Laser Scanning Speed Research Articles

Laser powder bed fusion sintering mechanism of phenolic resin investigated by ReaxFF molecular dynamics simulations

ABSTRACT This study presents an innovative approach utilising molecular dynamics simulations to elucidate the polymerisation and neck formation mechanisms of phenolic resin during laser powder bed fusion (L-PBF). By developing system and dual-particle models and employing infrared thermal imaging, we established a heat source model for phenolic resin under varying parameters. Our molecular dynamics simulations, using the ReaxFF reactive force field, indicate that high-power lasers significantly enhance cross-linking, achieving a peak polymerisation degree of 78.9% at 30W and 35W power levels. Conversely, increased scanning speed slows product degradation, reducing the polymerisation degree. The interplay of laser power and scanning speed influences polymerisation, with total input energy and heating rate impacting polymerisation and molecular size. Additionally, we investigated neck formation, revealing that small vortices evolve into larger ones during sintering, promoting neck formation. Optical microscopy confirmed diverse neck shapes under different conditions. Quantitative data showed that at a scanning speed of 1000 mm/s, the polymerisation degree reached 93%, with only 2 molecules remaining post-simulation. At 30W power, the largest individual molecule observed was C849H703O117. This research offers crucial insights for selective laser sintering processes, emphasising the importance of high energy input and optimal heating rates for well-formed necks.

Optimizing the Size of a Moving Annular Hollow Laser Heat Source

The physical phenomenon of the annular hollow laser surface treatment process is complex, and the internal mechanism involves multiple disciplines and fields. In addition to the general parameters of laser beams, such as laser power and scanning speed, an annular hollow laser beam exhibits unique physical characteristics, including hollow ratio and hollow area. The selection of the inner and outer annular radii of the laser plays a critical role in the study of metal surface heat treatment. From the point of view of heat transfer, the entransy dissipation theory is introduced in the metal surface treatment process with an annular hollow heat source. Firstly, using the principle of the extreme value of the entransy dissipation rate, under a constant heat flux boundary condition, the entransy dissipation rate is obtained through the temperature field distribution in the calculation area by numerical simulation. Secondly, the selection of the inner and outer ring radii of the annular laser is explored, and the average temperature difference of the heating surface is minimized to reduce the thermal stresses of the material. This paper seeks new insights into the geometric parameters of the inner and outer radii of the annular heat source.

Advances in Laser Powder Bed Fusion of Tungsten, Tungsten Alloys, and Tungsten-Based Composites.

In high-tech areas such as nuclear fusion, aerospace, and high-performance tools, tungsten and its alloys are indispensable due to their high melting point, low thermal expansion, and excellent mechanical properties. The rise of Additive Manufacturing (AM) technologies, particularly Laser Powder Bed Fusion (L-PBF), has enabled the precise and rapid production of complex tungsten parts. However, cracking and densification remain major challenges in printing tungsten samples, and considerable efforts have been made to study how various processing conditions (such as laser power, scanning strategy, hatch spacing, scan speed, and substrate preheating) affect print quality. In this review, we comprehensively discuss various critical processing parameters and the impact of oxygen content on the control of the additive manufacturing process and the quality of the final parts. Additionally, we introduce additive manufacturing-compatible W materials (pure W, W alloys, and W-based composites), summarize the differences in their mechanical properties, densification, and microstructure, and further provide a clear outlook for developing additive manufactured W materials.

Temperature-assisted crystallization and morphology for CH3NH3PbI3 perovskite solar cells using laser-induced heat treatment

In this study, perovskite solar cells (PSCs) were fabricated using a two-step solution deposition method. A laser beam was applied at the interface between lead iodide (PbI2) and methylammonium iodide to change the reaction temperature and stimulate the growth of perovskite crystals. A notable enhancement was observed in the power conversion efficiency of PSCs. The laser scanning speed was investigated to control the reaction temperature and further control the crystallization and morphology of the perovskite film. Based on the optimized laser scanning speed, the best and average PCEs obtained were 14.33 % and 13.92 ± 0.52 %, respectively, which were higher than those achieved using the conventional technique (12.18 % and 11.37 ± 0.74 %, respectively) and the conventional heating methods (14.09 % and 13.28 ± 0.56 %, respectively). The optimal reaction temperature at the interface was predicted to be 80 °C under optimized conditions using the COMSOL software. This study will help in scaling this technique for large–area PSCs, optimizing the laser parameters for different perovskite compositions, and investigating the long-term stability of enhanced PSCs, boosting their commercial viability.

Microstructure, corrosion resistance and high temperature oxidation properties of AlCrFeNiCu high-entropy alloy coating by high-speed laser cladding

AlCrFeNiCu high-entropy alloy (HEA) coating was prepared on Zirconium (Zr) rod by high-speed laser cladding (HSLC) technique. The effect of different laser power and scanning speed on the forming quality of the coating was studied. When the specific energy density was in the range of 3.0–3.2 J/mm2, the AlCrFeNiCu HEA coating with good adhesion to the substrate, low dilution rate (small heat affected zone) and no cracks were obtained. The phase structure, microstructure, microhardness, corrosion resistance and high temperature oxidation resistance of the coating were tested and analyzed. The results show that the AlCrFeNiCu HEA coating is composed of 76.9 % BCC phase and 23.1 % FCC phase, in which the BCC phase is mainly composed of disordered BCC phase rich in Cr and Fe elements and ordered B2 phase rich in Al and Ni elements, and the FCC phase is rich in Cu elements. Due to the rapid cooling of HSLC, the coating structure gradually changes from columnar crystal to equiaxial crystal from bottom to top. The hardness of the coating is as high as 805 HV, which may be caused by the solution strengthening effect, lattice distortion effect and slow diffusion effect of HEA. In 3.5 % NaCl solution and 0.1 mol/L KOH solution, the self-corrosion current density of the coating is 4.209 × 10−8 A/cm2 and 4.331 × 10−8 A/cm2, respectively. The passivation film on the surface of the coating is composed of a variety of oxides, and the oxygen vacancy density of the passivation film in the two solutions is 2.552 × 1020 cm−3 and 5.794 × 1020 cm−3, with fewer pitting pits on the surface. The structural integrity of the coating can be maintained after 60 min oxidation at 1200 °C. The oxidation process follows the growth kinetics curve, and the surface oxides mainly comprise FeAl2O4, Al2O3 and Cr2O3. AlCrFeNiCu HEA coating improves the corrosion resistance and high temperature oxidation resistance of the Zr-4 alloy.

Integrating data-driven system to predict temperature and distortion in multi-layer direct metal deposition processes

This study proposed a framework to train an artificial neural network (ANN) by a data-driven system to predict the temperature and distortion in multi-layer direct metal deposition (DMD) of SS 304. By integrating thermomechanical variables, the research ensures the fidelity of finite element (FE) simulations, which are validated against existing data. Notably, the study achieves enhanced precision over prior work by varying the heat input sources and heat transfer equations. A novel aspect of this research is the use verified FE simulation to add data to data-driven system to train an efficient ANN for predicting temperature and distortion based on key parameters such as laser power and scanning speed. The iterative process involved multiple FE simulations with varying laser parameters to refine the ANN’s predictive capabilities. This methodology enabled the identification of relationships between manufacturing parameters, temperature, and distortion. The iterative training continued until the ANN’s predictions and subsequent FE simulation results converged within an acceptable margin. The findings confirm that the trained ANN can predict temperature and distortion both accurately and expediently, marking a significant advancement in the control of the DMD process.Graphical

Numerical simulation of single-pass selective laser melting of Mg-Y-Sm-Zn-Zr alloy

A three-dimensional finite volume method (FVM) thermal-fluid coupling model was developed to simulate the temperature field distribution and dimensions of the melt pool in selective laser melting (SLM) of Mg-Y-Sm-Zn-Zr alloy powder. This model aims to accurately determine the temperature field and surface morphology of the melt pool by considering the laser's impact on powder particles, free surface dynamics, convection, and multiphase heat transfer. The study explores how laser power and scanning speed influence temperature distribution, melt pool size, and flow dynamics. The findings show that increasing laser power or reducing scanning speed consistently affects the melt pool size, peak temperature, and cooling rate. During the evaporation of the alloy in the melt pool, fluid flow is primarily governed by recoil pressure and Marangoni convection. Notably, increasing the laser power from 40 W to 100 W and the scanning speed at 0.3 m/s enhances the depth of the melt pool indentation from 13.86 µm to 23.35 µm. Furthermore, the indentation depth correlates positively with the laser line energy density.

Predictive modelling of laser powder bed fusion of Fe-based nanocrystalline alloys based on experimental data using multiple linear regression analysis

This study harnessed bivariate correlational analysis, multiple linear regression analysis and tree-based regression analysis to examine the relationship between laser process parameters and the final material properties (bulk density, saturation magnetization (Ms), and coercivity (Hc)) of Fe-based nano-crystalline alloys fabricated via laser powder bed fusion (LPBF). A dataset comprising of 162 experimental data points served as the foundation for the investigation. Each data point encompassed five independent variables: laser power (P), laser scan speed (v), hatch spacing (h), layer thickness (t), and energy density (E), along with three dependent variables: bulk density, Ms, and Hc. The bivariate correlational analysis unveiled that bulk density exhibited a significant correlation with P, v, h, and E, whereas Ms and Hc displayed significant correlations exclusively with v and P, respectively. This divergence may stem from the strong influence of microstructure on magnetic properties, which can be impacted not only by the laser process parameters explored in this study but also by other factors such as oxygen levels within the build chamber. Furthermore, our statistical analysis revealed that bulk density increased with rising P, h, and E, while decreased with higher v. Regarding the magnetic properties, a high Ms was achievable through low v, while low Hc resulted from high P. It was concluded that P and v were considered as the primary laser process parameters, influencing h and t due to their control over the melt-pool size. The application of multiple linear regression analysis allowed the prediction of the bulk density by using both laser process parameters and energy density. This approach offered a valuable alternative to time-consuming and costly trial-and-error experiments, yielding a low error of less than 1 % between the mean predicted and experimental values. Although a slightly higher error of approximately 6 % was observed for Ms, a clear association was established between Ms and v, with lower v values corresponding to higher Ms values. Additionally, a further comparison was conducted between multiple linear regression and three tree-based regression models to explore the effectiveness of these approaches.

Microstructure evolution and grain growth characteristics of IN625 in laser surface melting: Effects of laser power and scanning speed

This study investigated the effects of laser power and scanning speed on the microstructure evolution and grain growth characteristics of IN625 in laser surface melting. Laser powers of 400 W, 600 W and 800 W at scanning speeds from 200 mm/min to 800 mm/min were employed to melt the surface of as-cast IN625 using a continuous Yb-doped fibre laser. The microstructure from the surface to the melt pool boundary was characterised by scanning electron microscopy (SEM) and electron backscattered diffraction (EBSD). It was shown that a cellular structure was developed at low energy densities (≤ 240 J/mm2), since low energy densities resulted in more rapid solidification. A coarse grain region was found near the surface after melting and a columnar grain growth region was formed close to the melt pool boundary at increasing laser power. Large angle grain boundaries were eliminated and medium angle grain boundaries exhibited an area fraction of 90 % after laser surface melting. This was due to the fact that the twinned grains were fully melted in the melt pool and no twins were formed after solidification. An analytical approach was proposed to the estimate the melt pool depth and good agreement between experimental and calculated melt pool depth was obtained at laser power of 400 W and 600 W.

Microstructure evolution in AlSi10Mg alloy fabricated by laser-based directed energy deposition

Investigation of the influence of processing parameters on the microstructure evolution of the melt pool is important for microstructure control in laser-based directed energy deposition (DED-LB) processes. Single-track experiments of DED-LB AlSi10Mg alloy were conducted in this work, processing with two laser powers (900 and 1800 W) at a fixed scanning speed (20 mm/s). The grain morphology was changed from columnar to equiaxed towards the center of each melt pool. Meanwhile, the morphology of the α-Al cells in the grains varied from elongated to equiaxed along the same direction due to the increase of cooling rate. A more equiaxed microstructure can be obtained in the melt pool produced at a larger laser power. The scales of the microstructures and the grain morphologies in the melt pools were in accord with the microstructure selection map and grain morphology selection map in temperature gradient (G)–growth velocity (V) space constructed by solidification models. Since G and V are directly related to laser power and scanning speed, the G–V maps can provide the guidance for processing optimization to tailor microstructures in DED-LB Al–Si alloys based on the temperature field simulations of melt pools.

Prediction of Geometric Characteristics of Laser Cladding Layer Based on Least Squares Support Vector Regression and Crested Porcupine Optimization.

The morphology size of laser cladding is a crucial parameter that significantly impacts the quality and performance of the cladding layer. This study proposes a predictive model for the cladding morphology size based on the Least Squares Support Vector Regression (LSSVR) and the Crowned Porcupine Optimization (CPO) algorithm. Specifically, the proposed model takes three key parameters as inputs: laser power, scanning speed, and powder feeding rate, with the width and height of the cladding layer as outputs. To further enhance the predictive accuracy of the LSSVR model, a CPO-based optimization strategy is applied to adjust the penalty factor and kernel parameters. Consequently, the CPO-LSSVR model is established and evaluated against the LSSVR model and the Genetic Algorithm-optimized Backpropagation Neural Network (GA-BP) model in terms of relative error metrics. The experimental results demonstrate that the CPO-LSSVR model can achieve a significantly improved relative error of no more than 2.5%, indicating a substantial enhancement in predictive accuracy compared to other methods and showcasing its superior predictive performance. The high accuracy of the CPO-LSSVR model can effectively guide the selection of laser cladding process parameters and thereby enhance the quality and efficiency of the cladding process.

Processing Windows of Ni625 Alloy Fabricated Using Direct Energy Deposition

Herein, a process window is developed for Ni625 alloy fabricated using a Nikon Lasermeister laser powder direct energy deposition (LP‐DED) unit. The process map illustrates the relationship between the laser power, scan speed, and effective energy density, established by examining the correlation between the microstructure and mechanical properties. All samples exhibit a bimodal microstructure comprising equiaxed and columnar dendrite grains, and the dendrite arm spacing decreases with increasing scan speed. The tensile behavior of each sample demonstrates minimal variation, and the values are comparable to those reported previously. The ultimate tensile and yield strengths range from 1008 ± 2 to 941 ± 9 and 682 ± 11 to 640 ± 7 MPa, respectively. This study highlights the remarkable manufacturability of Ni625 alloy for additive manufacturing across diverse parameter sets, demonstrating that a single ideal process set does not exist for each material and machine. Instead, multiple “recipes” may be employed to achieve similar outcomes.

Sintering parameter optimization by inverse analysis in direct metal deposition of Inconel 718

PurposeThe net power delivered to the surface of parts (i.e. the actual heat flux) is a key parameter in the laser melting process and its exact control has a great impact on the numerical solutions. In this paper, the impact of laser additive manufacturing parameters including laser power, scanning speed and powder injection rate on thermal efficiency, net power delivered to the part and power loss due to powder flow has been investigated.Design/methodology/approachThe response surface method was applied to measure the net laser power in laser deposited Inconel 718 using k-type thermocouples. The temperature history obtained by thermocouples was used to calculate the net power delivered by inverse analysis method. The applied model is Rosenthal's optimized model, in which all the thermal properties of the material are considered to vary with temperature.FindingsThe results indicated that the thermal efficiency, power delivered to the part and power loss can be optimized simultaneously at laser power of 400 W, scanning speed of 2 mm/s and powder injection rate of 200 mg/s. The microstructure analysis indicated that a high-quality sample without microstructural defects was formed under optimal condition of parameters. Moreover, the primary dendrite arm spacing for the optimal sample was higher than that obtained for other samples.Originality/valueThe novelty of this research summarized as follows: Prediction of the thermal efficiency and power loss during the laser metal deposition of Inconel 718 superalloy using the inverse analysis. Finding the optimal values of thermal efficiency, power delivered to the surface and power loss in the laser metal deposition of Inconel 718 superalloy. Investigating the effect of laser power, powder injection rate and scanning speed on the thermal efficiency and power loss of Inconel 718 superalloy during the laser metal deposition.

Influence of Processing Parameters on Laser-Assisted Reactive Sintering of a Mixture of Ni and Ti Powders

Additive manufacturing (AM) plays a crucial role in the development of NiTi alloys, enabling the creation of complex and customized structures while optimizing properties for various biomedical and industrial applications. The aim of this paper was to investigate the influence of laser scanning speed on laser-assisted reactive sintering of a mixture of No and Ti powders. The samples were sintered at two different beam speeds, 4 and 5 4 mm/s and their morphological and microstructural characteristics were investigated. Scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM/EDX) and X-ray diffraction (XRD) analyses revealed the presence of intermetallic compounds rich in Ni and Ti for both scanning speeds; however, the scanning speed of 5 mm/s produced a microstructure with greater porosity, leading to a sintered body with poorer consolidation. Thus, employing a slower beam scanning of 4 mm/s seems to be a better alternative in the laser-assisted reactive sintering of NiTi alloys.

Advanced manufacturing of high conductive graphene coatings on polyethylene terephthalate substrate through laser-induced processes and thermal optimization

Graphene holds immense promise for applications in sensors due to its exceptional properties such as large carrier mobility and high electrical and thermal conductivities. Nevertheless, the manufacture of high conductive graphene films onto flexible substrates remains a formidable challenge. This paper presented a novel method for the precise incorporation of high conductive graphene coatings onto polyethylene terephthalate (PET) through the back transfer of laser-reduced graphene oxide (LRGO) from Al2O3 ceramic to PET. A record-low sheet resistance for LRGO so far, approximately 12 Ω/sq. was achieved, meantime exhibiting 5B level adhesion performance with PET substrate. The sheet resistance of the transferred LRGO was measured and demonstrated to be adjustable by manipulating laser power, scanning speed, and hatch distance. The analysis of reduction of graphene oxide (GO) involved a comprehensive examination of alterations in topography image, chemical bond content, C/O atomic ratio, and ID/IG intensity ratio. These observations provided insights into the factors contributing to the decrease of sheet resistance within LRGO. The adoption of a continuous gigahertz (GHz) femtosecond laser as the light source, coupled with the incorporation of thermally conductive Al2O3 plate as a heat sink, facilitated the manufacturing processes including reduction and transfer of GO. The manufacture of graphene coatings with high electrical conductivity on PET in a versatile and cost-effective manner, presents significant potential for diverse applications, particularly in conductive-coatings-based sensors.

Powder Bed Fusion-Laser Beam of IN939: The Effect of Process Parameters on the Relative Density, Defect Formation, Surface Roughness and Microstructure.

This study investigates the effects of process parameters in the powder bed fusion-laser beam (PBF-LB) process on IN939 samples. The parameters examined include laser power (160, 180, and 200 W), laser scanning speed (400, 800, and 1200 mm/s), and hatch distance (50, 80, and 110 μm). The study focuses on how these parameters affect surface roughness, relative density, defect formation, and the microstructure of the samples. Surface roughness analysis revealed that the average surface roughness (Sa) values of the sample ranged from 4.6 μm to 9.5 μm, while the average height difference (Sz) varied from 78.7 μm to 176.7 μm. Furthermore, increasing the hatch distance from 50 μm to 110 μm while maintaining constant laser power and scanning speed led to a decrease in surface roughness. Relative density analysis indicated that the highest relative density was 99.35%, and the lowest was 93.56%. Additionally, the average porosity values were calculated, with the lowest being 0.06% and the highest reaching 9.18%. Although some samples had identical average porosity values, they differed in porosity/mm2 and average Feret size. Variations in relative density and average porosity were noted in samples with the same volumetric energy density (VED) due to different process parameters. High VED led to large, irregular pores in several samples. Microcracks, less than 50 μm in length, were present, indicating solidification cracks. The microstructural analysis of the XZ planes revealed arc-shaped melt pools, columnar elongated grains aligned with the build direction, and cellular structures with columnar dendrites. This study provides insights for optimizing PBF-LB process parameters to enhance the quality of IN939 components.

Effects of process parameters on the surface characteristics of laser powder bed fusion printed parts: machine learning predictions with random forest and support vector regression

Laser powder bed fusion (L-PBF) fuses metallic powder using a high-energy laser beam, forming parts layer by layer. This technique offers flexibility and design freedom in metal additive manufacturing (MAM). However, achieving the desired surface quality remains challenging and impacts functionality and reliability. L-PBF process parameters significantly influence surface roughness. Identifying the most critical factors among numerous parameters is essential for improving quality. This study examines the effects of key process parameters on the surface roughness of AlSi10Mg, a widely used aluminum alloy in high-tech industries, fabricated by L-PBF. Part orientation, laser power, scanning speed, and layer thickness were identified as crucial parameters via cause-and-effect analysis. To systematically examine their effects, the Taguchi method was employed within the framework of the design of experiment (DoE). Experimental results and statistical analysis revealed that laser power, scanning speed, and layer thickness significantly influence surface roughness parameters: arithmetic mean (Ra) and root mean square (Rq). Main effect plots and energy density analyses confirmed their impact on surface quality. Microscopic investigations identified surface flaws such as spattering, balling, and porosity contributing to poor quality. Given the complex interplay between parameters and surface quality, accurately predicting their effects is challenging. To address this, machine learning models, specifically random forest regression (RFR) and support vector regression (SVR), were used to predict the effects on surface roughness. The RFR model’s R2 values for predicting Ra and Rq are 97% and 85%, while the SVR model’s predictions are 85% and 66%, respectively. Evaluation metrics demonstrated that the RFR model outperformed SVR in predicting surface roughness.

Influence of scanning speed on titanium alloy processed with TC4+Ni60/hBN composite powder by laser metal deposition coating technology

PurposeRegarding the broadening of the titanium alloy application field, the surface treatment coating of TC4 alloy has become an essential global research topic. This study aims to illustrate the titanium-based composite coating is created by laser cladding TC4+Ni60/hBN composite powder onto the surface of the TC4 alloy.Design/methodology/approachDifferent laser scanning speeds were initially selected to prepare TC4+Ni60/hBN titanium-based composite coating on the surface of TC4 alloy using RFL-C1000 Raycus fiber laser. Second, the cladding layers with different laser scanning speeds are composed of Ti2Ni, TiN0.3, TiC, TiB, α-Ti and other phases. Finally, precision balances, friction and wear testing machines were used to analyze and test the structure, phase, hardness, wear amount and friction coefficient of the composite coating and to study the effect of laser scanning speed on the microstructure and properties of the titanium-based composite coating.FindingsIt is evident that at the low laser scanning speed, the reinforcing phase agglomeration area is distributed in the substrate as a network. Increasing the laser scanning speed can reduce the cladding layer's friction coefficient and improve the cladding layer's hardness and wear resistance. But too high a laser scanning speed will cause defects such as pores and cracks in the cladding layer and also affect the cladding layer. The bonding performance of the layer and the substrate is optimal in this research at a laser scanning speed of 10 mm/s.Originality/valueThis research has practical value in improving the quality of surface treatment coating in modern aerospace and automotive companies.

Effect of Laser Parameters on Surface Texture of Polyformaldehyde and Parameter Optimization

This research aimed to investigate the influence of laser process parameters on the surface texture of Polyformaldehyde (POM) and to improve its processability and process predictability. A comparative experiment and analysis involving multiple processing parameters, including laser power, scanning speed, and pulse width, were conducted on POM. Statistical prediction models of laser processing POM were established among the laser power, scanning speed, pulse width, texture depth, surface roughness at the bottom of texture, and multi-objective optimization and experimental verification of process parameters were carried out based on the grey-Taguchi analysis method. Experimental results show that the laser power and scanning speed significantly affect the texture depth. Higher laser power and lower scanning speed are conducive to forming depth. The surface roughness at the bottom of the texture increases with the increase in scanning speed and shows a tendency to rise and then fall as the laser power increases. The surface roughness and texture depth obtained under the optimal process parameters(A5B1C1) are 1.373 μm and 466.891 μm, respectively, which were reduced by 10.08 % and increased by 3.42 % compared with the minimum surface roughness and maximum depth in the orthogonal experiments. The validation experiments of the prediction model show that it can meet the reliability requirements, and the errors of the predicted values of depth and surface roughness are 1.86 % and 7.60 %, respectively. The above research provides theoretical and experimental support for the precise control of surface texture prepared by laser processing POM.

Defects, organization, and properties of TiB2–TiC Bi-ceramic phase by laser cladding in situ synthesis

An enhanced Ni50A composite coating was formed on the AISI 1045 steel surface using Ti, B4C, and Ni50A powders. The work meticulously examined the effects of various process parameters, including laser power (800, 1200, and 1600 W) and scanning speed (4, 6, and 8 mm/s), as well as different Ti/B4C powder ratios (2:1, 3:1, and 4:1, mol.%) on the defect, hardness, wear resistance, and corrosion resistance of the coating. The microstructure of the coating showed that TiB2 and TiC synthesized in situ were the key phases for coating enhancement, and there were a variety of solid solutions (FeNi3 and Cr2Ni3). TiB2 particles showed dark gray hexagons and long rectangular blocks. The TiC particles mainly appeared in light gray dendritic shapes, occasionally petal-shaped and equiaxial forms, and grew around TiB2. The research results showed that increased laser power and the Ti/B4C ratio decreased coating hardness and wear resistance. When the scanning speed increased, the hardness and wear resistance of the coating were significantly improved. The main wear mechanisms of the TiB2–TiC ceramic phase coating were oxidative wear and abrasive particle wear. Besides, the higher laser power and Ti/B4C ratio reduced the defect rate of the coating and enhanced the corrosion resistance. However, as the scanning speed increased, the defect rate increased, which decreased the corrosion resistance of the coating. The comprehensive performance evaluation showed that under the conditions of the laser power of 1200 W, a scanning speed of 8 mm/s, and a Ti/B4C ratio of 4:1, the coating exhibited optimal performance (dilution rate = 10.928±0.090%, defect rate = 8.657±0.128%, hardness = 63.300±1.159 HRC, wear volume = 0.0149±0.000100 mm3, Ecorr = −0.565±0.001 V, Icorr = 1.058E−06±1.528E−09A⋅cm2). The results provide an important basis for research on the high-quality and efficient strengthening technology and performance of refractory alloys.