Three-Jet Powder Flow and Laser–Powder Interaction in Laser Melting Deposition: Modelling Versus Experimental Correlations

Coupling Characteristics of Powder and Laser of Coaxial Cone Nozzle for Laser Direct Metal Deposition: Numerical Simulation and Experimental Study

Laser direct metal deposition (LDMD) enables not only the preparation of high-performance coatings on the surfaces of low-property materials but also the three-dimensional direct manufacturing and re-manufacturing of parts. In the LDMD process, the spatial coupling characteristics of the powder flow and the laser beam are the key factors affecting the forming quality of the cladding layer. Based on the gas-solid two-phase flow theory, a numerical model of coaxial powder feeding was established by CFD. The powder flow characteristics of the lower part of the nozzle, the powder particle motion trajectory, and the optical-powder spatial coupling morphology and law were studied, and the relationship between the powder flow morphology, laser beam, and powder utilization was explored. On this basis, the law between the optical-powder coupling characteristics and the geometric characteristics of the cladding layer is discussed in conjunction with LDMD experiments. The results show that the powder concentration scalar located in the focal plane of the laser beam can be used to visualize the optical-powder coupling morphology. When the powder feeding speed exceeds the loading capacity of the carrier gas flow, the powder concentration in the center of the spot and the powder utilization rate decrease. When the carrier gas flow rate is 4.0 L/min and the powder feeding rate is 4.0 g/min, the best utilization rate achieved is 81.4%. In addition, the H (height) of the cladding layer is more sensitive to changes in the powder concentration than the W (width). These findings provide new ideas for nozzle structure design and the optimization of LDMD parameters.

One-Temperature Analytical Model for Femto-/Atto-Second Laser–Metals Drilling: A Novel Approach

Recently, ultrafast lasers have been developed and potentially become a point of interest worldwide, as their interaction with matter is yet unknown and can be mediated by new physical mechanisms. Real-time experimentation requires enormous costs, and there is therefore a need to develop computational models for this domain. By keeping in view this idea, a non-Fourier heat equation has solved the case of ultrafast laser–material interaction. Initial and boundary conditions were considered, and a one-dimensional mathematical model was presented. The simulations were compared with the experimental results for ultrashort laser–metallic sample interaction, and a close correlation was proven. It was found that the coupling of electron–phonon becomes “zero” due to short laser–material interaction time. The propagation of thermal waves was identified due to non-Fourier heat implementation. When the pulse duration increases, the variation in the thermal distribution becomes trivial due to an inverse correlation between the pulse duration and total energy within the pulse. When the laser–material interaction time decreases from fs to as, the generation of thermal waves increases and the powerful laser intensity acts as a shock wave during laser–material interaction, which causes a higher intensity of the thermal wave.

Influence of Silicon Carbide on Direct Powder Bed Selective Laser Process (Sintering/Melting) of Alumina

The powder bed selective laser process (sintering/melting) has revolutionised many industries, including aerospace and biomedicine. However, PBSLP of ceramic remains a formidable challenge. Here, we present a unique slurry-based approach for fabricating high-strength ceramic components instead of traditional PBSLP. A special PBSLP platform capable of 1000 °C pre-heating was designed for this purpose. In this paper, PBSLP of Al₂O₃ was accomplished at different SiC loads up to 20 wt%. Several specimens on different laser powers (120 W to 225 W) were printed. When the SiC content was 10 wt% or more, the chemical interaction made it difficult to process. Severe melt pool disturbances led to poor sintering and melting. The structural analysis revealed that the micro-structure was significantly affected by the weight fraction of SiC. Interestingly, when the content was less than 2 wt%, it showed significant improvement in the microstructure during PBSLP and no effects of LPS or chemical interaction. Particularly, a crack pinning effect could be clearly seen at 0.5 wt%.

Spatter Formation and Splashing Induced Defects in Laser-Based Powder Bed Fusion of AlSi10Mg Alloy: A Novel Hydrodynamics Modelling with Empirical Testing

Powder spattering and splashing in the melt pool are common phenomena during Laser-based Powder Bed Fusion (LPBF) of metallic materials having high fluidity. For this purpose, analytical and computational fluid dynamics (CFD) models have been deduced for the LPBF of AlSi10Mg alloy. The single printed layer’s dimensions were estimated using primary operating conditions for the analytical model. In CFD modelling, the volume of fluid and discrete element modelling techniques were applied to illustrate the splashing and spatter phenomena, providing a novel hydrodynamics CFD model for LPBF of AlSi10Mg alloy. The computational results were compared with the experimental analyses. A trial-and-error method was used to propose an optimized set of parameters for the LPBF of AlSi10Mg alloy. Laser scanning speed, laser spot diameter and laser power were changed. On the other hand, the powder layer thickness and hatch distance were kept constant. Following on, 20 samples were fabricated using the LPBF process. The printed samples’ microstructures were used to select optimized parameters for achieving defect-free parts. It was found that the recoil pressure, vaporization, high-speed vapor cloud, Marangoni flow, hydraulic pressure and buoyancy are all controlled by the laser-material interaction time. As the laser-AlSi10Mg material interaction period progresses, the forces presented above become dominant. Splashing occurs due to a combination of increased recoil pressure, laser-material interaction time, higher material’s fluidity, vaporization, dominancy of Marangoni flow, high-speed vapor cloud, hydraulic pressure, buoyancy, and transformation of keyhole from J-shape to reverse triangle-shape that is a tongue-like protrusion in the keyhole. In the LPBF of AlSi10Mg alloy, only the conduction mode melt flow has been determined. For multi-layers printing of AlSi10Mg alloy, the optimum operating conditions are laser power = 140 W, laser spot diameter = 180 µm, laser scanning speed = 0.6 m/s, powder layer thickness = 50 µm and hatch distance = 112 µm. These conditions have been identified using sample microstructures.

Keyhole Formation by Laser Drilling in Laser Powder Bed Fusion of Ti6Al4V Biomedical Alloy: Mesoscopic Computational Fluid Dynamics Simulation versus Mathematical Modelling Using Empirical Validation

In the laser powder bed fusion (LPBF) process, the operating conditions are essential in determining laser-induced keyhole regimes based on the thermal distribution. These regimes, classified into shallow and deep keyholes, control the probability and defects formation intensity in the LPBF process. To study and control the keyhole in the LPBF process, mathematical and computational fluid dynamics (CFD) models are presented. For CFD, the volume of fluid method with the discrete element modeling technique was used, while a mathematical model was developed by including the laser beam absorption by the powder bed voids and surface. The dynamic melt pool behavior is explored in detail. Quantitative comparisons are made among experimental, CFD simulation and analytical computing results leading to a good correspondence. In LPBF, the temperature around the laser irradiation zone rises rapidly compared to the surroundings in the powder layer due to the high thermal resistance and the air between the powder particles, resulting in a slow travel of laser transverse heat waves. In LPBF, the keyhole can be classified into shallow and deep keyhole mode, controlled by the energy density. Increasing the energy density, the shallow keyhole mode transforms into the deep keyhole mode. The energy density in a deep keyhole is higher due to the multiple reflections and concentrations of secondary reflected beams within the keyhole, causing the material to vaporize quickly. Due to an elevated temperature distribution in deep keyhole mode, the probability of pores forming is much higher than in a shallow keyhole as the liquid material is close to the vaporization temperature. When the temperature increases rapidly, the material density drops quickly, thus, raising the fluid volume due to the specific heat and fusion latent heat. In return, this lowers the surface tension and affects the melt pool uniformity.

Mesoscopic Computational Fluid Dynamics Modelling for the Laser-Melting Deposition of AISI 304 Stainless Steel Single Tracks with Experimental Correlation: A Novel Study

For laser-melting deposition (LMD), a computational fluid dynamics (CFD) model was developed using the volume of fluid and discrete element modeling techniques. A method was developed to track the flow behavior, flow pattern, and driving forces of liquid flow. The developed model was compared with experimental results in the case of AISI 304 stainless steel single-track depositions on AISI 304 stainless steel substrate. A close correlation was found between experiments and modeling, with a deviation of 1–3%. It was found that the LMD involves the simultaneous addition of powder particles that absorb a significant amount of laser energy to transform their phase from solid to liquid, resulting in conduction-mode melt flow. The bubbles within the melt pool float at a specific velocity and escape from the melt pool throughout the deposition process. The pores are generated if the solid front hits the bubble before escaping the melt pool. Based on the simulations, it was discovered that the deposited layer’s counters took the longest time to solidify compared to the overall deposition. The bubbles strived to leave through the contours in an excess quantity, but became stuck during solidification, resulting in a large degree of porosity near the contours. The stream traces showed that the melt flow adopted a clockwise vortex in front of the laser beam and an anti-clockwise vortex behind the laser beam. The difference in the surface tension between the two ends of the melt pool induces “thermocapillary or Benard–Marangoni convection” force, which is insignificant compared to the selective laser melting process. After layer deposition, the melt region, mushy zone, and solidified region were identified. When the laser beam irradiates the substrate and powder particles are added simultaneously, the melt adopts a backwards flow due to the recoil pressure and thermocapillary or Benard–Marangoni convection effect, resulting in a negative mass flow rate. This study provides an in-depth understanding of melt pool dynamics and flow pattern in the case of LMD additive manufacturing technique.

A State-of-the-Art Review on Integral Transform Technique in Laser-Material Interaction: Fourier and Non-Fourier Heat Equations

Heat equations can estimate the thermal distribution and phase transformation in real-time based on the operating conditions and material properties. Such wonderful features have enabled heat equations in various fields, including laser and electron beam processing. The integral transform technique (ITT) is a powerful general-purpose semi-analytical/numerical method that transforms partial differential equations into a coupled system of ordinary differential equations. Under this category, Fourier and non-Fourier heat equations can be implemented on both equilibrium and non-equilibrium thermo-dynamical processes, including a wide range of processes such as the Two-Temperature Model, ultra-fast laser irradiation, and biological processes. This review article focuses on heat equation models, including Fourier and non-Fourier heat equations. A comparison between Fourier and non-Fourier heat equations and their generalized solutions have been discussed. Various components of heat equations and their implementation in multiple processes have been illustrated. Besides, literature has been collected based on ITT implementation in various materials. Furthermore, a future outlook has been provided for Fourier and non-Fourier heat equations. It was found that the Fourier heat equation is simple to use but involves infinite speed heat propagation in comparison to the non-Fourier heat equation and can be linked with the Two-Temperature Model in a natural way. On the other hand, the non-Fourier heat equation is complex and involves various unknowns compared to the Fourier heat equation. Fourier and Non-Fourier heat equations have proved their reliability in the case of laser-metallic materials, electron beam-biological and -inorganic materials, laser-semiconducting materials, and laser-graphene material interactions. It has been identified that the material properties, electron-phonon relaxation time, and Eigen Values play an essential role in defining the precise results of Fourier and non-Fourier heat equations. In the case of laser-graphene interaction, a restriction has been identified from ITT. When computations are carried out for attosecond pulse durations, the laser wavelength approaches the nucleus-first electron separation distance, resulting in meaningless results.

Laser Coatings via State-of-the-Art Additive Manufacturing: A Review

Ceramics and ceramic-reinforced metal matrix composites (CMMCs) demonstrate high wear resistance, excellent chemical inertness, and exceptional properties at elevated temperatures. These characteristics are suitable for their utilization in biomedical, aerospace, electronics, and other high-end engineering industries. The aforementioned performances make them difficult to fabricate via conventional manufacturing methods, requiring high costs and energy consumption. To overcome these issues, laser additive manufacturing (LAM) techniques, with high-power laser beams, were developed and extensively employed for processing ceramics and ceramic-reinforced CMMCs-based coatings. In respect to other LAM processes, laser melting deposition (LMD) excels in several aspects, such as high coating efficiency and lower labor cost. Nevertheless, difficulties such as poor bonding between coating and substrate, cracking, and reduced toughness are still encountered in some LMD coatings. In this article, we review recent developments in the LMD of ceramics and CMMCs-based coatings. Issues and solutions, along with development trends, are discussed and summarized in support of implementing this technology for current industrial use.

Numerical Analysis of Keyhole and Weld Pool Behaviors in Ultrasonic-Assisted Plasma Arc Welding Process

The acoustic radiation force driving the plasma jet and the ultrasound reflection at the plasma arc-weld pool interface are considered to modify the formulas of gas shear stress and plasma arc pressure on the anode surface in ultrasonic-assisted plasma arc welding (U-PAW). A transient model taking into account the dynamic changes of heat flux, gas shear stress, and arc pressure on the keyhole wall is developed. The keyhole and weld pool behaviors are numerically simulated to predict the heat transfer and fluid flow in the weld pool and dynamic keyhole evolution process. The model is experimentally validated. The simulation results show that the acoustic radiation force increases the plasma arc velocity, and then increases both the plasma arc pressure and the gas shear stress on the keyhole wall, so that the keyholing capability is enhanced in U-PAW.

Artificial Neural Network Algorithms for 3D Printing

Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. Therefore, it is a complex task to develop a correlation between process parameters and printed parts' properties via traditional optimization methods. A machine-learning technique was recently validated to carry out intricate pattern identification and develop a deterministic relationship, eliminating the need to develop and solve physical models. In machine learning, artificial neural network (ANN) is the most widely utilized model, owing to its capability to solve large datasets and strong computational supremacy. This study compiles the advancement of ANN in several aspects of 3D printing. Challenges while applying ANN in 3D printing and their potential solutions are indicated. Finally, upcoming trends for the application of ANN in 3D printing are projected.