Impact of powder recoating speed on built properties in PBF-LB process

In powder bed fusion laser-beam (PBF-LB), powder deposition parameters are not commonly adjusted for different materials. In this study, the recoater speed was varied from 150 mm/s to 500 mm/s to study its influence on processability of two different powders. The 420 steel powder possesses better flowability and higher packing density than Inconel 718 at different testing speeds. This is reflected on surface roughness (Sa): Inconel 718 samples show larger variations than 420 steel ones across different locations on the build plate and under different recoating speeds. This demonstrates the necessity of accounting for powder properties for robust PBF-LB processing

Influence of part geometry on spatter formation in laser powder bed fusion of Inconel 718 alloy revealed by optical tomography

The metal powder used during the Laser Powder Bed Fusion (L-PBF) process is usually cycled for reuse in subsequent build jobs for cost-effectiveness and sustainability. Qualification guidelines are being established based on testing results of powder properties in terms of flowability, chemistry and rheological behaviors, etc. for making decisions on whether a batch of reused powder is suitable for producing parts that meet certain requirements. The current paper aims to develop experimental strategies for tracking powder history using novel design of specimens and on-line monitoring. Powder-capturing containers designed with internal lattice structures of varied beam lengths and diameters were manufactured by the L-PBF process using an Inconel 718 powder to investigate the influence of part geometry on the degradation of reused powder. The L-PBF experiment was monitored by a commercial Optical Tomography (OT) system which records the thermal emissions from the build area. Data were extracted from the OT images to evaluate the emissions of spatter particles introduced to the powder bed, which is influenced by the local layer profiles of the lattices and the overall geometries of the container. The collected powder samples were tested by combustion analysis for oxygen content and characterized by Scanning Electron Microscopy (SEM). Surface chemistry analyses of the powders were performed by X-ray Photoelectron Spectroscopy (XPS). Depending on the lattice structure geometry, the oxygen uptake in the powder collected from the containers was increasing by 10 ppm in case of empty container and up to as high as 118 ppm in case of container with larger areas of overhangs and higher surface-to-volume ratio. XPS results revealed the presences of Al-rich and Cr-rich oxides on the surface of powder samples collected from the container filled with lattices of high surface-to-volume ratio and the container filled with lattices of large overhangs, which agrees with the analyses of OT data

Process variation in Laser Powder Bed Fusion of Ti-6Al-4V

In this work, a concept of using surface roughness data as an evaluation tool of the process variation in a commercial Laser Powder Bed Fusion (L-PBF) machine is demonstrated. The interactive effects of powder recoating, spatter generation, gas flow and heat transfer are responsible for the intra-build quality inconsistency of the L-PBF process. Novel specimens and experiments were designed to investigate how surface roughness varies across the build volume and with the progression of a build. The variation in roughness has a clear and repeatable pattern due to the strong impact of the orientation of inclined surface to the laser origin. The effects of other factors such as exposure sequence of specimens, build height, and recoating process are less prominent and are difficult to isolate. A neural network regression model was built upon the large dataset in measured Ra values. The neural network model was applied to predict distribution of roughness within the build volume under hypothetical processing conditions. Connections between the predicted variation in roughness and underlying physical mechanisms are discussed. The present work has value for machine qualification and modifications which lead to the manufacturing of parts with better consistency in quality. The detailed variation observed in surface roughness can be used as a reference for designing experiments to optimise processing parameters in order to minimise the roughness of inclined surfaces

Towards data-driven additive manufacturing processes

Additive Manufacturing (AM), or 3D printing, is a potential game-changer in medical and aerospatial sectors, among others. AM enables rapid prototyping (allowing development/manufacturing of advanced components in a matter of days), weight reduction, mass customization, and on-demand manufacturing to reduce inventory costs. At present, though, AM has been showcased in many pilot studies but has not reached broad industrial application. Online monitoring and data-driven decision-making are needed to go beyond existing offline and manual approaches. We aim at advancing the state-of-the-art by introducing the STRATA framework. While providing APIs tailored to AM printing processes, STRATA leverages common processing paradigms such as stream processing and key-value stores, enabling both scalable analysis and portability. As we show with a real-world use case, STRATA can support online analysis with sub-second latency for custom data pipelines monitoring several processes in parallel

Numerical modelling of heat transfer and experimental validation in Powder-Bed Fusion with the Virtual Domain Approximation

Among metal additive manufacturing technologies, powder-bed fusion features very thin layers and rapid solidification rates, leading to long build jobs and a highly localized process. Many efforts are being devoted to accelerate simulation times for practical industrial applications. The new approach suggested here, the virtual domain approximation, is a physics-based rationale for spatial reduction of the domain in the thermal finite-element analysis at the part scale. Computational experiments address, among others, validation against a large physical experiment of 17.5 $\mathrm{[cm^3]}$ of deposited volume in 647 layers. For fast and automatic parameter estimation at such level of complexity, a high-performance computing framework is employed. It couples FEMPAR-AM, a specialized parallel finite-element software, with Dakota, for the parametric exploration. Compared to previous state-of-the-art, this formulation provides higher accuracy at the same computational cost. This sets the path to a fully virtualized model, considering an upwards-moving domain covering the last printed layers

DiffusionInst: Diffusion Model for Instance Segmentation

Diffusion frameworks have achieved comparable performance with previous state-of-the-art image generation models. Researchers are curious about its variants in discriminative tasks because of its powerful noise-to-image denoising pipeline. This paper proposes DiffusionInst, a novel framework that represents instances as instance-aware filters and formulates instance segmentation as a noise-to-filter denoising process. The model is trained to reverse the noisy groundtruth without any inductive bias from RPN. During inference, it takes a randomly generated filter as input and outputs mask in one-step or multi-step denoising. Extensive experimental results on COCO and LVIS show that DiffusionInst achieves competitive performance compared to existing instance segmentation models with various backbones, such as ResNet and Swin Transformers. We hope our work could serve as a strong baseline, which could inspire designing more efficient diffusion frameworks for challenging discriminative tasks. Our code is available in https://github.com/chenhaoxing/DiffusionInst

Grain refinement in additively manufactured ferritic stainless steel by in situ inoculation using pre-alloyed powder

For ferritic stainless steels, TiN has effectively been used as an inoculant to produce equiaxed grain structures in casting and welding. However, it is not established whether TiN would be an effective inoculant in additive manufacturing. In this study, the effectiveness of TiN as an inoculant in a ferritic stainless steel processed by laser powder-bed fusion is studied. An alloy without Ti is fabricated and compared to an alloy designed to form a high amount of TiN early during solidification. The work shows that the presence of TiN provides general grain refinement and that TiN-covered oxide particles are effective in enabling columnar-to-equiaxed transition in certain regions of the meltpool. The applied approach of pre-alloying powders with inoculant-forming elements offers a straightforward route to achieving fine, equiaxed grain structures in additively manufactured metallic materials. It also shows how oxygen present during the process can be utilized to nucleate effective inoculating phases

DiffUTE: Universal Text Editing Diffusion Model

Diffusion model based language-guided image editing has achieved great success recently. However, existing state-of-the-art diffusion models struggle with rendering correct text and text style during generation. To tackle this problem, we propose a universal self-supervised text editing diffusion model (DiffUTE), which aims to replace or modify words in the source image with another one while maintaining its realistic appearance. Specifically, we build our model on a diffusion model and carefully modify the network structure to enable the model for drawing multilingual characters with the help of glyph and position information. Moreover, we design a self-supervised learning framework to leverage large amounts of web data to improve the representation ability of the model. Experimental results show that our method achieves an impressive performance and enables controllable editing on in-the-wild images with high fidelity. Our code will be avaliable in \url{https://github.com/chenhaoxing/DiffUTE}

Helium bubble nucleation in Laser Powder Bed Fusion processed 304L stainless steel

The interest in application of Additive Manufacturing (AM) to nuclear industry stems not only from the benefits of design freedom and shortened lead time, but also from the possibility of enhancing the performance through microstructure control. One of the most important requirements for in-core structural material in nuclear power plants is helium resistance. The Laser Powder Bed Fusion (LPBF) processed 304L stainless steel possesses strong defect sinks such as high densities of dislocation-surrounded sub-grains and dispersed nano-inclusions. In this work the LPBF processed 304L in as-built and solution-annealed conditions along with a conventionally rolled counterpart were implanted with 350\ua0keV He+\ua0ion at 300\ua0\ub0C to 0.24 dpa (displacement per atom). Transmission Electron Microscopy (TEM) observations indicate significantly higher helium resistance of the as-built LPBF 304L compared to the other two samples. The sink strengths in the three samples are calculated based on the measurements of the microstructural features using simplified equations for the correlation between microstructural characteristics and helium tolerance. Based on the calculation, the cellular sub-grains and the dispersed nano-inclusions are the primary and secondary contributors to the helium resistance of LPBF 304L steel

Numerical modelling and experimental validation in Selective Laser Melting

In this work a finite-element framework for the numerical simulation of the heat transfer analysis of additive manufacturing processes by powder-bed technologies, such as Selective Laser Melting, is presented. These kind of technologies allow for a layer-by-layer metal deposition process to cost-effectively create, directly from a CAD model, complex functional parts such as turbine blades, fuel injectors, heat exchangers, medical implants, among others. The numerical model proposed accounts for different heat dissipation mechanisms through the surrounding environment and is supplemented by a finite-element activation strategy, based on the born-dead elements technique, to follow the growth of the geometry driven by the metal deposition process, in such a way that the same scanning pattern sent to the numerical control system of the AM machine is used. An experimental campaign has been carried out at the Monash Centre for Additive Manufacturing using an EOSINT-M280 machine where it was possible to fabricate different benchmark geometries, as well as to record the temperature measurements at different thermocouple locations. The experiment consisted in the simultaneous printing of two walls with a total deposition volume of 107 cm3 in 992 layers and about 33,500 s build time. A large number of numerical simulations have been carried out to calibrate the thermal FE framework in terms of the thermophysical properties of both solid and powder materials and suitable boundary conditions. Furthermore, the large size of the experiment motivated the investigation of two different model reduction strategies: exclusion of the powder-bed from the computational domain and simplified scanning strategies. All these methods are analysed in terms of accuracy, computational effort and suitable application