The key to keyhole formation: The formation of keyholes, or vapor-filled depressions, during laser welding presents a large problem for additive manufacturing. Cunningham et al. used high-speed x-ray imaging to take a detailed look at keyhole formation in a titanium alloy. They found a simplified relationship between operational parameters and keyhole shape, which may allow for the prevention of pore formation going forward. Science , this issue p. 849

Driving the pores away: The formation of “keyholes” (vapor-filled depressions) during additive manufacturing leads to porosity, which degrades alloy performance, especially fracture properties, and remains a big challenge for the 3D printing of metals. Zhao et al. used high-speed x-ray imaging to take a detailed look at how keyhole formation connects to porosity in a titanium alloy. They found that instability at the keyhole tip drives pores away to get trapped in the solidification front. Understanding this process and the operating parameters under which it occurs provides a roadmap for avoiding porosity and building high-quality metal parts. Science , this issue p. 1080

Porosity defects are currently a major factor that hinders the widespread adoption of laser-based metal additive manufacturing technologies. One common porosity occurs when an unstable vapor depression zone (keyhole) forms because of excess laser energy input. With simultaneous high-speed synchrotron x-ray imaging and thermal imaging, coupled with multiphysics simulations, we discovered two types of keyhole oscillation in laser powder bed fusion of Ti-6Al-4V. Amplifying this understanding with machine learning, we developed an approach for detecting the stochastic keyhole porosity generation events with submillisecond temporal resolution and near-perfect prediction rate. The highly accurate data labeling enabled by operando x-ray imaging allowed us to demonstrate a facile and practical way to adopt our approach in commercial systems.

The high-speed synchrotron X-ray imaging technique was synchronized with a custom-built laser-melting setup to capture the dynamics of laser powder-bed fusion processes in situ. Various significant phenomena, including vapor-depression and melt-pool dynamics and powder-spatter ejection, were captured with high spatial and temporal resolution. Imaging frame rates of up to 10 MHz were used to capture the rapid changes in these highly dynamic phenomena. At the same time, relatively slow frame rates were employed to capture large-scale changes during the process. This experimental platform will be vital in the further understanding of laser additive manufacturing processes and will be particularly helpful in guiding efforts to reduce or eliminate microstructural defects in additively manufactured parts.

Although additive manufacturing (AM) is becoming increasingly popular for various applications, few studies have addressed design and potential problems in thin wall fabrication for the laser powder bed fusion (LPBF) process. In the LPBF process, rapid cooling induces thermal shrinkage, which in turn, results in high residual stress and complicates thin wall fabrication. The minimum wall thickness is limited by the parameters and machine settings while the dimensional accuracy is controlled by the powder size, scan strategy, and part geometry. The ability to fabricate thin-wall components is important for applications such as heat exchangers (HX). This study explores the performance of the LPBF process by fabricating thin walls with extreme geometries in different processing conditions and alloys using an EOS M290 LPBF machine. Results show that the material, part design, and scanning strategy contribute to the variation in thin wall dimensions. A maximum inclination angle of 60° and a minimum wall thickness of ~ 100 μm in Ti-6Al-4V, Inconel 718, and AlSi10Mg were achieved using optimized part design and processing conditions. The effects of part design and material on the thermal distortion and surface finish of thin walls were also investigated leading to a discussion on how the scan mode assigned by the EOS software affects design and fabrication. Additionally, synchrotron-based X-ray micro-tomography (μSXCT) was utilized to quantify the porosity in thin-wall structures and to correlate it with the integrity of the structures. Comprehensive design guidelines presented in this work can increase the success rate of fabricating thin-wall geometries.

In the laser powder bed fusion additive manufacturing of metals, extreme thermal conditions create many highly dynamic physical phenomena, such as vaporization and recoil, Marangoni convection, and protrusion and keyhole instability. Collectively, however, the full set of phenomena is too complicated for practical applications and, in reality, the melting modes are used as a guideline for printing. With an increasing local material temperature beyond the boiling point, the mode can change from conduction to keyhole. These mode designations ignore laser-matter interaction details but in many cases are adequate to determine the approximate microstructures, and hence the properties of the build. To date no consistent, common, and coherent definitions have been agreed upon because of historic limitations in melt pool and vapor depression morphology measurements. In this review, process-based definitions of different melting modes are distinguished from those based on postmortem evidence. The latter are derived mainly from the transverse cross sections of the fusion zone, whereas the former come directly from time-resolved x-ray imaging of melt pool and vapor depression morphologies. These process-based definitions are more strict and physically sound, and they offer new guidelines for laser additive manufacturing practices and create new research directions. The significance of the keyhole, which substantially enhances the laser energy absorption by the melt pool, is highlighted. Recent studies strongly suggest that stable-keyhole laser melting enables efficient, sustainable, and robust additive manufacturing. The realization of this scenario demands the development of multiphysics models, signal translations from morphology to other feasible signals, and in-process metrology across platforms and scales.