Snehashis Pal

Laser-Powder Bed Fusion is one of the most widely used Additive Manufacturing processes for metal and polymeric parts, enabling the realisation of complex, free-form, and low-density structures that are impossible or difficult to produce using conventional manufacturing techniques. However, this process is characterised by defects such as porosity, roughness, layer delamination, and others that limit mechanical behaviour. This work aims to analyse the correlation between mechanical performance and shape complexity in additive manufacturing parts. A complexity index was adopted to characterise the geometry of specimens and lattice structures, and prototyping and testing activities were carried out to evaluate the correlation between shape complexity and performance. The metals used in the experimental analysis are Al-Si-Mg and Al-Cu alloys produced by Laser Powder Bed Fusion. To extend the approach and determine whether comparable results can be obtained in polymers, the authors also considered specimens made from PA12 by Powder Bed Fusion, and from a biocompatible resin produced by VAT photopolymerization. The results show a dependence between shape complexity and mechanical performance, highlighting a non-linear behaviour. A higher shape complexity is associated with lower internal porosity. However, contrary to initial assumptions, it also shows lower tensile strength. Although shape complexity offers a significant advantage in tailoring component performance in mechanical products, the results show that designers must use it with particular attention because material performance fluctuates as shape complexity increases.

Laser-Powder Bed Fusion process is one of the most widespread Additive Manufacturing processes for metal and polymeric parts, enabling the realisation of complex, free-form, and low-density structures that are impossible or difficult to realise with traditional manufacturing techniques. However, this process is characterised by defects such as porosity, roughness, layer delamination, and others that limit mechanical behaviour. This work aims to analyse the correlation between mechanical performance and shape complexity in additive manufacturing parts. A complexity index was adopted to characterise the geometry of specimens and lattice structures, and prototyping and testing activities were carried out to evaluate the correlation between the shape complexity and performance. The materials involved in the experimental analysis are Al-Si-Mg, Al-Cu, and PA12, all realised by Laser-Powder Bed Fusion processes. To extend the approach, specimens with a biocompatible resin realised by VAT photopolymerization were also considered. The results show a dependence between shape complexity and mechanical performance, highlighting a non-linear behaviour. Despite the shape complexity offering a significant advantage in achieving tailored component performance in mechanical products, the results showed that designers must use it with particular attention due to fluctuating material performance as shape complexity increases.

This work used machine learning to forecast product density and optimize the laser powder bed fusion (LPBF) process for parts made of pure zinc (Zn). A relative density of 90–97% (6.42–6.95 g/cm³) was obtained by varying combinations of key process parameters, including laser power, scanning speed, track overlapping, hatch spacing, and layer thickness. Machine learning provided models for density prediction and better comprehension of the impact of input parameters. A SHapley Additive exPlanation (SHAP) analysis quantified the contributions of specific features, enhancing model interpretability. Fifty-one experimental runs were used to test several methods, including Bayesian ridge, CatBoost, elastic net, lasso, linear regression, random forest, ridge regression, and XGBoost. CatBoost performed best, with a test coefficient of determination (R²) of 0.893, a mean absolute error (MAPE) of 0.010 and a root mean square error (RMSE) of 0.015. A feature importance analysis showed that laser power (49%) and scanning speed (42%) had the greatest influence, while hatch spacing (5%) and layer thickness (4%) had minimal impacts on product density. Therefore, selecting the correct optimized set of process parameters determines the resulting density and can support more efficient LPBF process development.

This study examined the melting behavior and flowability of pure magnesium during selective laser melting. The potential to increase product density was also investigated. Various combinations of manufacturing parameters were considered. The laser power was gradually increased in different machine runs, with different scanning speeds for each run to vary the energy density (ED). The laser power ranged from 10 W to 75 W, and the scanning speed ranged from 100 mm/s to 800 mm/s. Lower laser powers resulted in poor melting, while higher laser powers produced better melting, with significant differences even when the ED was the same. High EDs between 3.50 J/mm² and 4.30 J/mm² led to a lack of melting at low laser power and to an unstable melt pool with significant spattering at high laser power. In contrast, moderate EDs in the range of 1.40 J/mm² to 2.90 J/mm² resulted in better density at high laser power. Higher scanning speeds helped to avoid the formation of a dense smog cloud and provided sufficient energy in a short time with the aid of higher laser power. Therefore, increasing both laser power and scanning speed improved melting performance and increased product density. The relative product density ranged from 80 % to 96.5 %. Reducing the layer thickness from 50 µm to 25 µm at a laser power of 40 W resulted in the formation of a well-formed melt pool in some areas and significant melt spattering in others, which led to a deterioration in density.

In this work, Laser Powder Bed Fusion (LPBF), an additive manufacturing (AM) process, was optimised to produce pure magnesium components. The focus of the presented work is on the prediction of the relative product density using the machine learning model XGBoost to improve the production process and thus the usability of the material for practical use. Experimental tests with different parameters, laser power, scanning speed and layer thickness, and fixed parameters, track overlapping and hatching distance, were analysed and resulted in relative material densities between 89.29% and 99.975%. The XGBoost model showed high predictive power, achieving an R² test result of 0.835, a mean absolute error (MAE) of 0.728 and a root mean square error (RMSE) of 0.982. Feature importance analysis showed that the interaction of laser power and scanning speed had the largest influence on the predictions at 35.9%, followed by laser power × layer thickness at 29.0%. The individual contributions were laser power (11.8%), scanning speed (10.7%), scanning speed × layer thickness (9.0%) and layer thickness (3.6%). These results provide a data-based method for LPBF parameter settings that improve manufacturing efficiency and component performance in the aerospace, automotive and biomedical industries and identify optimal parameter regions for a high density, serving as a pre-optimisation stage.

The manufacture of lightweight components is one of the most important requirements in the automotive and aerospace industries. Gears, on the other hand, are among the heaviest parts in terms of their total weight. Accordingly, a spur gear was considered, the body of which was configured as a lattice structure to make it lightweight. In addition, the structure was optimized by topology optimization using ProTOP software. Subsequently, the gear was manufactured by a selective laser melting process by using a strong and lightweight material, namely Ti-6Al-4V. This study defeated the problems of manufacturing orientation, surface roughness, support structure, and bending due to the high thermal gradient in the selective laser melting process. To experimentally investigate the benefits of such a lightweight gear body structure, a new test rig with a closed loop was developed. This rig enabled measurements of strains in the gear ring, hub, and tooth root. The experimental results confirmed that a specifically designed and selectively laser-melted, lightweight cellular lattice structure in the gear body can significantly influence strain. This is especially significant with respect to strain levels and their time-dependent variations in the hub section of the gear body.

This study investigated how the tensile properties of selectively laser-sintered polyamide 12 (PA12) specimens are affected by aging and refresh ratio. Starting with a new powder, printing cycles of approximately ten hours were performed, during which tensile and density specimens were printed. After each cycle, the powder was tested for melt flow index (MFI). In the first part, the best processing direction was determined. It was found that tensile strength, modulus of elasticity, elongation at break and MFI showed a linear trend, with the independent variable being the number of hours at elevated temperature. This was analyzed by linear regression. MFI increased with the number of hours at elevated aging, while all other properties mentioned decreased.

When manufacturing complicated products where both material and design play a role, especially thin and curved components, it is difficult to maintain accurate dimensions in Selective Laser Melting. Considering these difficulties, this article presents the dimensional errors in the fabrication of Ti-6Al-4V discs and their thermomechanics during manufacturing. Various combinations of laser processing parameters were used to fabricate the 2.00 mm thick discs with a diameter of 5.70 mm. It was found that the thickness shortened and the round shape changed to an oval shape for most of the discs. The thickness decreased along the build-up direction from the bottom to the top and formed a taper that increased with increasing energy density (ED). The horizontal diameter of the discs changed slightly, while the vertical diameters changed remarkably with increasing ED. On the other hand, reducing the laser power resulted in a reduction of the roundness error, while it caused a reduction of the thickness. The hatch spacing significantly affected the volume of the melt pool and caused a change in the vertical diameter. The central part of the curved surface of the discs became concave and the concavity increased due to the increasing ED.