition of Ti6Al4V

Batut, Benoit De La; Fergani, Omar; Brøtan, Vegard; Bambach, Markus; Mansouri, Mohamed El

doi:10.3390/jmmp1010003

Cited by 27 publications

(11 citation statements)

References 28 publications

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“…The aforementioned heat source solutions were originally developed by Carslaw and Jaeger [ 45 ]. The moving point heat source was further developed with consideration of heat source shape for temperature prediction in SLM [ 46 ]. Rosenthal developed a moving line heat source solution to predict the temperature in welding for an infinite thin plate [ 47 ].…”

Section: Introductionmentioning

confidence: 99%

Analytical Modeling of In-Process Temperature in Powder Bed Additive Manufacturing Considering Laser Power Absorption, Latent Heat, Scanning Strategy, and Powder Packing

et al. 2019

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Temperature distribution gradient in metal powder bed additive manufacturing (MPBAM) directly controls the mechanical properties and dimensional accuracy of the build part. Experimental approach and numerical modeling approach for temperature in MPBAM are limited by the restricted accessibility and high computational cost, respectively. Analytical models were reported with high computational efficiency, but the developed models employed a moving coordinate and semi-infinite medium assumption, which neglected the part dimensions, and thus reduced their usefulness in real applications. This paper investigates the in-process temperature in MPBAM through analytical modeling using a stationary coordinate with an origin at the part boundary (absolute coordinate). Analytical solutions are developed for temperature prediction of single-track scan and multi-track scans considering scanning strategy. Inconel 625 is chosen to test the proposed model. Laser power absorption is inversely identified with the prediction of molten pool dimensions. Latent heat is considered using the heat integration method. The molten pool evolution is investigated with respect to scanning time. The stabilized temperatures in the single-track scan and bidirectional scans are predicted under various process conditions. Close agreements are observed upon validation to the experimental values in the literature. Furthermore, a positive relationship between molten pool dimensions and powder packing porosity was observed through sensitivity analysis. With benefits of the absolute coordinate, and high computational efficiency, the presented model can predict the temperature for a dimensional part during MPBAM, which can be used to further investigate residual stress and distortion in real applications.

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Section: Introductionmentioning

confidence: 99%

Analytical Modeling of In-Process Temperature in Powder Bed Additive Manufacturing Considering Laser Power Absorption, Latent Heat, Scanning Strategy, and Powder Packing

et al. 2019

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“…In our work. P = [600, 1000], 𝒗 = [8,12] and 𝑫 = 2 mm (constant). As energy density is a combination of laser power and scanning speed, the graph showed a similar trend as laser power, as shown in Figure 6 (b).…”

Section: Effects Of Process Variables On Materials Strength Behaviorsmentioning

confidence: 99%

Prediction of Mechanical Behaviors of Additively Manufactured SS 316L via Machine Learning

Era

Grandhi²,

Liu³

2022

Preprint

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Directed energy deposition (DED) is a rising field in the arena of metal additive manufacturing and has extensive applications in aerospace, medical and rapid prototyping. The process parameters, such as laser power, scanning speed, and layer thickness, play an important role in controlling and affecting the properties of DED fabricated parts. Nevertheless, both experimental and simulation methods have shown constraints and limited ability to generate accurate and efficient computational predictions on the correlations between the process parameters and the final part quality. In this work, data-driven machine learning models using XGBoost and Random Forest have been built and applied to predict the tensile behaviors of the stainless steel 316L parts by DED with variation in process parameters. The results suggest that both models can predict the tensile properties, e.g., yield strength, elongation, and ultimate tensile strength, of the fabricated parts and compute the significance of the factors affecting the quality of the part. The performance of the models was then compared with the Ridge Regression. XGBoost outperformed both Ridge Regression and Random Forest.

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“…This is due to the extremely large number of spots that are required to melt the cross-section of a single layer. This problem can be partially solved with an analytical solution of the thermal field [22,23]. Nevertheless, this solution returns the global temperature of a single layer and it is not possible to distinguish between different heat distributions due to the multiple scanning strategies.…”

Section: Finite Element Methods -Simulation Numerical Analysis and Somentioning

confidence: 99%

Finite Element Thermal Analysis of Metal Parts Additively Manufactured via Selective Laser Melting

Pitassi¹,

Savoia²,

Fontanari³

et al. 2018

Finite Element Method - Simulation, Numerical Analysis and Solution Techniques

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In this chapter, a three-dimensional finite element model is developed to simulate the thermal behavior of the molten pool in selective laser melting (SLM) process. Laser-based additive manufacturing (AM) is a near net shape manufacturing process able to produce 3D objects. They are layer-wise built through selective melting of a metal powder bed. The necessary energy is provided by a laser source. The interaction between laser and material occurs within a few microseconds, hence the transient thermal behavior must be taken into account. A calibration procedure is carried out to fit the numerical solution with the experimental data. Once the calibration has corrected the thermal parameters, a dynamic mesh refinement is applied to reduce the computational cost. The scanning strategy adopted by the laser is simulated by a path simulator built using MatLab ® , while numerical analysis is carried out using ANSYS ® , a commercial finite element software. To improve the performance of the simulation, the two codes interact each other to solve the analysis. Temperature distribution and geometrical feature of the molten pool under different process conditions are investigated. Results from the FE analysis provide guidance for setting up the optimization of process parameters and develop a base for further residual stress analysis.

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Analytical and Numerical Temperature Prediction in Direct Metal Deposition of Ti6Al4V

Cited by 27 publications

References 28 publications

Analytical Modeling of In-Process Temperature in Powder Bed Additive Manufacturing Considering Laser Power Absorption, Latent Heat, Scanning Strategy, and Powder Packing

Analytical Modeling of In-Process Temperature in Powder Bed Additive Manufacturing Considering Laser Power Absorption, Latent Heat, Scanning Strategy, and Powder Packing

Prediction of Mechanical Behaviors of Additively Manufactured SS 316L via Machine Learning

Finite Element Thermal Analysis of Metal Parts Additively Manufactured via Selective Laser Melting

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