Mechanisms of Spatter Formation and Suppression in Aluminum Alloy via Hybrid Fiber–Semiconductor Laser System

Abstract

This study investigates the spatter suppression mechanism in aluminum alloy welding using a hybrid fiber–semiconductor laser system. By integrating high-speed photography and three-dimensional thermal-fluid coupling numerical simulations, the spatter formation process and its suppression mechanisms were systematically analyzed. The results indicate that spatter formation is primarily governed by surface tension and recoil pressure. In single fiber laser welding, concentrated laser energy induces a steep temperature gradient on the molten pool surface, triggering a strong Marangoni effect and subsequent spatter generation. In contrast, the hybrid laser system optimizes energy distribution, reducing the temperature gradient and weakening the Marangoni effect, thereby suppressing spatter. Additionally, the hybrid laser stabilizes molten pool flow through uniform recoil pressure distribution, further inhibiting spatter formation. Experimental results demonstrate that the hybrid fiber–semiconductor laser system significantly reduces spatter, improving welding quality and stability. This study provides theoretical and technical support for optimizing aluminum alloy laser welding.

1. Introduction

Aluminum alloys are widely utilized in the manufacturing of critical components in aerospace and new energy vehicles due to their high strength-to-weight ratio [1]. Laser welding has become a predominant method for joining aluminum alloys owing to its high energy density, rapid processing speed, and operational flexibility [2]. However, the inherent low viscosity and surface tension of aluminum alloys at elevated temperatures frequently cause process instability. Specifically, the evaporation of low-boiling-point alloying elements (e.g., Mg and Li) induces vigorous solid–liquid–gas phase transitions, increasing the propensity for welding defects such as porosity and spatter. Among these, spatter poses unique challenges due to its direct impact on weld morphology, surface quality, and downstream reliability. Despite decades of advancement in laser welding technology, significant challenges remain in the welding of aluminum alloys containing volatile elements.

Conventional single-mode Gaussian laser beams, with their concentrated energy distribution, frequently induce unstable keyhole dynamics, resulting in defects including spatter, incomplete fusion, and porosity. Of particular concern is the fact that excessive metal spatter not only compromises mechanical performance but also poses safety hazards. Moreover, compared with steel and other structural alloys, aluminum alloys are more susceptible to spatter due to their lower viscosity, higher thermal conductivity, and rapid melt pool response under laser irradiation. These physical characteristics amplify interfacial instabilities, making it more difficult to suppress spatter through empirical parameter adjustment alone. Therefore, a deeper physical understanding of spatter initiation and evolution is essential.

To mitigate these limitations, dual-beam hybrid laser systems have emerged as a viable solution [3]. Notably, the hybrid fiber–semiconductor laser system leverages unique energy distribution characteristics to address aluminum alloy welding challenges. Compared to single laser sources, this system—configured with coaxial or dual-beam fiber–diode arrangements—modifies laser energy distribution, attenuates molten pool temperature gradients, and enhances process stability, thereby improving weld quality.

Beginning in 2000, researchers initiated investigations into dual-beam hybrid laser welding for aluminum alloys to optimize process efficiency. Widespread industrial adoption commenced in 2011, following successful applications in copper alloy welding before being extended to aluminum alloys [4,5,6]. Glumann et al. [7] pioneered the implementation of dual 5 kW CO₂ lasers for hybrid welding, demonstrating stabilized process conditions and effective porosity suppression. Dual-beam systems enhance laser absorption and penetration depth, minimize porosity and spatter formation, and produce smoother weld surfaces with negligible porosity in full-penetration mode [8,9]. Furthermore, the strategic combination of wavelengths and spot diameters significantly affects workpiece absorption efficiency and weld morphology [10,11].

With advancements in fiber laser technology, Ishige et al. [12] conducted comparative studies of molten pool behaviors between semiconductor and fiber lasers, revealing that semiconductor lasers generate deeper, more stable molten pools. Maina et al. [13] employed a hybrid fiber–semiconductor laser system (1064 nm + 532 nm) for copper welding, with simulation results confirming increased penetration depth and processing efficiency. Zhu and Zhao [14,15] established that hybrid laser welding of aluminum alloys optimizes molten pool flow through coordinated heat conduction and convection, thereby enhancing process stability and weld quality. Zhao et al. [16] reported that coaxial hybrid wavelength laser beams (HW-HLB) exhibit superior power density compared to single fiber lasers (FLB), leading to plasma plume intensification and deeper keyhole formation.

Despite these advancements, current studies on hybrid laser welding remain largely concentrated on process optimization, with a notable lack of systematic research into the mechanisms of spatter suppression. In particular, limited attention has been paid to the interplay between thermal-fluid dynamics and spatter behavior in aluminum alloys, despite the fact that melt pool instabilities and interfacial force imbalances are key contributors to spatter formation. This study integrates high-fidelity multiphysics simulations with high-speed imaging to elucidate the influence of hybrid fiber–semiconductor laser systems on molten pool dynamics and spatter formation. Through parameter optimization, we aim to establish theoretical frameworks and technical guidelines for improving aluminum alloy welding quality, ultimately facilitating the intelligent development of laser welding technology.

2. Experiments and Methods

2.1. Hybrid Fiber–Semiconductor Laser Welding Experiments

The experimental investigation utilized 3003 aluminum–manganese alloy as the substrate material, with its detailed chemical composition presented in

Table 1. The chemical composition of the 3003 Al-Mn alloy (wt %).

Element	Mn	Cu	Fe	Si	Zn	Al
3003	1.3	0.13	0.7	0.6	0.1	Bal.

.

This non-heat-treatable alloy is widely used in battery cases, vehicle bodies, and structural panels due to its excellent corrosion resistance, moderate strength, and good weldability. In particular, its relatively low melting point and high surface energy make it highly suitable for thermal processing using hybrid laser sources, as they promote stable melt pool formation under dual-beam irradiation.

The laser systems employed in this study consisted of two distinct laser sources manufactured by Han’s Laser Technology Co., Ltd., Shenzhen, China: a single-mode fiber laser (Model: HL-WS-1000-G2) and a semiconductor laser (Model: HWD-2000), as shown in Figure 1a. These laser systems were integrated with a high-precision motion control platform, also produced by Han’s Laser, which achieved exceptional positioning accuracy with a motion precision of ≤0.1 mm. This experimental setup provided precise control and reproducibility of the welding process parameters.

Figure 1a shows the experimental setup for hybrid fiber–semiconductor laser welding. The integration of the two laser systems is achieved through a specialized pivoting dual-beam hybrid laser welding head (Model: HW-C-PT-1125001, Han’s Laser Technology Co., Ltd., Shenzhen, China). In this configuration, both laser beams are collimated before being combined into a single coaxial output beam at the welding head. The optical system parameters are specified as follows: the semiconductor laser collimator has a focal length of 100 mm, while the fiber laser collimator has a 150 mm focal length, with both systems utilizing a common focusing lens having a 250 mm focal length. This optical arrangement produces a final output spot diameter of 0.3 mm for the fiber laser and 0.8 mm for the semiconductor laser. Figure 1b shows the wavelength-dependent absorption characteristics of 3003 aluminum alloy, while Figure 1c displays the longitudinal cross-sectional schematic of the welding process. The welding operation is performed under a high-purity argon (99.99%) shielding atmosphere, with a flow rate maintained at 20 L·min⁻¹ to ensure optimal protection of the molten pool.

The welding parameters used in this study, including beam power, focal positioning, and scanning speed, were selected based on preliminary experiments designed to balance keyhole stability and spatter suppression under hybrid laser excitation. These parameter settings were consistently maintained throughout all trials to ensure experimental repeatability and comparability.

2.2. Energy Characteristics of the Hybrid Laser Beam

The optical architecture within the hybrid fiber–semiconductor laser welding head consists of two distinct subsystems, as shown in Figure 2a: the fiber laser beam transmission module and the semiconductor laser beam reflection module. In the semiconductor laser pathway, a 400 μm diameter beam is first collimated by lens A, then sequentially reflected by a mirror and beam splitter before reaching the final focusing lens. In contrast, the 20 μm single-mode fiber laser beam is collimated by lens B and transmitted through the beam splitter to the shared focusing lens. This configuration ensures both laser beams are coaxially aligned through the same optical element, achieving sub-micron focal alignment on the workpiece surface.

Figure 2b shows the measured energy distribution profiles for three operational modes: the single fiber laser beam, the semiconductor laser beam, and the hybrid fiber–semiconductor laser beam. The irradiation characteristics are quantified in Figure 2c, demonstrating the spatially modulated laser energy distribution on the workpiece surface. The central high-intensity zone represents the combined fiber–semiconductor laser irradiation, while the concentric annular region corresponds to the peripheral 915 nm semiconductor laser irradiation. This engineered dual-zone energy distribution enables precise thermal management through controlled heat flux gradients and molten pool dynamics.

Based on the principle of coaxial laser beam superposition, a superposition model for multiple laser beams was established. Figure 3a shows the simulation results for the 1070 nm single-mode fiber laser beam, employing a Gaussian rotational heat source model. The key beam parameters—including divergence angle, Rayleigh range, and beam radius—were calculated using Equations (1)–(3):

$$\mathsf{\theta } ={\displaystyle \frac{\mathsf{\lambda }{\mathrm{M}}^2}{\mathsf{\pi }{\mathsf{\omega }}^2}}$$

(1)

$${\mathrm{Z}}_{\mathrm{R}}={\displaystyle \frac{\mathsf{\pi }{{\mathsf{\omega }}_0}^2}{\mathsf{\lambda }{\mathrm{M}}^2}}$$

(2)

$$\mathsf{\omega }\left(\mathrm{z}\right)={\mathsf{\varpi }}_0\left[1+{\left({\displaystyle \frac{\mathrm{z}}{{\mathrm{Z}}_{\mathrm{R}}}}\right)}^2\right]$$

(3)

In the equations, θ represents the divergence angle of the fiber laser beam, λ denotes the wavelength, M² is the beam quality factor, ω₀ indicates the beam waist radius, Z_R corresponds to the Rayleigh range, ω(z) represents the beam radius at propagation distance z, and z is the axial distance from the beam waist.

Figure 3b shows the simulation results for the 915 nm semiconductor laser beam, employing a top-hat modified Gaussian heat source model. Building upon the standard three-dimensional Gaussian heat source formulation, we introduced a super-Gaussian coefficient N and divergence angle parameter θ. The super-Gaussian coefficient is calculated using Equation (4):

$$f\left(\mathrm{x}\right)=\mathrm{e}\mathrm{x}\mathrm{p}\left(-{\displaystyle \frac{{\mathrm{x}}^2}{{{\mathsf{\omega }}_0}^2}}\right){\sum }_{\mathrm{k}=0}^{\mathrm{N}}{\displaystyle \frac{1}{\mathrm{k}!}}{\left({\displaystyle \frac{{\mathrm{x}}^2}{{{\mathsf{\omega }}_0}^2}}\right)}^{\mathrm{k}},\mathrm{N}=\mathrm{0,1},2\dots$$

(4)

In Equation (4), N represents the super-Gaussian order coefficient, where k! denotes the factorial of the mode order k, used to control the beam profile sharpness in super-Gaussian formulations.

The simulation models for the fiber laser beam and semiconductor laser beam are coupled through non-sequential ray tracing. The focal length of the semiconductor laser beam was optically matched to the fiber laser beam’s focal plane. Figure 3 presents the simulation results for the hybrid fiber–semiconductor laser beam system. Key parameter comparisons between the fiber laser and semiconductor laser are summarized in

Table 2. Fiber laser and semiconductor laser excitation parameters comparison.

Parameter	Fiber Laser	Semiconductor Laser
Maximum power W	1000	2000
Wavelength λ/nm	1070	915
Fiber core diameter df/µm	20	400
Focal distance f/mm	200	200

.

2.3. Multiphysics Modeling of Hybrid Laser Welding

The hybrid fiber–semiconductor laser welding process involves complex multiphase interactions and material state transformations. To investigate the molten pool dynamics, we established a comprehensive three-dimensional thermal-fluid coupling model that explicitly accounts for the synergistic effects between the dual laser systems.

This numerical model incorporates three critical physical phenomena: gas–liquid–solid phase transitions, coupled heat and mass transfer mechanisms, and laser-matter interaction physics. The computational domain, illustrated in Figure 4a, encompasses both metallic and gaseous phases. Considering the symmetrical energy distribution characteristic of the hybrid laser system along the welding direction, we implemented half-symmetry boundary conditions to optimize computational efficiency.

The meshing strategy employs adaptive refinement to balance accuracy and computational cost. Near the laser interaction zone, we applied high-resolution hexahedral elements (0.02 mm × 0.02 mm × 0.02 mm), while peripheral regions utilize coarser grids (0.02 mm × 0.1 mm × 0.02 mm). Figure 4b presents this multi-scale mesh configuration, with Figure 4c providing a detailed view of the transition region between refinement levels. This approach maintains solution accuracy while significantly reducing computational requirements compared to uniform meshing.

In modeling fluid motion, the governing equations of mass, momentum, and energy conservation must be simultaneously satisfied [12]. The mass conservation (continuity) equation is expressed as

$${\displaystyle \frac{\mathsf{\partial }\mathsf{\rho }}{\mathsf{\partial }\mathrm{t}}}+\nabla \cdot \left(\mathsf{\rho }\overrightarrow{\mathrm{u}}\right)=0$$

(5)

In the continuity equation, ρ represents the fluid density, and $\overrightarrow{\mathrm{u}}$ denotes the velocity vector field.

The momentum conservation equation is expressed as

$${\displaystyle \frac{\mathsf{\partial }\left(\mathsf{\rho }\overrightarrow{\mathrm{u}}\right)}{\mathsf{\partial }\mathrm{t}}}+\nabla \cdot \left(\mathsf{\rho }\overrightarrow{\mathrm{u}\otimes }\overrightarrow{\mathrm{u}}\right)=\nabla \cdot \left(\mathsf{\mu }\nabla \overrightarrow{\mathrm{u}}\right)-\nabla \mathrm{P}+\mathsf{\rho }\overrightarrow{\mathrm{g}}+\overrightarrow{\mathrm{F}}$$

(6)

In the equation, μ is the dynamic viscosity coefficient, $\nabla \overrightarrow{\mathrm{u}}$ is the velocity gradient, ∇P represents the influence of pressure variations in the fluid on its motion, $\overrightarrow{\mathrm{g}}$ is the gravitational acceleration, and $\overrightarrow{\mathrm{F}}$ is the external force term, representing external forces acting on the fluid (such as recoil pressure, thermal buoyancy, electromagnetic force, etc.).

The energy conservation equation is

$${\displaystyle \frac{\mathsf{\partial }\left(\mathsf{\rho }\mathrm{H}\right)}{\mathsf{\partial }\mathrm{t}}}+\nabla \cdot \left(\mathsf{\rho }\overrightarrow{\mathrm{u}}\mathrm{H}\right)=\nabla \cdot \left({\displaystyle \frac{\mathrm{K}}{{\mathrm{C}}_{\mathrm{P}}}}\nabla \mathrm{H}\right)+\mathrm{S}$$

(7)

In the equation, H represents the enthalpy, K is the thermal conductivity, C_p is the specific heat capacity at constant pressure, and S is the energy source term, which corresponds to the laser heat source.

The VOF (volume of fluid) method is selected as the free surface tracking technique for welding pores. By solving the independent momentum equations and determining the volume fraction of each fluid within the control volume, the issue of tracking the gas–liquid interface during the transient melting process is effectively addressed.

The VOF equation is

$${\displaystyle \frac{\mathsf{\partial }\mathsf{\alpha }}{\mathsf{\partial }\mathrm{t}}}+\nabla \cdot \left(\mathsf{\alpha }\overrightarrow{\mathrm{u}}\right)=0$$

(8)

In the equation, α represents the volume fraction, which indicates the volume ratio of a phase (such as liquid or gas) in a multiphase flow, where α ∈ [0, 1]. In the computational model, the first phase is set as the 3003 manganese–aluminum alloy, and the second phase is set as the gas phase. When the liquid phase volume fraction of a cell is between 0 < α < 1, it indicates that the cell is in the gas–liquid mixing zone. Typically, cells with α > 0.5 are considered to be in the liquid region, and cells with 0 ≤ α ≤ 0.5 are considered to be in the gas region. Therefore, in the entire computational domain, the fluid fraction of each grid cell will approach either 0 or 1, and the interface between the gas and liquid phases forms the gas–liquid free surface. Figure 5 illustrates this relationship.

At the free surface of the keyhole, the forces acting on it include the following: surface tension P_σ, recoil pressure P_r, Marangoni shear stress, static pressure, and other external forces. The keyhole wall maintains dynamic equilibrium under the combined effects of these forces [13]. The molten pool is governed by three dominant forces: thermal capillary convection, buoyancy force, and gravitational effects. The pressure balance in the normal direction of the free surface must satisfy the Young–Laplace condition:

$$\mathrm{P}={\mathrm{P}}_{\mathsf{\sigma }}+{\mathrm{P}}_{\mathrm{r}}$$

(9)

In the equation, P is the normal pressure at the free surface, P_σ is the surface tension, and P_r is the recoil pressure.

The surface tension is calculated using the following equation:

$${\mathrm{P}}_{\mathsf{\sigma }}=\mathsf{\gamma }\cdot \left({\displaystyle \frac{1}{{\mathrm{R}}_1}}+{\displaystyle \frac{1}{{\mathrm{R}}_2}}\right)$$

(10)

In the equation, γ is the surface tension coefficient, and R₁ and R₂ are the principal radii of curvature of the free surface. The recoil pressure P_r is calculated using the following formula:

$${\mathrm{P}}_{\mathrm{r}}={\displaystyle \frac{\mathsf{\rho }{\mathrm{v}}^2}{2}}$$

(11)

In the equation, ρ is the fluid density, and v is the fluid velocity.

The surface tension coefficient of the 3003 aluminum–manganese alloy studied in this paper decreases as the temperature increases. The surface tension coefficient can be expressed as

$$\mathsf{\gamma }\left(\mathrm{T}\right)={\mathsf{\gamma }}_0\left[1-\mathsf{\beta }\left(\mathrm{T}-{\mathrm{T}}_0\right)\right]$$

(12)

In the equation, γ₀ is the surface tension coefficient at room temperature, β is the rate at which the surface tension coefficient changes with temperature, T is the current temperature, and T₀ is the room temperature.

At the keyhole wall, the vapor recoil pressure serves as the exclusive driving force for keyhole expansion and plays a pivotal role in pore formation during welding, substantially governing its morphological characteristics. Based on established research, the vapor recoil pressure acting on the keyhole during laser welding can be expressed as

$${\mathrm{P}}_{\mathrm{r}}\left(\mathrm{T}\right)\cong 0.54{\mathrm{P}}_{\mathrm{s}\mathrm{a}\mathrm{t}}\left(\mathrm{T}\right)=0.54{\mathrm{P}}_0\mathrm{e}\mathrm{x}\mathrm{p}\left[{\displaystyle \frac{{\mathrm{L}}_{\mathrm{v}}\left(\mathrm{T}-{\mathrm{T}}_{\mathrm{b}}\right)}{\overline{\mathrm{R}}\mathrm{T}{\mathrm{T}}_{\mathrm{b}}}}\right]$$

(13)

In the equation, P_sat(T) is the saturation vapor pressure, P₀ is the atmospheric pressure, T_b is the boiling temperature, and $\overline{\mathrm{R}}$ is the universal gas constant.

In the melt pool, the metal density varies with temperature. The buoyancy term is expressed using the Boussinesq approximation formula as

$${\mathrm{F}}_{\mathrm{b}}=\mathsf{\rho }\mathrm{g}{\mathsf{\beta }}_0\left(\mathrm{T}-{\mathrm{T}}_{\mathrm{l}}\right)$$

(14)

In the equation, g is the gravitational acceleration, β₀ is the thermal expansion coefficient, and T₁ is the melting point of 3003 aluminum alloy.

Moreover, the choice of the heat source model will directly affect the consistency between the simulation results and the experimental results. According to previous work, due to the differences in energy density and power between fiber lasers and semiconductor lasers, there are significant differences in heat input, heat transfer modes, and energy distribution, which in turn affect the melt pool characteristics of the weld joint. The Gaussian distribution characteristics of the single-mode fiber laser lead to the formation of a narrow and deep keyhole in deep penetration welding modes. The temperature gradient on the surface of the melt pool is large, and under the influence of the Marangoni effect, natural convection of liquid molecules occurs, resulting in fluctuating flow and a tendency for keyhole collapse, thus forming a deep “V”-shaped weld seam. In contrast, the large-spot semiconductor laser primarily uses conduction welding, with energy distributed in a flat-top pattern. The liquid flow is more lateral, forming a bowl-shaped melt pool. In hybrid laser welding, the semiconductor laser promotes lateral flow in the melt pool, reduces the cooling rate, increases the overall temperature, and enhances the stability of the keyhole, ultimately forming an ideal Y-shaped weld seam and reducing the occurrence of spatter and porosity. Considering the energy characteristics of the two heat sources and the forming characteristics of the weld joint, a hybrid heat source model combining a Gaussian rotating body heat source and a flat-top Gaussian body heat source is established.

The described Gaussian rotating body heat source includes both surface and volume heat sources. The distribution function of the heat flux density of this heat source is described as follows:

$${\mathrm{Q}}_{\mathrm{s}}\left(\mathrm{x},\mathrm{y}\right)={\displaystyle \frac{{\mathsf{\eta }}_1{\mathrm{q}}_{\mathrm{s}}}{\mathsf{\pi }{{\mathrm{r}}_{\mathrm{s}}}^2}}\mathrm{e}\mathrm{x}\mathrm{p}\left[-{\displaystyle \frac{{\mathsf{\alpha }}_{\mathrm{r}}\left({\mathrm{x}}^2+{\mathrm{y}}^2\right)}{{{\mathrm{r}}_{\mathrm{s}}}^2}}\right]$$

(15)

$${\mathrm{Q}}_{\mathrm{v}}\left(\mathrm{x},\mathrm{y},\mathrm{z}\right)={\displaystyle \frac{{9\mathsf{\eta }}_2{\mathrm{q}}_{\mathrm{v}}}{\mathsf{\pi }{{{\mathrm{h}}_{\mathrm{l}\mathrm{a}\mathrm{s}\mathrm{e}\mathrm{r}}\mathrm{r}}_{\mathrm{s}}}^2\left(1-{\mathrm{e}}^{-3}\right)}}\mathrm{e}\mathrm{x}\mathrm{p}\left[-{\displaystyle \frac{9\left({\mathrm{x}}^2+{\mathrm{y}}^2\right)}{{{\mathrm{r}}_{\mathrm{s}}}^2\ln \left(\frac{{\mathrm{h}}_{\mathrm{l}\mathrm{a}\mathrm{s}\mathrm{e}\mathrm{r}}}{\mathrm{z}}\right)}}\right]$$

(16)

In the equation, Q_s and Q_v represent the heat flux density of the Gaussian surface heat source and the volume heat source, respectively. η₁ and η₂ represent the efficiencies of the surface and volume heat sources, respectively, and q_s and q_v represent the laser power of the surface and volume heat sources, respectively. r_s is the radius of the heat source influence area, α_r is the parameter that controls the rate of heat flux decay, and h_laser is the depth to which the laser penetrates into the material.

The distribution function of the heat flux density for a conical Gaussian heat source is described as follows:

$$\mathrm{Q}\left(\mathrm{x},\mathrm{y},\mathrm{z}\right)={\displaystyle \frac{\mathsf{\eta }\mathrm{q}}{\mathsf{\pi }{\left[{\mathrm{r}}_0\left(\mathrm{y}\right)\right]}^2}}\mathrm{e}\mathrm{x}\mathrm{p}\left[-{\displaystyle \frac{{\mathrm{x}}^2+{\mathrm{z}}^2}{{\mathrm{r}}_0\left(\mathrm{y}\right)}}\right]$$

(17)

$${\mathrm{r}}_0\left(\mathrm{y}\right)={\mathrm{r}}_{\mathrm{e}}+{\displaystyle \frac{{\mathrm{r}}_{\mathrm{l}}+{\mathrm{r}}_{\mathrm{e}}}{{\mathrm{y}}_{\mathrm{l}}+{\mathrm{y}}_{\mathrm{e}}}}\left(\mathrm{y}-{\mathrm{y}}_{\mathrm{e}}\right)$$

(18)

In the equation, η is the efficiency coefficient of the conical Gaussian heat source, q is the laser heat source power, and r₀(y) is the radius of the heat source influence area. The thermophysical properties of the 3003 aluminum–manganese alloy used in this study are listed in

Table 3. Comparison between experimental and numerical simulation of molten pool size.

Physical Property	Symbol	Value
Density (solid)	${\mathsf{\rho }}_{\mathrm{s}}$	2.73 g/cm3
Density (liquid)	${\mathsf{\rho }}_{\mathrm{l}}$	2.37 g/cm3
Latent heat of fusion	$∆{\mathrm{H}}_{\mathrm{f}}$	3.97 × 105 J/Kg
Latent heat of vaporization	$∆{\mathrm{H}}_{\mathrm{v}\mathrm{a}\mathrm{p}}$	1.04 × 107 J/Kg
Surface tension at room temperature	${\mathsf{\gamma }}_0$	0.87 N/m
Surface tension gradient	$\mathsf{\beta }$	−1.6 × 10−3 N/m·K
Solidus temperature	${\mathrm{T}}_{\mathrm{s}}$	916 K
Liquidus temperature	${\mathrm{T}}_{\mathrm{l}}$	927 K
Boiling point	${\mathrm{T}}_{\mathrm{b}}$	2743 K
Ambient temperature	${\mathrm{T}}_0$	300 K

.

The accuracy of this multiphysics model was confirmed through experimental validation. As shown in the Section 3, the simulated weld cross-sections demonstrated excellent agreement with experimental measurements, with all dimensional deviations controlled within 5%. Detailed validation data including weld width and penetration depth comparisons are provided in Figure 6 and

Table 4. Comparison between experimental and numerical simulation of molten pool size.

		Weld Width	Weld Depth
Fiber laser power 0.8 KW	Experimental data (μm)	239	1910
	Simulation data (μm)	247	1987
	Errors (%)	+3.35	+4.03
Semiconductor laser power 1.6 KW	Experimental data (μm)	1280	297
	Simulation data (μm)	1340	295
	Errors (%)	+4.69	−0.67
Fiber laser power 0.6 KW, Semiconductor laser power 0.8 KW	Experimental data (μm)	504	1088
	Simulation data (μm)	528	1074
	Errors (%)	+4.76	−1.29

.

3. Results and Discussion

To systematically investigate the spatter suppression effect of the hybrid fiber–semiconductor laser system, the heat source models for single-mode fiber laser, semiconductor laser, and hybrid fiber–semiconductor laser were validated through weld cross-section comparisons. Three representative parameter sets were selected for validation: Figure 6a presents the comparison between experimental and simulated cross-sections for 800 W fiber laser welding at 70 mm/s with 0 mm defocus; Figure 6b shows the validation results for a 1600 W semiconductor laser; Figure 6c illustrates the hybrid laser welding case combining 600 W fiber laser and 800 W semiconductor laser. The simulation domain was selected at the position of maximum weld width. The results demonstrate excellent agreement between simulation and experiment, with weld penetration depth deviations within ±5% (Table 4), confirming the accuracy of the proposed heat source model. This rigorous validation establishes a reliable foundation for subsequent investigation of spatter formation mechanisms and the development of effective suppression strategies in hybrid laser welding of aluminum alloys.

3.1. Analysis of Spatter Formation Mechanism

Under single-fiber laser welding conditions, spatter-induced pit defects frequently compromise weld bead surface quality, ultimately resulting in product rejection. Consequently, investigating spatter formation mechanisms becomes essential for developing effective suppression strategies. Figure 7 captures the spatter formation sequence through high-speed imaging. The process initiates when laser energy melts and vaporizes the metal surface, creating downward recoil pressure that establishes the keyhole (Figure 7a). Subsequently, ascending liquid metal droplets interact with cooler, turbulent molten pool fluid, constricting the keyhole aperture (Figure 7b). This interaction simultaneously reduces thermal energy at the keyhole apex. The inherent energy density gradient between the keyhole center and periphery establishes a temperature gradient that drives upward fluid transport along the keyhole walls through Marangoni convection. Protective gas shear forces deform the droplets while intensifying temperature gradients [18] impart higher ejection velocities (Figure 7c). At the keyhole exit, competing forces including inertia, surface tension, gravity, and recoil pressure collectively determine droplet trajectory (Figure 7d). The irregular molten mass eventually fragments under multidirectional stress (Figure 7e), with portions reintegrating into the weld pool while others solidify as discrete spatter particles (Figure 7f).

From the high-speed photography data in Figure 7, spatter formation is observed to initiate predominantly at the keyhole periphery. Thermocapillary instability may also contribute to spatter initiation by inducing wave-like deformation along the melt pool boundary under steep temperature gradients. These instabilities arise from non-uniform surface tension distribution and interact with vapor recoil and shear forces to disturb the molten interface. Related studies on aluminum alloy welding have reported similar unstable behaviors at the keyhole wall and melt pool surface under laser irradiation [19,20].

Figure 8 illustrates the vertical force balance acting on molten metal droplets near the keyhole, where the driving forces (recoil pressure and vapor shear stress) compete against restraining forces (gravity and surface tension). When the resultant upward forces exceed the downward restraints, droplet separation occurs: the upper portion ejects as spatter while surface tension draws the lower portion back into the weld pool.

Figure 9 presents the formation process of spatter defects through numerical simulation. As shown in the figure, at the initial stage (t = t₀), laser irradiation rapidly establishes a high-temperature zone on the material surface, inducing melting and vaporization that forms a stable keyhole structure in the weld pool (Figure 9a). With continued laser interaction, the keyhole region temperature rises further. Metal vaporization within the keyhole generates downward recoil pressure, destabilizing the molten pool and driving upward fluid flow along the keyhole walls (Figure 9b,c).

At t = t₀ + 0.68 ms, sustained recoil pressure and vapor shear forces promote liquid metal accumulation at the keyhole apex, where upward motion is counteracted by gravity and surface tension (Figure 9c). When the driving forces overcome these restraints, partial liquid ejection occurs, resulting in droplet fragmentation and spatter formation (Figure 9d–f). These simulation results exhibit strong correlation with high-speed photographic observations, explicitly capturing the fluid dynamics under competing influences of recoil pressure, gravitational force, and surface tension.

Comparative analysis with experimental data reveals the complex force equilibrium governing molten metal behavior at the keyhole boundary. The numerical model confirms that spatter generation stems from time-dependent interactions between four principal forces: recoil pressure, vapor shear stress, surface tension, and gravity. Particularly, recoil pressure and Marangoni convection dominate the droplet ascent and rupture process, enabling liquid transfer from the keyhole interior to the workpiece surface where final spatter detachment occurs. The potential contribution of thermocapillary instability to interface fluctuation further supports this mechanism [20]. The consistency between simulation and experiment validates the model’s capability to reproduce the essential physics of spatter defect formation.

3.2. Study on the Spatter Suppression Mechanism

Previous research has demonstrated that hybrid fiber–semiconductor laser welding exhibits significantly superior spatter suppression compared to conventional single-mode fiber laser welding. Spatter formation represents a prevalent defect in welding processes, particularly in high-energy-density laser welding applications. The occurrence of spatter adversely impacts both weld quality and subsequent manufacturing operations. Consequently, the implementation of hybrid laser welding technology offers an effective solution for enhancing process stability and final product quality.

3.2.1. Surface Tension Effect

Energy distribution uniformity and effective regulation of molten pool surface tension. Studies have shown that hybrid laser welding optimizes the laser beam’s energy distribution, significantly reducing the temperature difference between the laser interaction zone and the molten pool surface. This thermal control substantially decreases the intensity of the Marangoni effect—the surface tension variation caused by temperature gradients in liquid metals that typically drives localized convective flows and spatter formation [21].

In single-mode fiber laser welding, the concentrated energy density creates substantial temperature differences between the center and edges of the molten pool, leading to strong surface tension variations that induce vigorous fluid flow and spatter generation. The hybrid laser system, combining semiconductor and fiber laser sources, achieves more uniform energy distribution during welding. This optimized thermal profile reduces surface temperature gradients, thereby suppressing Marangoni-driven surface flows.

The temperature gradient comparison between (a) fiber laser and (b) hybrid laser welding in Figure 10 clearly demonstrates this mechanism. The fiber laser welding shows steep thermal gradients with a distinct high-temperature core and cooler periphery, while the hybrid laser welding exhibits more uniform temperature distribution with reduced thermal gradients. This direct visualization confirms that the temperature homogenization in hybrid laser welding weakens Marangoni-induced surface flows, leading to effective spatter reduction.

Experimental results demonstrate that hybrid laser welding significantly decreases molten pool surface flow velocities and reduces spatter generation compared to single-laser operations. This improvement stems from the hybrid system’s ability to prevent localized high-temperature zones and subsequent surface tension imbalances. These findings provide fundamental theoretical support for enhancing welding quality while improving process stability in industrial laser welding applications.

As demonstrated in Figure 10, the hybrid laser system’s ability to reduce temperature gradients serves as the first key factor for spatter suppression. This thermal control mechanism weakens Marangoni-driven convection, thereby decreasing the kinetic energy available for droplet ejection.

3.2.2. Recoil Pressure Effect

Beyond thermal gradient control, the hybrid laser system achieves spatter reduction through a second key factor: molten pool flow stabilization. Figure 11 directly compares the flow patterns under single laser and hybrid laser conditions, demonstrating how energy distribution uniformity prevents droplet detachment.

In addition to surface tension optimization, recoil pressure plays a critical role in hybrid laser welding. This pressure originates from laser-material interactions, particularly the rapid vaporization induced by concentrated laser energy at the material surface. The resulting gas pressure increase drives molten metal flow within the weld pool.

Compared to single fiber laser welding, the hybrid system provides superior energy density distribution and broader power modulation capability. These advantages yield more uniform temperature and pressure profiles throughout the molten pool. The expanded pool volume and increased surface area help dissipate excessive recoil pressure, stabilizing metal flow while minimizing bubble formation and spatter generation.

The balanced energy distribution in hybrid laser welding enables gradual establishment of thermal and pressure equilibrium within the molten pool, preventing localized overheating and unstable flow patterns. This equilibrium enhances pool stability and reduces flow-induced spatter. Experimental evidence confirms that larger molten pool volumes promote smoother metal flow and decreased spatter formation.

The comparative high-speed imaging results in Figure 11 clearly demonstrate the process characteristics: (a) fiber laser welding exhibits pool instability with constricted keyhole dimensions, turbulent flow, and frequent spatter ejection, while (b) hybrid laser welding shows stable pool geometry with uniform keyhole expansion, laminar flow, and minimal spatter generation. These visual comparisons confirm that the hybrid laser’s energy distribution achieves superior process stability through reduced thermal gradients and moderated surface tension effects.

Further refinements in power distribution and travel speed control can optimize gas pressure management, providing additional spatter reduction. These findings establish a theoretical framework for spatter control in hybrid laser welding and guide practical process optimization.

Spatter reduction results from two complementary mechanisms: temperature gradient minimization in Figure 10 suppresses Marangoni effects, while molten pool flow stabilization in Figure 11 inhibits droplet ejection. The hybrid system’s energy optimization concurrently controls both aspects.

4. Conclusions

This study systematically investigates the spatter formation mechanisms and suppression strategies in hybrid fiber–semiconductor laser welding of aluminum alloys. Through the integration of high-speed imaging technology and a comprehensive three-dimensional thermal-fluid coupling numerical model, we have successfully captured and analyzed the spatter formation processes. The following conclusions were drawn from both numerical simulations and experimental observations:

• Spatter Formation Mechanism: High-speed imaging reveals that laser-induced metal vaporization forms a keyhole, where recoil pressure causes molten pool fluctuations. Droplet collisions reduce the keyhole volume, while temperature gradients trigger Marangoni convection, elevating droplets that subsequently rupture into spatter particles. Simulations confirm experimental observations of liquid metal dynamics under competing forces (recoil pressure, gravity, and surface tension), demonstrating that spatter generation results from complex multi-force interactions. These findings provide fundamental insights for welding process optimization.
• Surface Tension-Mediated Spatter Suppression: Hybrid laser welding effectively reduces spatter through optimized energy distribution and molten pool surface tension regulation. The process minimizes thermal gradients, thereby weakening Marangoni-driven flows. Experimental results demonstrate reduced molten pool flow velocities and significant spatter suppression, ultimately enhancing welding quality and process stability. This approach combines semiconductor and fiber lasers to achieve superior energy distribution uniformity.
• Recoil Pressure-Mediated Spatter Suppression: The hybrid system suppresses spatter by controlling recoil pressure generated through laser-induced vaporization, promoting stable molten metal flow. Compared to single fiber lasers, the hybrid configuration provides enhanced energy density distribution and broader power modulation capability, enabling more uniform temperature and pressure profiles. High-speed imaging confirms improved process stability, showing well-developed molten pools with smooth flow characteristics and minimal spatter generation. Further optimization of laser parameters can enhance spatter control capabilities.
• Future Research Directions: Subsequent studies will integrate optical coherence tomography (OCT) for real-time spatter monitoring and employ machine learning techniques to analyze multi-sensor datasets for quantitative spatter prediction and classification. In addition, the effects of thermocapillary instability under evaporative recoil pressure on melt pool boundary fluctuations and spatter formation will be systematically investigated. This will provide deeper insight into the coupled fluid-thermal dynamics influencing spatter generation.

This study primarily focused on numerical modeling and qualitative analysis of dual-laser welding. Future experimental work should include quantitative measurements, such as melt pool dimensions and spatter counts, to further validate the simulation results. Such data would help establish clearer correlations between process parameters and weld quality. This integrated approach will advance understanding of spatter suppression mechanisms and facilitate welding parameter optimization.