Comprehensive Analytical Modeling of Laser Powder-Bed/Fed Additive Manufacturing Processes and an Associated Magnetic Focusing Module

State-of-the-art metal additive manufacturing (AM), mainly laser powder-fed AM (LPF-AM) and laser powder-bed AM (LPB-AM), has been used to produce high-quality, complex-shaped, and end-user metallic parts. To achieve desirable dimensional, microstructural and mechanical features of as-built components through fast process optimization or feedback-control-based adaptive processing adjustment, high fidelity and calculation-efficient processing model is urgently needed. The thesis research has been motivated by the need for time-efficient process models of both LPF-AM and LPB-AM. To this end, comprehensive accelerated models for these processes have been built and experimentally verified. The comprehensive process model of LPF-AM was built by an innovative analytical approach. Firstly, a mathematical module that couples laser heat flux and powder mass flow was developed, while considering the attenuated laser intensity distribution and the heated powder spatial distribution. Correspondingly, a powder catchment module was built in terms of a three-dimensional (3D) melt pool shape and powder stream spatial distribution. Integrating these physical modules into the thermal modeling, a coupled heat and mass comprehensive model of the LPF-AM process was achieved. Experimental depositions of Inconel 625 proves the model’s high accuracy in predicting as-built deposits’ geometry (a maximum error of ~6.2% for clad width, ~7.8% for clad height) and powder catchment efficiency (maximum error of less than ~6.8%). It was found that the predicted real-time melt pool peak temperatures match well with the experimental results in Stainless Steel (SS) 316L deposition. The calculated micro-hardness has a maximum prediction error of ~16.2% compared with the measured results. The predicted microstructural evolutions show reasonable agreement with the experimental observations for both SS 316L and Inconel 625 depositions. Moreover, sensitivity analysis shows that the powder feed rate has the largest positive effect on the clad height. The time-efficient process model of LPB-AM was achieved by a novel analytical approach that couples the critical physics of the process, while considering the volume shrinkage and the melting regime. The proposed model can perform a time-efficient prediction of the localized-transient thermal field, melt pool temperature distribution, and multi-track overlapping dimension. The powder bed was treated as a homogeneous medium with effective thermophysical properties derived from the randomly packed rain model. In addition, different melting regimes of the LPB-AM process were considered in the built model. A 3D heat source model with variant penetration depths, together with the varying melting regimes, was utilized to solve the transient thermal field. Moreover, the density and top surface roughness of the final parts were empirically modeled using response surface regression under a Box-Behnken design. Subsequently, the mechanical properties of the part and the in-situ build rates were simultaneously optimized by combining the built analytical models and empirical models with employing a multi-objective genetic algorithm. Experimental results with SS 17-4PH show that the predicted melt pool dimensions have a high degree of accuracy under steady melting regimes, with a maximum of ~14% error for the width prediction and ~15% error for the depth calculation. Furthermore, an optimized parameter solution set was provided based on the built 3D Pareto fronts. The built models’ calculation time for the localized-transient characteristics for LPF-AM and LPB-AM are ~4 ms and ~1.2 ms, respectively. These findings confirm the great potential of the present research to be used for fast process optimization and in-situ process control. In addition, a new magnetic concentration approach designed with various configurations was explored. This approach is designed to focus the diverging metal particles in the gas-powder stream of LPF-AM, thereby improving powder catchment and deposition accuracy. It was shown that the proposed permanent-magnet-based configurations may not be suitable for concentrating submillimeter-sized particles. However, an additional development, a doublet-electromagnet-quadrupoles-based configuration with high frequency, may be capable of concentrating the non-ferrous metallic particles (e.g., aluminum particle) with a radius of r_p≥150 μm

Fast Growth of Thin MAPbI\u3csub\u3e3\u3c/sub\u3e Crystal Wafers on Aqueous Solution Surface for Efficient Lateral-Structure Perovskite Solar Cells

Solar-grade single or multiple crystalline wafers are needed in large quantities in the solar cell industry, and are generally formed by a top-down process from crystal ingots, which causes a significant waste of materials and energy during slicing, polishing, and other processing. Here, a bottom-up technique that allows the growth of wafer-size hybrid perovskite multiple crystals directly from aqueous solution is reported. Single-crystalline hybrid perovskite wafers with centimeter size are grown at the top surface of a perovskite precursor solution. As well as saving raw materials, this method provides unprecedented advantages such as easily tunable thickness and rapid growth of the crystals. These crystalline wafers show high crystallinity, broader light absorption, and a long carrier recombination lifetime, comparable with those of bulk single crystals. Lateral-structure perovskite solar cells made of these crystals demonstrate a record power conversion efficiency of 5.9%. Includes supplementary materials

Quasi-Continuous Wave Pulsed Laser Welding of Copper Lap Joints Using Spatial Beam Oscillation

Laser beam welding of copper (Cu) using near-infrared radiation is extremely challenging due to its high thermal conductivity and large laser reflectivity. In the present study, the challenges and benefits of using spatial beam oscillation during quasi-continuous wave (QCW) pulsed laser beam welding of 0.4 mm Cu to 1 mm Cu in lap joint configuration are presented. This work demonstrates how laser beam oscillating parameters can be used to control the laser weld quality and laser weld dimensions for Cu-Cu joining. Compared to a non-oscillated laser beam, welds made using laser beam oscillation showed fewer spatters, porosities, and better surface quality. Four levels of oscillating amplitudes (0.2 mm, 0.4 mm, 0.6 mm, and 0.8 mm) and oscillating frequencies (100 Hz, 200 Hz, 300 Hz, and 400 Hz) were compared to reveal the effect of beam oscillation parameters. The weld width was mainly controlled by oscillating amplitude, while weld penetration was affected by both oscillating amplitude and frequency. As the oscillating amplitude increased, the weld width increased while the weld penetration decreased. Increasing the oscillating frequency reduced the weld penetration but had a negligible effect on the weld width. The maximum tensile force of approximately 1944 N was achieved for the joint with a high width-to-depth ratio with an oscillating amplitude of 0.8 mm and an oscillating frequency of 200 Hz

Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named LFH that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterwards, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with eleven state-of-the-art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experiment results demonstrate a significant performance gain (average 12.5% in node classification and 13.3% in link prediction) compared with recent state-of-the-art methods

Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning

Sleep stage classification is crucial for detecting patients' health conditions. Existing models, which mainly use Convolutional Neural Networks (CNN) for modelling Euclidean data and Graph Convolution Networks (GNN) for modelling non-Euclidean data, are unable to consider the heterogeneity and interactivity of multimodal data as well as the spatial-temporal correlation simultaneously, which hinders a further improvement of classification performance. In this paper, we propose a dynamic learning framework STHL, which introduces hypergraph to encode spatial-temporal data for sleep stage classification. Hypergraphs can construct multi-modal/multi-type data instead of using simple pairwise between two subjects. STHL creates spatial and temporal hyperedges separately to build node correlations, then it conducts type-specific hypergraph learning process to encode the attributes into the embedding space. Extensive experiments show that our proposed STHL outperforms the state-of-the-art models in sleep stage classification tasks

Porosity in wire-arc directed energy deposition of aluminum Formation mechanisms, influencing factors and inhibition strategies

Wire-arc directed energy deposition (DED) offers advantages such as high forming efficiency and the ability to create parts without potential constraints on size. It possesses unique advantages in the high-efficiency production of large or ultra-large alloy metal components, for example aluminum. However, the issue of porosity in wire-arc DED aluminum alloys has been a subject of widespread discussion. Porosity defects can induce stress concentration and site for crack formation and propagation. This deterioration results in diminished tensile strength and fatigue resistance, limiting the potential applications of wire-arc DED in aluminum alloy builds. To this end, for the first time, this review offers a thorough examination of prevalent porosity imperfections in wire-arc DED aluminum alloys, including gas pores, shrinkage cavities and porosity arising from the volatilization of elements. Particular emphasis is placed on elucidating the formation mechanisms and spatial distribution of hydrogen pores, which constitute the primary pore defects in wire-arc DED aluminum alloys. Moreover, the research scrutinizes the influence of various wire-arc DED techniques, arc modes, process parameters, and shielding gas environments on porosity formation. The inhibition strategies of porosity defects in wire-arc DED aluminum alloys, including laser-arc hybrid additive manufacturing, ultrasonic vibration assistance, external magnetic field, inter-layer rolling, inter-layer friction stir processing, ultrasonic peening treatment, laser shock peening, and hot isostatic pressing, are further summarized. Ultimately, this work anticipates the future trajectory of wire-arc DED aluminum alloys, offering valuable guidance for the fabrication of high-quality wire-arc DED aluminum alloy intricate components

Keyhole fluctuation and pore formation mechanisms during laser powder bed fusion additive manufacturing

Keyhole porosity is a key concern in laser powder-bed fusion (LPBF), potentially impacting component fatigue life. However, some keyhole porosity formation mechanisms, e.g., keyhole fluctuation, collapse and bubble growth and shrinkage, remain unclear. Using synchrotron X-ray imaging we reveal keyhole and bubble behaviour, quantifying their formation dynamics. The findings support the hypotheses that: (i) keyhole porosity can initiate not only in unstable, but also in the transition keyhole regimes created by high laser power-velocity conditions, causing fast radial keyhole fluctuations (2.5–10 kHz); (ii) transition regime collapse tends to occur part way up the rear-wall; and (iii) immediately after keyhole collapse, bubbles undergo rapid growth due to pressure equilibration, then shrink due to metal-vapour condensation. Concurrent with condensation, hydrogen diffusion into the bubble slows the shrinkage and stabilises the bubble size. The keyhole fluctuation and bubble evolution mechanisms revealed here may guide the development of control systems for minimising porosity

Excess charge-carrier induced instability of hybrid perovskites

Identifying the origin of intrinsic instability for organic–inorganic halide perovskites (OIHPs) is crucial for their application in electronic devices, including solar cells, photodetectors, radiation detectors, and light-emitting diodes, as their efficiencies or sensitivities have already been demonstrated to be competitive with commercial available devices. Here we show that free charges in OIHPs, whether generated by incident light or by current-injection from electrodes, can reduce their stability, while efficient charge extraction effectively stabilizes the perovskite materials. The excess of both holes and electrons reduce the activation energy for ion migration within OIHPs, accelerating the degradation of OIHPs, while the excess holes and electrons facilitate the migration of cations or anions, respectively. OIHP solar cells capable of efficient charge-carrier extraction show improved light stability under regular operation conditions compared to an open-circuit condition where the photo-generated charges are confined in the perovskite layers

Thermoelectric magnetohydrodynamic control of melt pool flow during laser directed energy deposition additive manufacturing

Melt flow is critical to build quality during additive manufacturing (AM). When an external magnetic field is applied, it causes forces that alter the flow through the thermoelectric magnetohydrodynamic (TEMHD) effect, potentially altering the final microstructure. However, the extent of TEMHD forces and their underlying mechanisms, remain unclear. We trace the flow of tungsten particles using in situ high-speed synchrotron X-ray radiography and ex situ tomography to reveal the structure of TEMHD-induced flow during directed energy deposition AM (DED-AM). When no magnetic field is imposed, Marangoni convection dominates the flow, leading to a relatively even particle distribution. With a magnetic field parallel to the scan direction, TEMHD flow is induced, circulating in the cross-sectional plane, causing particle segregation to the bottom and side of the pool. Further, a downward magnetic field causes horizontal circulation, segregating particles to the other side. Our results demonstrate that TEMHD can disrupt melt pool flow during DED-AM

Valley-polarized Exitonic Mott Insulator in WS2/WSe2 Moir\'e Superlattice

Strongly enhanced electron-electron interaction in semiconducting moir\'e superlattices formed by transition metal dichalcogenides (TMDCs) heterobilayers has led to a plethora of intriguing fermionic correlated states. Meanwhile, interlayer excitons in a type-II aligned TMDC heterobilayer moir\'e superlattice, with electrons and holes separated in different layers, inherit this enhanced interaction and strongly interact with each other, promising for realizing tunable correlated bosonic quasiparticles with valley degree of freedom. We employ photoluminescence spectroscopy to investigate the strong repulsion between interlayer excitons and correlated electrons in a WS2/WSe2 moir\'e superlattice and combine with theoretical calculations to reveal the spatial extent of interlayer excitons and the band hierarchy of correlated states. We further find that an excitonic Mott insulator state emerges when one interlayer exciton occupies one moir\'e cell, evidenced by emerging photoluminescence peaks under increased optical excitation power. Double occupancy of excitons in one unit cell requires overcoming the energy cost of exciton-exciton repulsion of about 30-40 meV, depending on the stacking configuration of the WS2/WSe2 heterobilayer. Further, the valley polarization of the excitonic Mott insulator state is enhanced by nearly one order of magnitude. Our study demonstrates the WS2/WSe2 moir\'e superlattice as a promising platform for engineering and exploring new correlated states of fermion, bosons, and a mixture of both