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This work presents a physics-based analytical modeling methodology for the prediction of the lack-of-fusion porosity in powder bed metal additive manufacturing (PBMAM) considering the molten pool geometry, powder size variation, and packing. The presented model has promising short computational time without resorting to the finite element method or any iteration-based simulations. The temperature profiles were calculated using a closed-form temperature solution. Multiple transverse sectional areas of the molten pool geometry were plotted on a cross-sectional area of the part based on hatch space and layer thickness to calculate the lack-of-fusion area. The powder bed porosity was calculated using advancing front approach with consideration of powder statistical distribution and powder packing. The part porosity was converted from the calculated lack-of-fusion area by multiplying the calculated powder bed porosity. Acceptable agreements were observed upon validation against experimental measurements under various process conditions in PBMAM of Ti6Al4V. The computational time was recorded less than 26 s for the porosity calculation of five consecutive layers. The presented model has high prediction accuracy and high computational efficiency, which allow the porosity calculation for large-scale parts and process parameters planning through inverse analysis, and thus improves the usefulness of analytical modeling in real applications.

Temperature distribution gradient in metal powder bed additive manufacturing (MPBAM) directly controls the mechanical properties and dimensional accuracy of the build part. Experimental approach and numerical modeling approach for temperature in MPBAM are limited by the restricted accessibility and high computational cost, respectively. Analytical models were reported with high computational efficiency, but the developed models employed a moving coordinate and semi-infinite medium assumption, which neglected the part dimensions, and thus reduced their usefulness in real applications. This paper investigates the in-process temperature in MPBAM through analytical modeling using a stationary coordinate with an origin at the part boundary (absolute coordinate). Analytical solutions are developed for temperature prediction of single-track scan and multi-track scans considering scanning strategy. Inconel 625 is chosen to test the proposed model. Laser power absorption is inversely identified with the prediction of molten pool dimensions. Latent heat is considered using the heat integration method. The molten pool evolution is investigated with respect to scanning time. The stabilized temperatures in the single-track scan and bidirectional scans are predicted under various process conditions. Close agreements are observed upon validation to the experimental values in the literature. Furthermore, a positive relationship between molten pool dimensions and powder packing porosity was observed through sensitivity analysis. With benefits of the absolute coordinate, and high computational efficiency, the presented model can predict the temperature for a dimensional part during MPBAM, which can be used to further investigate residual stress and distortion in real applications.

Elevated temperature in the machining process is detrimental to cutting tools—a result of the effect of thermal softening and material diffusion. Material diffusion also deteriorates the quality of the machined part. Measuring or predicting machining temperatures is important for the optimization of the machining process, but experimental temperature measurement is difficult and inconvenient because of the complex contact phenomena between tools and workpieces, and because of restricted accessibility during the machining process. This paper presents an original analytical model for fast prediction of machining temperatures at two deformation zones in orthogonal cutting, namely the primary shear zone and the tool–chip interface. Temperatures were predicted based on a correlation between force and temperature using the mechanics of the cutting process and material constitutive relation. Minimization of the differences between calculated material flow stresses using a mechanics model and a constitutive model yielded an estimate of machining temperatures. Experimental forces, cutting condition parameters, and constitutive model constants were inputs, while machining forces were easily measurable by a piezoelectric dynamometer. Machining temperatures of AISI 1045 steel were predicted under various cutting conditions to demonstrate the predictive capability of each presented model. Close agreements were observed by verifying them against documented values in the literature. The influence of model inputs and computational efficiency were further investigated. The presented model has high computational efficiency that allows real-time prediction and low experimental complexity, considering the easily measurable input variables.

This paper presents a novel low-light image enhancement (LLIE) method based on magnitude control (MaCo). Our method establishes a relationship between the low-light image and illumination, using pixel intensity and image brightness. Exploiting this relationship, MaCo enhances pixels with varying magnitudes, achieving pixel-wise LLIE. This yields high-quality enhanced images without local overexposure. We also introduce a set of carefully formulated unsupervised loss functions to enable training using only low-light images. Concretely, our method first trains a lightweight deep network, Low-res Coefficient Estimation Network (LCE-Net), to estimate the feature map in low-resolution space. Next, the High-res Illumination Estimation Module (HIE Module) is proposed to perform bilateral-grid-based upsampling for obtaining the high-res illumination. The illumination is finally utilized to light up the low-light image, yielding the enhanced image. MaCo is efficient and consumes fewer resources, as most computations occur in low resolution and the network is lightweight. Current LLIE datasets are mostly synthesized or generated through altered camera settings. These datasets are often limited in accurately representing real-world situations. To address this problem, we create a new dataset, IOLD, containing 572 images captured under real low- and normal-light conditions. In particular, the potential advantage of MaCo for face detection in the dark is also discussed. Extensive qualitative and quantitative experiments demonstrate that our method performs favorably against state-of-the-art methods in terms of effectiveness and efficiency. The IOLD dataset will be made publicly available at https://drive.google.com/drive/folders/1VAkuj9gheZ4aPEhBZ5MszHF6zpbrE_9_?usp=sharing .

Because of the three-dimensional (3D) imaging scene’s sparsity, compressed sensing (CS) algorithms can be used for linear array synthetic aperture radar (LASAR) 3D sparse imaging. CS algorithms usually achieve high-quality sparse imaging at the expense of computational efficiency. To solve this problem, a fast Bayesian compressed sensing algorithm via relevance vector machine (FBCS–RVM) is proposed in this paper. The proposed method calculates the maximum marginal likelihood function under the framework of the RVM to obtain the optimal hyper-parameters; the scattering units corresponding to the non-zero optimal hyper-parameters are extracted as the target-areas in the imaging scene. Then, based on the target-areas, we simplify the measurement matrix and conduct sparse imaging. In addition, under low signal to noise ratio (SNR), low sampling rate, or high sparsity, the target-areas cannot always be extracted accurately, which probably contain several elements whose scattering coefficients are too small and closer to 0 compared to other elements. Those elements probably make the diagonal matrix singular and irreversible; the scattering coefficients cannot be estimated correctly. To solve this problem, the inverse matrix of the singular matrix is replaced with the generalized inverse matrix obtained by the truncated singular value decomposition (TSVD) algorithm to estimate the scattering coefficients correctly. Based on the rank of the singular matrix, those elements with small scattering coefficients are extracted and eliminated to obtain more accurate target-areas. Both simulation and experimental results show that the proposed method can improve the computational efficiency and imaging quality of LASAR 3D imaging compared with the state-of-the-art CS-based methods.

This research proposes an analytical method for the prediction of molten pool size in laser-based powder bed fusion (LPBF) additive manufacturing with the consideration of process conditions-dependent absorptivity. Under different process conditions, the melting modes in LPBF are different, which induces the difference in laser absorptivity. An empirical model of absorptivity was used to calculate the laser absorptivity under various process conditions. An analytical point-moving heat source model was employed to calculate the temperature distribution of the build-in LPBF, with absorptivity, material properties, and process conditions as inputs. The molten pool width, length, and depth were determined by comparing the predicted temperature profile with the melting temperature of the material. To validate the proposed method, the predicted molten pool width, and depth of Ti6Al4V were compared with the reported experimental measurements under various process conditions. The predicted molten pool widths were very close to the measured results, and the predictions of molten pool depth were also acceptable. The computational time of the presented model is less than 200s, which shows better computational efficiency than most methods based on numerical iterations, such as the finite element method (FEM). The sensitivity of molten pool width and depth to normalized enthalpy w also discussed. The presented analytical method can be a potential tool for the research of molten pool size and related defects in LPBF.

This paper presents a closed-form solution for the temperature prediction in selective laser melting (SLM). This solution is developed for the three-dimensional temperature prediction with consideration of heat input from a moving laser heat source, and heat loss from convection and radiation on the part top boundary. The consideration of heat transfer boundary condition and latent heat in the closed-form solution leads to an improvement on the understanding of thermal development and prediction accuracy in SLM, and thus the usefulness of the analytical model in the temperature prediction in real applications. A moving point heat source solution is used to calculate the temperature rise due to the heat input. A heat sink solution is used to calculate the temperature drop due to heat loss from convection and radiation on the part boundary. The heat sink solution is modified from a heat source solution with equivalent power due to heat loss from convection and radiation, and zero-moving velocity. The temperature solution is then constructed from the superposition of the linear heat source solution and linear heat sink solution. Latent heat is considered using a heat integration method. Ti-6Al-4V is chosen to test the presented model with the assumption of isotropic and homogeneous material. The predicted molten pool dimensions are compared to the documented values from the finite element method and experiments in the literature. The presented model has improved prediction accuracy and significantly higher computational efficiency compared to the finite element model.

This work investigates the machining temperatures of ultra-fine-grained titanium (UFG Ti), prepared by equal channel angular extrusion, through analytical modeling. UFG Ti has great usefulness in biomedical applications because of its high mechanical strength, sufficient manufacturability, and high biocompatibility. The temperatures were predicted using a physics-based predictive model based on material constitutive relation and mechanics of the orthogonal cutting process. The minimization between the stress calculated using Johnson–Cook constitutive model and the same stress calculated using mechanics model yields the estimation of machining temperatures at two deformation zones. Good agreements are observed upon validation to the values reported in the literature. The machinability of UFG Ti is investigated by comparing its machining temperature to that of Ti–6Al–4V alloy under the same cutting conditions. Significantly lower temperatures are observed in machining UFG Ti. The computational efficiency of the presented model is investigated by comparing its average computational time (~ 0.5 s) to that of a widely used modified chip formation model (8900 s) with comparable prediction accuracy. This work extends the applicability of the presented temperature model to a broader class of materials, specifically ultra-fine-grained metals. The high computational efficiency allows the in situ temperature prediction and optimization of temperature condition with process parameters planning.

Energy efficiency is motivating the search for new high-temperature (high-T) metals. Some new body-centered-cubic (BCC) random multicomponent “high-entropy alloys (HEAs)” based on refractory elements (Cr-Mo-Nb-Ta-V-W-Hf-Ti-Zr) possess exceptional strengths at high temperatures but the physical origins of this outstanding behavior are not known. Here we show, using integrated in-situ neutron-diffraction (ND), high-resolution transmission electron microscopy (HRTEM), and recent theory, that the high strength and strength retention of a NbTaTiV alloy and a high-strength/low-density CrMoNbV alloy are attributable to edge dislocations. This finding is surprising because plastic flows in BCC elemental metals and dilute alloys are generally controlled by screw dislocations. We use the insight and theory to perform a computationally-guided search over 10 7 BCC HEAs and identify over 10 6 possible ultra-strong high-T alloy compositions for future exploration. The strength in BCC high-entropy alloys is associated with the type of mobile dislocations. Here the authors demonstrate by means of an ample array of experimental techniques that edge dislocations can control the strength of BCC high-entropy alloys.

In this work, we extend the energy stable discontinuous Galerkin (DG) schemes proposed in Bokil et al. (J Comput Phys 350:420–452, 2017), for the time domain Maxwell’s equations augmented with a class of nonlinear constitutive polarization laws, to higher dimensions. The nontrivial discrete temporal treatment of the nonlinearity in the ordinary differential equations that encode the Kerr and Raman effects (Bokil et al. 2017), is first generalized to higher spatial dimensions. To further improve the computational efficiency in dealing with the nonlinearity, we apply nodal DG methods in space. Energy stability is proved for the semi-discrete in time and in space schemes as well as for the fully-discrete schemes. Under some assumptions on the strength of nonlinearity, error estimates are established for the semi-discrete in space methods, and, in particular, optimal accuracy is achieved for the methods on Cartesian meshes with Q k -type elements and alternating fluxes. Attention is paid to the role of the nodal form of the DG discretizations in the analysis. We numerically validate the accuracy, energy stability, and computational efficiency of the proposed schemes using manufactured solutions. We further illustrate the performance of the methods through physically relevant experiments involving spatial soliton propagation and airhole scattering in realistic glasses.