A dynamic volumetric heat source model for laser additive manufacturing

Effect of thermal-fluidic transport on the temperature distribution and the melt pool in laser powder bed fusion of Ti6Al4V

On the effect of thin-wall thickness on melt pool dimensions in laser powder-bed fusion of Hastelloy X: Numerical modeling and experimental validation

Avoiding heat source calibration for finite element modeling of the laser powder bed fusion process

PurposeThis study aims to investigate the relationship between melt pool dimensions and acoustic emission (AE) signal magnitudes obtained during laser powder bed fusion (L-PBF) process of austenitic stainless steel. Specifically, by varying laser power and scan speed, the widths, depths and aspect ratios of melt pools were compared with AE signal magnitudes across a frequency range of 100–200 kHz.Design/methodology/approachExperiments were conducted under different laser powers at a fixed scan speed and scan speeds at a fixed laser power. Melt pool dimensions were measured from cross-sectional optical images, and AE signals were obtained using a piezoelectric AE sensor installed beneath the build plate. Short-Time Fourier Transform (STFT) was applied to AE signals, and the magnitudes of frequency components were obtained.FindingsA strong correlation between melt pool dimensions and STFT magnitude was obtained. Pearson correlation coefficients between melt pool dimensions and STFT magnitudes were above 0.9 and the p-values were below 0.05. Increasing the laser energy enlarged the volume of melt pool and intensified the oscillation of melt pool. When scan speed exceeded 1,100 mm/s, STFT magnitude showed a slight increase owing to the increase in the vapor pressure.Originality/valuePrevious studies used AE signals to detect defects, but this study found a correlation between STFT magnitude and melt pool dimensions in L-PBF process. It was also found that STFT magnitude was more affected by vapor pressure at higher scan speeds. Monitoring STFT magnitude can help to understand melt pool dynamics, maintain process consistency and identify irregularities in real time.

Development and validation of a material evaporation assisted thermal model for time-efficient calculation of thermal and solidification parameters during laser powder bed fusion process for Ti6Al4V

Light-weight materials, such as magnesium and its alloys, have excellent mechanical properties, such as high specific strength, high specific modulus, good damping ability, thermal conductivity, etc. These properties make them a potential candidate for aerospace and automotive applications. The inability of the conventional manufacturing processes to manufacture precise and high-quality magnesium parts drives the exploring of new manufacturing techniques. In this study, the feasibility of additive manufacturing of AZ91D magnesium alloy using Selective Laser Melting (SLM) process has been investigated. Numerical modelling and simulations are performed to study the melt pool shape and size evolution, temperature and velocity fields, temperature gradients and cooling rate during the SLM of AZ91D powder, and the influence of the input laser power is described. The model considers conduction heat transfers and radiation heat losses from the powder bed surface, melting and solidification and convection in the melt pool. The simulation results provide preliminary insights of the complex physical phenomena occurring during SLM of AZ91D by describing the interaction between the laser source and the magnesium powder. The temperature and the velocity field were found playing a significant role in deciding the melt pool dimensions and geometry. An attempt has been made to describe the melt pool stability by calculating the ratios of melt pool length to width and melt pool width to depth. The spatiotemporal variation of temperature shows that very large temperature gradients and cooling rates (of the order of 107 K/m and 106 K/s, respectively) can be achieved. The high cooling rates are likely to result in the development of finer microstructure and so improved mechanical properties.

Fusion-based additive manufacturing of metal is particularly challenging to simulate due to the broad range of length/time scales of multiphysics phenomena to bridge. Integrated Computational Materials Engineering (ICME) offers a promising route to accelerate the design of new materials, optimize manufacturing processes, and thus improve material properties and performance. ICME heavily relies on linking multiple modelling techniques relevant to distinct length/time scales and/or different physics. While a wide range of physics-based and predictive models exist, the efficient linking of these models remains challenging. In the context of additive manufacturing, interdependent phenomena across scales include: multicomponent alloys with complex phase diagrams, multiple phase transformations and temperature-dependent properties; complex thermal exchanges in the vicinity of the melt pool; and the development of nonequilibrium grain microstructures within this melt pool. A multiscale modeling strategy (i.e. micro and macro) has been developed to simulate powder-bed fusion of metallic alloys, which combines: (1) Temperaturedependent alloy properties and phase diagrams calculated by CalPhaD, (2) macroscale thermal and thermo-mechanical simulations of the material addition and fusion using finite elements (FE), (3) Phase-field (PF) simulations of the melt pool’s solidification at the microscopic scale, and (4) Simulations of the microscopic solidification of the melt pool using cellular automaton (CA). Realistic processing parameters are used in the methodology to simulate selective laser melting (SLM) of an Inconel 718 alloy. In the first step, three-dimensional thermal and thermo-mechanical frameworks were developed – using an in-house finite element code (IRIS) and a material library (MUESLI) implemented in C++ programming language – by considering these four factors: (1) Representing the thermal input from the scanning laser, (2) A material model to specify the temperature-dependent material property in the cyclic heating and cooling environment, (3) A physics-based method to explain the layer build-up process, and (4) A laser material interaction model to account for transient thermo-mechanical phenomena. Then, the thermal model was coupled with CalPhaD and PF methods to predict the formation of microstructures at the scale of the entire melt pool. In this part, the effect of temperature-dependent properties and the necessity of taking into account differences in properties between the powder bed and the dense material have been discussed. We perform an appropriately-converged PF solidification simulation at the scale of the entire melt pool using a two-dimensional longitudinal slice of the thermal field calculated through FE simulations. This calculation has over one billion grid points, yet is performed on a single cluster node with eight graphics processing units (GPUs). These microscale simulations, with a level of detail down to individual dendrites, offer new insight into the selection of grain texture via polycrystalline growth competition under realistic SLM conditions. Finally, comparisons between phase-field (PF) and cellular automaton (CA) simulations of polycrystalline growth in a two-dimensional melt pool under additive manufacturing (powder-bed fusion) relevant conditions have been performed. The generated grain structures from local (point-by-point) measurements and grain orientation distributions that were averaged over various simulations have been compared. We investigated how the melt pool aspect ratio and the CA spatial discretization level affected the selected grain texture. Our simulations demonstrate that only PF simulations are capable of capturing the fine-grained microscopic features related to transient growth conditions and solid-liquid interface stability, such as the initial planar growth stage before its cellular/dendritic destabilization or the early elimination of unfavorably oriented grains due to neighbor grain sidebranching. CA grid refinement can only partially address the resulting disagreement between PF and CA predictions. In addition, with some variability on the CA grid and melt pool shape, overall grain distributions averaged throughout the entire melt pools of several simulations appear to lead to a noticeably better agreement between PF and CA. This research offers a helpful step in that direction by quantitatively comparing both approaches at process-relevant length and time scales, even though more effort is still needed, especially to identify the appropriate selection of CA spatial discretization and its link to characteristic microstructural length scales.

Texture control of 316L parts by modulation of the melt pool morphology in selective laser melting

A testbed for investigation of laser powder bed fusion at elevated atmospheric pressure

Effects of heat distribution during cold metal transfer arc welding on galvanized steel using volumetric heat source model

Numerical modeling of melt-pool behavior in selective laser melting with random powder distribution and experimental validation

Among all the metal additive manufacturing processes, the Laser Powder Bed Fusion Process (LPBF) is widely researched for fabricating complex and intricate parts, especially in aerospace, automobile, medical, etc. The process uses a fine raw powder bed that melts and solidifies to fabricate components. Multi-material systems are widely recognized for offering advantages over mono-material systems in various applications; however, their LPBF processing is challenging. The present study has developed a semi-analytical model to study the melt-pool dynamics of multi-material systems, especially metal-metal multi-material systems. The novelty of the formulation lies in incorporating temperature-dependent thermophysical material properties for multi-material systems by employing rule-of-mixture material modelling. The laser heat source is assumed to be a moving two-dimensional Gaussian heat source, which depicts a real-process scenario of LPBF. The Finite volume method has been employed to solve the discretized spatial and temporal domain to estimate the heat loss due to convection and conduction iteratively to obtain the temperature distribution in the powder bed without considering the Marangoni effect, radiative heat loss, and recoil pressure. This model considers the phase change during the melting and the temperature-dependent Thermo-physical properties to get an accurate simulation result. The model analyses the melt pool dynamics along with the molten zone’s depth and width dimensions in the laser-material interaction domain with varying process parameters in the LPBF, such as laser power, laser scan velocity, etc. The simulation results strongly correlate process parameters and melt pool dimensions at low computational cost. The results concluded that the increasing volume fraction of 10 to 40% for SS304L causes the melt pool depth and width to decrease by approximately 35.71% and 26.32%, respectively.

Heat transfer simulations of selective laser melting process based on volumetric heat source with powder size consideration

Development of avocado shape heat source model for finite element based heat transfer analysis of high-velocity arc welding process

Selective Laser Melting (SLM) and Electron Beam Additive Manufacturing (EBAM) are two of the most promising additive manufacturing technologies that can make full density metallic components using layer-by-layer fabrication methods. In this study, three-dimensional computational fluid dynamics models with Ti-6Al-4V powder were developed to conduct numerical simulations of both the SLM and EBAM processes. A moving conical volumetric heat source with Gaussian distribution and temperature-dependent thermal properties were incorporated in the thermal modeling of both processes. The melt-pool geometry and its thermal behavior were investigated numerically and results for temperature profile, cooling rate, variation in specific heat, density, thermal conductivity, and enthalpy were obtained with similar heat source specifications. Results obtained from the two models at the same maximum temperature of the melt pool were then compared to describe their deterministic features to be considered for industrial applications. Validation of the modeling was performed by comparing the EBAM simulation results with the EBAM experimental results for melt pool geometry.