On the role of melt flow into the surface structure and porosity development during selective laser melting

In this study, the development of surface structure and porosity of Ti–6Al–4V samples fabricated by selective laser melting under different laser scanning speeds and powder layer thicknesses has been studied and correlated with the melt flow behaviour through both experimental and modelling approaches. The as-fabricated samples were investigated using optical microscopy (OM) and scanning electron microscopy (SEM). The interaction between laser beam and powder particles was studied by both high speed imaging observation and computational fluid dynamics (CFD) calculation. It was found that at a high laser power and a fixed powder layer thickness (20 μm), the samples contain particularly low porosity when the laser scanning speeds are below 2700 mm/s. Further increase of scanning speed led to increase of porosity but not significantly. The porosity is even more sensitive to powder layer thickness with the use of thick powder layers (above 40 μm) leading to significant porosity. The increase of porosity with laser scanning speed and powder layer thickness is not inconsistent with the observed increase in surface roughness complicated by increasingly irregular-shaped laser scanned tracks and an increased number of discontinuity and cave-like pores on the top surfaces. The formation of pores and development of rough surfaces were found by both high speed imaging and modelling, to be strongly associated with unstable melt flow and splashing of molten material

Nucleation of recrystallisation in castings of single crystal Ni-based superalloys

Recrystallisation in single crystal Ni-based superalloys during solution heat treatment results in a significant cost to the investment casting industry. In this paper two sources of surface nucleation have been identified in the alloy CMSX-4^®. Firstly, Electron Backscattered Diffraction (EBSD) has revealed micro-grains of γ', between 2 and 30 μm diameter in the layer of surface eutectic found in the upper part of the casting. These have high angle boundaries with respect to the bulk single crystal and a fraction coarsen during solution heat treatment. Secondly, in the lower regions where surface eutectic does not form, locally deformed regions, 5–20 μm deep, form where the metal adheres to the mould. The local strain causes misorientations up to ≈20° with respect the bulk single crystal, and after heat treatment these regions develop into small grains of similar low-angle misorientations. However, they also form twins to produce further grains which have mobile high-angle boundaries with respect to the bulk single crystal. Experiments have shown that micro-grains at the surface grow to cause full recrystallisation where there is sufficient strain in the bulk material, and by removing these surface defects, recrystallisation can be completely mitigated. Etching of the cast surface is demonstrated to be an effective method of achieving this.Engineering and Physical Sciences Research Council and Rolls-Royce plc for financial support from Dorothy Hodgkin Postgraduate Awards and the EPRSC-Rolls-Royce Strategic Partnership Grant EP/H500375/

Laser-based Additive Manufacturing of Bulk Metallic Glasses: Recent Advances and Future Perspectives for Biomedical Applications

Bulk metallic glasses (BMGs) are non-crystalline class of advanced materials and have found potential applications in the biomedical field. Although there are numerous conventional manufacturing approaches for processing BMGs, the most commonly used like copper-mould casting have some limitations. It is not easy to manage and control the critical cooling rate, especially when the fabrication of complex BMG geometries is involved. Other limitations of these techniques include the size constraints, non-flexibility, and the tooling and accessories are costly. The emergence of additive manufacturing (AM) has opened another promising manufacturing route for processing BMGs. AM processes, particularly laser powder-bed fusion (PBF-LB/M) builds parts layer-by-layer and successively fused the powder-melted feedstocks using prescribed computer-controlled laser scanner system, thereby forming a BMGs part upon sufficiently rapid cooling to ensure the glass forming-ability. PBF-LB/M overcomes the limitations of the pre-existing BMGs processing techniques by not only improving the part size, but also produces exceptionally complex structures and patient-specific implants. This review article aims to summarise and discuss the mechanism of BMGs formation through PBF-LB/M for biomedical applications and to highlight the current scientific and technological challenges as well as the future research perspectives towards overcoming the pore-mediated microcracks, partial crystallisation, brittleness and BMG size constraint

Laser-inherent porosity defects in additively manufactured Ti-6Al-4V implant: Formation, distribution, and effect on fatigue performance

Porosity defects are inherently present in Ti–6Al–4V (Ti6-4) parts produced using additive manufacturing (AM) methods like laser powder-bed fusion (LPBF). This work aims to investigate different laser-inherent porosity defects at various LPBF parameter settings and assess their impact on the fatigue behaviour of Ti6-4 implants processed by LPBF. The presence of LPBF-inherent porosity defects with different shapes and sizes was established using microstructural examination and X-ray micro-CT analysis. These mostly comprise lack-of-fusion porosity (LoFP), gas-entrapped porosity (GeP), and pores-induced microcracks. Volumetric porosity defects were seen to range from 1.9 × 104 to 9.52 × 105 μm3. The L-1 specimen exhibited the lowest defect, while the L-6 specimen displayed the largest number of defects. While LoFP defects predominate in L-6, there was a notable presence of GeP defects in the specimens processed using the factory default condition (L-D). Upon examination of the majority of specimens, GeP and LoFP coalesced to form clusters, leading to the formation of pores-induced microcracks. This ultimately leads to a decrease in fatigue performance. By maintaining the power at the default setting and increasing the scan speed by 8% of the default value, a specimen (L-1) with minimal porosity defects and superior fatigue performance is achieved. L-6 exhibits defects with significant dimensions and irregular form. Consequently, it displays inferior fatigue characteristics

In-process monitoring and direct simulation of Argon shielding gas and vapour dynamics to control laser-matter interaction in laser powder bed fusion additive manufacturing

Laser powder bed fusion (L-PBF) additive manufacturing (AM) enables the fabrication of parts with precise dimensional control, freedom of design and material properties similar to or better than those fabricated using traditional manufacturing approaches. AM quality control depends upon the fundamental of the laser-matter interaction during metal AM using L-PBF to exploit the potential use of the materials and process control. In this work, thermal-fluid dynamics in gas chamber experimentally and computationally is used to elucidate the interplay between vapour, liquid, and solid phases in L-PBF. It is revealed that the argon (Ar) shielding gas flow with varied inlet velocities by different nozzles has a pronounced effect to minimise the laser-fume interaction, resulting in the reduction in unstable metal vapour flow and enhancing laser absorptivity. In-process monitoring via high-speed visualisation has been used to understand the simultaneous gas plume dynamics as a result of vapourisation and subsequent laser-fume interaction, backed up by thermal-fluid flow simulation. Unfavourable process dynamics associated with unwanted defects such as lack of fusion can be avoided to improve process design and enhance process stability

Pore evolution mechanisms during directed energy deposition additive manufacturing.

Porosity in directed energy deposition (DED) deteriorates mechanical performances of components, limiting safety-critical applications. However, how pores arise and evolve in DED remains unclear. Here, we reveal pore evolution mechanisms during DED using in situ X-ray imaging and multi-physics modelling. We quantify five mechanisms contributing to pore formation, migration, pushing, growth, removal and entrapment: (i) bubbles from gas atomised powder enter the melt pool, and then migrate circularly or laterally; (ii) small bubbles can escape from the pool surface, or coalesce into larger bubbles, or be entrapped by solidification fronts; (iii) larger coalesced bubbles can remain in the pool for long periods, pushed by the solid/liquid interface; (iv) Marangoni surface shear flow overcomes buoyancy, keeping larger bubbles from popping out; and (v) once large bubbles reach critical sizes they escape from the pool surface or are trapped in DED tracks. These mechanisms can guide the development of pore minimisation strategies

Real-time prediction and adaptive adjustment of continuous casting based on deep learning

Digitalisation of metallurgical manufacturing, especially technological continuous casting using numerical models of heat and mass transfer and subsequent solidification has been developed to achieve high manufacturing efficiency with minimum defects and hence low scrappage. It is still challenging to perform adaptive closed-loop process adjustment using high-fidelity computation in real-time. To address this challenge, surrogate models are a good option to replace the high-fidelity model, with acceptable accuracy and less computational time and cost. Based on deep learning technology, here we developed a real-time prediction (ReP) model to predict the three-dimensional (3D) temperature field distribution in continuous casting on millisecond timescale, with mean absolute error (MAE) of 4.19 K and mean absolute percent error (MAPE) of 0.49% on test data. Moreover, by combining the ReP model with machine learning technology—Bayesian optimisation, we realised the rapid decision-making intelligent adaptation of the operating parameters for continuous casting with high predictive capability. This innovative and reliable method has a great potential in the intelligent control of the metallurgical manufacturing process