For a non-contact, non-destructive quality evaluation, laser ultrasonic testing (LUT) has received increasing attention in complex manufacturing processes, such as additive manufacturing (AM). This work assessed the LUT method for the inspection of internal hole defects in additive manufactured Ti-6Al-4V part. A Q-switched pulsed laser was utilized to generate ultrasound waves on the top surface of a Ti-6Al-4V alloy part, and a laser Doppler vibrometer (LDV) was utilized to detect the ultrasound waves. Sub-millimeter (0.8 mm diameter) internal hole defect was successfully detected by using the established LUT system in pulse-echo mode. The method achieved a relatively high resolution, suggesting significant application prospects in the non-destructive evaluation of AM part. The relationship between the diameter of the hole defects and the amplitude of the laser-generated Rayleigh waves was studied. X-ray computed tomography (XCT) was conducted to validate the results obtained from the LUT system.

Laser polishing is a widely used technology to improve the surface quality of the products. However, the investigation on the physical mechanism is still lacking. In this paper, the established numerical transient model reveals the rough surface evolution mechanism during laser polishing. Mass transfer driven by Marangoni force, surface tension and gravity appears in the laser-induced molten pool so that the polished surface topography tends to be smoother. The AlSi10Mg samples fabricated by laser-based powder bed fusion were polished at different laser hatching spaces, passes and directions to gain insight into the variation of the surface morphologies, roughness and microhardness in this paper. The experimental results show that after laser polishing, the surface roughness of Ra and Sa of the upper surface can be reduced from 12.5 μm to 3.7 μm and from to 29.3 μm to 8.4 μm, respectively, due to sufficient wetting in the molten pool. The microhardness of the upper surface can be elevated from 112.3 HV to 176.9 HV under the combined influence of the grain refinement, elements distribution change and surface defects elimination. Better surface quality can be gained by decreasing the hatching space, increasing polishing pass or choosing apposite laser direction.

Selective laser melting (SLM) is a layer by layer process of melting and solidifying of metal powders. The surface quality of the previous layer directly affects the uniformity of the next layer. If the surface roughness value of the previous layer is large, there is the possibility of not being able to complete the layering process such that the entire process has to be abandoned. At least, it may result in long term durability problem and the inhomogeneity, may even make the processed structure not be able to be predicted. In the present study, the ability of a fiber laser to in-situ polish the rough surfaces of four typical additive-manufactured alloys, namely, Ti6Al4V, AlSi10Mg, 316L and IN718 was demonstrated. The results revealed that the surface roughness of the as-received alloys could be reduced to about 3 μm through the application of the laser-polishing process, and the initial surfaces had roughness values of 8.80–16.64 μm. Meanwhile, for a given energy density, a higher laser power produced a laser-polishing effect that was often more obvious, with the surface roughness decreasing with an increase in the laser power. Further, the polishing strategy will be optimized by simulation in our following study.

Heat-resistant, load-bearing components are common in aircraft, and they have high requirements for lightweight and mechanical performance. Lattice topology optimization can achieve high mechanical properties and obtain lightweight designs. Appropriate lattice selection is crucial when employing the lattice topology optimization method. The mechanical properties of a structure can be optimized by choosing lattice structures suitable for the specific stress environment being endured by the structural components. Metal lattice structures exhibit excellent unidirectional load-bearing performance and the triply periodic minimal surface (TPMS) porous structure can satisfy multi-scale free designs. Both lattice types can provide unique advantages; therefore, we designed three types of metal lattices (body-centered cubic (BCC), BCC with Z-struts (BCCZ), and honeycomb) and three types of TPMS lattices (gyroid, primitive, and I-Wrapped Package (I-WP)) combined with the solid shell. Each was designed with high level of relative density (40%, 50%, 60%, 70%, and 80%), which can be directly used in engineering practice. All test specimens were manufactured by selective laser melting (SLM) technology using Inconel 718 superalloy as the material and underwent static tensile testing. We found that the honeycomb test specimen exhibits the best strength, toughness, and stiffness properties among all structures evaluated, which is especially suitable for the lattice topology optimization design of heat-resistant, unidirectional load-bearing structures within aircraft. Furthermore, we also found an interesting phenomenon that the toughness of the primitive and honeycomb porous test specimens exhibited sudden increases from 70% to 80% and from 50% to 60% relative density, respectively, due to their structural characteristics. According to the range of the exponent value n and the deformation laws of porous structures, we also concluded that a porous structure would exhibit a stretching-dominated deformation behavior when exponent value n < 0.3, a bending-dominated deformation behavior when n > 0.55, and a stretching-bending-dominated deformation behavior when 0.3 < n < 0.55. This study can provide a design basis for selecting an appropriate lattice in lattice topology optimization design.

The quality stability and batch consistency of laser powder bed fusion products are key issues that must be solved in additive manufacturing. The melt pool radiation intensity data of laser powder bed fusion contain a significant amount of forming process information, and studies have shown that the analysis of melt pool radiation intensity using data-driven methods can achieve online quality judgment; however, there are still speed and accuracy problems. In this study, we propose a data-driven model for hardness predictions of laser powder bed fusion products based on process parameters fused with power spectrum features of melt pool intensity data, which quickly and accurately predicts the microhardness of laser powder bed fusion specimens and can make constructive guidance for closed-loop feedback quality regulation in practical production. The effects of three integrated learning models, Random Forest, XGBoost and LightGBM, are also compared. The results indicate that random forest has the highest prediction accuracy in this dataset; however, it has the limitation of slow training and prediction speeds. The LightGBM algorithm has the fastest training and prediction speeds, about 1.4% and 4.4% of the random forest, respectively; however, the prediction accuracy is lower than that of random forest and XGBoost. XGBoost has the best overall comparative performance with adequate training and prediction speeds, about 23.7% and 37.9% of the random forest, respectively, while ensuring a specified prediction accuracy, which is suitable for application in engineering practices.