One problematic task in the laser-based powder bed fusion (LB-PBF) process is the estimation of meltpool depth, which is a function of the process parameters and thermophysical properties of the materials. In this research, the effective factors that drive the meltpool depth such as optical penetration depth, angle of incidence, the ratio of laser power to scan speed, surface properties and plasma formation are discussed. The model is useful to estimate the meltpool depth for various manufacturing conditions. A proposed methodology is based on the simulation of a set of process parameters to obtain the variation of meltpool depth and temperature, followed by validation with reference to experimental test data. Numerical simulation of the LB-PBF process was performed using the computational scientific tool “Flow3D Version 11.2” to obtain the meltpool features. The simulation data was then developed into a predictive analytical model for meltpool depth and temperature based on the thermophysical powder properties and associated parameters. The novelty and contribution of this research are characterising the fundamental governing factors on meltpool depth and developing an analytical model based on process parameters and powder properties. The predictor model helps to accurately estimate the meltpool depth which is important and has to be sufficient to effectively fuse the powder to the build plate or the previously solidified layers ensuring proper bonding quality. Results showed that the developed analytical model has a high accuracy to predict the meltpool depth. The model is useful to rapidly estimate the optimal process window before setting up the manufacturing tasks and can therefore save on lead-time and cost. This methodology is generally applied to Inconel 718 processing and is generalisable for any powder of interest. The discussions identified how the effective physical factors govern the induced heat versus meltpool depth which can affect the bonding and the quality of LB-PBF components.

When reporting surface quality, the roughest surface is a reference for the measurements. In LPBF due to recoil pressure and scan movement, asymmetric surface is shaped, and surface roughness has different values in different measurement orientations. In this research, the influence of the laser powder bed fusion (LPBF) process parameters on surface tension and roughness of Ti-6AI-4 V parts in three orientations are investigated. To improve the mechanical properties, heat treatment was carried out and added to the designed matrix to generate a comprehensive data set. Taguchi design of experiment was employed to print 25 samples with five process parameters and post-processing. The effect and interaction of the parameters on the formation of surface profile comprising tension, morphology and roughness in various directions have been analysed. The main contribution of this paper is developing a model to approximate the melting pool temperature and surface tension based on the process parameters. Other contributions are an analysis of process parameters to determine the formation and variation of surface tension and roughness and explain the governing mechanisms through rheological phenomena. Results showed that the main driving factors in the variation of surface tension and formation of the surface profile are thermophysical properties of the feedstock, rheology and the temperature of the melting pool. Also, the results showed that while the value of surface tension is the same for each test case, morphology and the value of roughness are different when analysing the surface in perpendicular, parallel and angled directions to laser movement.

One of the main issues of laser-based powder bed fusion (LB-PBF) parts is surface quality and dimensional deviations, which require post-processing. Conventional post-processing such as turning and milling cannot machine internal surfaces and therefore is not suitable for hollow components. In this paper, Ti–6Al–4 V components with different hollow shapes were printed by LB-PBF and post-processed by centrifugal barrel finishing (CBF). Samples were printed based on Taguchi L18 design of experiments (DoE) on the (L18: 2¹ × 3³) matrix and polished in abrasive solution by porcelain triangular media 2 × 2 mm. The effect of process parameters including rotation direction, speed, time and volumetric percentage of abrasive on hardness and manufacturability, including surface quality, material removal rate (MRR) and dimensional deviation, are discussed. The novelty of this work is the application of this process to clean both the internal and external surfaces of LB-PBF parts, where previously it has only been investigated for external surfaces. This paper scrutinized the performance of the CBF on internal geometries, and it was shown for the size of the investigated components, the hexagonal hollow achieved the highest maximum removal rate over the square and circular hollows. In addition, the effect of CBF on plastic deformation and microstructural characterization has been investigated to find the effect of this process on work hardening. The results of this study also show that the rotational speed and the volumetric percentage of the abrasive directly drive the MRR. A higher rotational speed increases the slope of the sliding path and the sliding speed between printed parts and abrasive media, which causes higher cutting and grinding, MRR and media wear rate.

Metal thin-walled structures (TWS) are critical to all engineering industries, however, their complex manufacture has prevented large-scale adoption. The additive manufacturing process of laser-based powder bed fusion (PBF-LB) can produce high-resolution metal TWS with micro-scale geometries and intricate features. However, the thermal nature of PBF-LB increases the likelihood of defect formation. By modeling the manufacturability of metal TWS using a digitally reconstructed model guided by Multi-Layer Perceptron (MLP) Artificial Neural Networks (ANN) and Computational Fluid Dynamics (CFD), this problem can be predicted and then resolved. This research presents the first experimentally validated digital reconstructions guided by ANN-CFD as a predictive tool for PBF-LB manufacturability of metal TWS. This work contributes to the body of knowledge by introducing a new AI-based model to predict TWS porosity, dimensional deviations and distortion. To generate the MLP-ANN model, three control factors were selected: inclination angle, laser power, and the number of laser scan passes. To train the ANN, a full factorial dataset of AlSi10Mg samples was produced. Results show the MLP-ANN model as a precise tool to predict the manufacturability of TWS produced by PBF-LB with accuracy exceeding 90%. The most effective factor for the thickness, dimensional deviations and distortion was found to be the number of laser passes. The results also showed inclination angle was the main driving factor for the porosity of the TWS. The outcomes from this study highlight the value of ANN networks in the prediction and eventual certification of AM processes for global engineering interests.