Post-processing Techniques Research Articles

Laser powder bed fused 304L steel shot-peened with various ceramic shot sizes: Surface/subsurface characteristics, tensile behavior, and fatigue behavior

Shot peening is an efficient post-processing technique for eliminating surface manufacturing defects, inducing compressive residual stress, and improving the mechanical properties of laser powder bed fused (L-PBFed) metallic materials. With a series of microstructural characterizations, this work comprehensively evaluated the influence of ceramic shot size on the surface characteristics, subsurface characteristics, and mechanical properties, especially fatigue performance, of L-PBF manufactured 304L austenitic stainless steel (SS). The mechanical testing process was in situ observed using an infrared thermal camera, and the tensile-fractured and fatigue-fractured microstructures were systematically examined, aiming to explore the life improvement mechanisms caused by different ceramic shot sizes. The results revealed that when compared to small-sized AZB 100 shots, the large-sized AZB 300 shots could result in higher surface roughness, larger thickness reduction, more apparent surface gradient structure, higher strength, and longer fatigue lifetimes. The strength improvement could be associated with the size reduction of L-PBF induced subsurface defects, the grain refinement at the surface layer, and the α'-martensitic transformation during tensile deformation. The life improvement could be primarily attributed to the subsurface defect strengthening, which resulted in the fatigue crack initiation transition from subsurface defects to surface defects. During the fatigue process, the energy dissipation originating from the core materials dominated the thermal equilibrium temperature, whereas the density of micro-defects (crystalline defects) at the surface layer dominated the crack tip temperature. These findings provide a basic understanding of how to advance the shot peening process for L-PBFed materials.

Efficient Post-Processing techniques for enhancing performance of PeLEDs

Metal halide perovskites have gained significant attention as a hopeful research area for developing perovskite light-emitting diodes. Achieving high-quality and cost-effective perovskite light-emitting diodes is crucial for their commercialization. This report focuses on an effective postprocessing approach that enhances the performance of perovskite emissive thin films without the need for vacuum equipment. By employing annealing and plasma treatment, the method successfully reduces grain size, and increases photoluminescence quantum yield, carrier recombination rate, external quantum efficiency (∼20.5 %), and luminance (∼68000 cd/m2) of the perovskite emitters (MAPbBr3). The proposed technique offers a time-efficient and economical solution for fabricating efficient commercial devices.

Simulation of Porosity Regrowth during Heat Treatment after Hot Isostatic Pressing in Titanium Components

Abstract Additive manufacturing (AM) is driven by design freedom, having fewer process constraints than traditional manufacturing processes. It requires careful process control and qualified parameters to create dense metal parts. However, defects in the form of cavities can be detected in as-built specimens by computed tomography. Post-processing techniques such as hot isostatic pressing (HIP) are applied to eliminate porosity, but regrowth of argon gas pores is observed after additional heat treatment. In this work, a mesoscopic heat treatment simulation of an argon-filled gas pore in titanium components is presented. A combination of HIP and high-temperature heat treatment for β-annealing is simulated. Calculated pore regrowth is qualitatively consistent with experimental observation from the literature. Simulation results support the hypothesis of argon not dissolving in the titanium matrix by assuming a constant amount of argon particles in the pore. Mesoscopic heat treatment simulations may be a part of a simulation-driven optimization of thermal post-processing to improve the quality and performance of AM components.

Cow key point detection in indoor housing conditions with a deep learning model

Lameness in dairy cattle is a costly and highly prevalent problem that affects all aspects of sustainable dairy production, including animal welfare. Automation of gait assessment would allow monitoring of locomotion in which the cows' walking patterns can be evaluated frequently and with limited labor. With the right interpretation algorithms, this could result in more timely detection of locomotion problems. This in turn would facilitate timely intervention and early treatment, which is crucial to reduce the effect of abnormal behavior and pain on animal welfare. Gait features of dairy cows can potentially be derived from key points that locate crucial anatomical points on a cow's body. The aim of this study is 2-fold: (1) to demonstrate automation of the detection of dairy cows' key points in a practical indoor setting with natural occlusions from gates and races, and (2) to propose the necessary steps to postprocess these key points to make them suitable for subsequent gait feature calculations. Both the automated detection of key points as well as the postprocessing of them are crucial prerequisites for camera-based automated locomotion monitoring in a real farm environment. Side-view video footage of 34 Holstein-Friesian dairy cows, captured when exiting the milking parlor, were used for model development. From these videos, 758 samples of 2 successive frames were extracted. A previously developed deep learning model called T-LEAP was trained to detect 17 key points on cows in our indoor farm environment with natural occlusions. To this end, the dataset of 758 samples was randomly split into a train (n = 22 cows; no. of samples = 388), validation (n = 7 cows; no. of samples = 108), and test dataset (n = 15 cows; no. of samples = 262). The performance of T-LEAP to automatically assign key points in our indoor situation was assessed using the average percentage of correctly detected key points using a threshold of 0.2 of the head length (PCKh0.2). The model's performance on the test set achieved a good result with PCKh0.2: 89% on all 17 key points together. Detecting key points on the back (n = 3 key points) of the cow had the poorest performance PCKh0.2: 59%. In addition to the indoor performance of the model, a more detailed study of the detection performance was conducted to formulate postprocessing steps necessary to use these key points for gait feature calculations and subsequent automated locomotion monitoring. This detailed study included the evaluation of the detection performance in multiple directions. This study revealed that the performance of the key points on a cows' back were the poorest in the horizontal direction. Based on this more in-depth study, we recommend the implementation of the outlined postprocessing techniques to address the following issues: (1) correcting camera distortion, (2) rectifying erroneous key point detection, and (3) establishing the necessary procedures for translating hoof key points into gait features.

Effects of post-processing techniques on the mechanical characterization of additively manufactured parts

Additive manufacturing (AM) has been used for manufacturing of components with complex geometries. AM also known as three-dimensional (3D) printing, is a topic of high growth in recent years, requiring improved mechanical performance of 3D-printed parts. In this context, much progress is being made to improve the reliability of 3D printing and mechanical behavior of AMed parts. Indeed, post-processing techniques can highly improve the microstructure, and consequently the mechanical behavior of the functional end-use products. Since accuracy and mechanical behavior of 3D-printed components are influenced by heat and surface treatment techniques, a better understanding of these effects can lead to improvement in performance of printed parts. In this context, the current study presents a review on the effects of post-processing on the mechanical characterization of 3D-printed parts. To this aim, 3D printing techniques on both metal and polymer materials are considered. Here, we have reviewed how post-processes can relieve residual stresses, provide smooth surfaces, restore a minimum material ductility, and prevent distortions. The summarized information can be utilized not only for comparison and the improvement of 3D-printed components, but also can be beneficial in the development of new post-processing techniques.

Multiple synchro-tuning chirplet transform

Time-frequency analysis (TFA) is an effective tool for characterizing non-stationary signals. Due to the limited time-frequency resolution capability, traditional TFA methods cannot achieve the optimal time-frequency representation (TFR). Based on the post-processing technique, a novel TFA method named multiple synchro-tuning chirplet transform (MSTCT) is proposed to obtain high-resolution and robust TFR for non-stationary signals. The MSTCT first estimates the spectral energy, instantaneous frequency (IF), and chirp rate (CR) of non-stationary signals with high time-frequency resolution, enabling precise energy localization on the time-frequency plane. Then, a multiple reassignment procedure is proposed to concentrate the blurry time-frequency energy and yield a better TFR. Moreover, benefiting from the multiple squeezing mechanism in the frequency axis, the MSTCT holds the potential to reconstruct the signal with high accuracy. Both the simulation results and the real-world experiment using the bat echolocation data demonstrate that the proposed MSTCT algorithm achieves superior performance in the time-frequency resolution, noise immunity, and reconstruction capability compared to the state-of-art linear TFA methods.

A local surface roughness mapping method for post-failure interpretation of brittle fracture propagation

Tensile fractures in brittle materials may exhibit striation features running along the surface that indicate the trajectory of fracture propagation. Because of the directional persistence of these features, directional roughness characterization methods can be used on subsampled fracture surface data to map localized roughness features that follow these striations. These maps can aid in the interpretation of fracture initiation sites and crack front positions with the use of post-processing visualization techniques such as vector maps and streamlines. Tensile fractures in rock and synthetic rock analogues are used to demonstrate the various fracture propagation patterns that emerge following this analysis. This mapping method and streamline analysis can be further used to analyze elevation profiles that follow fracture propagation along non-linear paths which may inform about the mechanisms that can associate surface features with the fracture process.

Surface grain refinement of casting A380 aluminum alloy by ultrasonic-assisted surface mechanical grinding treatment

A novel surface processing technique, ultrasonic-assisted surface mechnical grinding treatment (U-SMGT), was proposed and applied to modify the surface micriostructure of the casting aluminum alloy (A380). Comprehensive post-process chracterization techniques including 3D profilometry, optical microscope, scanning electric microscopy (SEM), and electron backscatter diffraction (EBSD) were carried out to investigate the surface morphology and microstructure of the processed region (PR). It was found that a surface layer of grain refinement with a thickness of about 50 µm was formed. In the as-received base material (BM), large clusters of dendritic eutectic phase with a size of approximately 20 µm were isolated by α-Al matrix. Nano-indentation tests showed that hardness of the eutectic and α-Al in the as-received BM was 2.15 GPa and 1.2 GPa respectively. After U-SMGT, the large dendritic eutectic phase was broken down into much fine particles with an average size of 0.57 µm. These fine particles were uniformly distributed into the α-Al matrix. Nano-indentation measurement exhibited a hardness of 2.27 GPa within the processed region. The overall high hardness in the processed region is a result of the pinning effect of uniformly distributed fine dendrites and grain refinement of Al-matrix.

Fictitious domain method: A stabilized post-processing technique for boundary-flux calculation using cut elements

This paper presents a technique for calculating boundary fluxes in the context of fictitious domain methods. We focus on a simple boundary-value problem and use Nitsche’s method stabilized with ghost penalty for the finite element formulation. To recover the flux, we derive a formulation directly from the boundary-value problem’s finite element formulation. Since the boundary may not align with the mesh, we compute the approximate flux using piecewise linear polynomials on cut elements. We then deduce the desired flux as the trace of the solution on the boundary. To ensure that the condition number of the resulting system matrix is independent of the boundary’s position on the mesh, we add a ghost penalty term. This term acts on the jumps of the gradients over interior facets belonging to elements intersected by the boundary. Two and three-dimensional numerical examples are provided, and show that the method is accurate and has optimal convergence regardless of the immersed boundary position.

Enhanced Signal Area Estimation in Radio-Communication Spectrograms Based on Morphological Image Processing

The concept of signal area (SA), defined as the rectangular time–frequency region in a spectrogram where a signal is detected, plays an important role in spectrum usage measurements. The need for signal area estimation (SAE) is justified by its role in the process of allocating white space spectrum to secondary users in dynamic spectrum access systems as well as in other interesting applications such as compliance verification and enforcement of spectrum regulations, signal interception, and network planning and optimisation. Existing SAE methods are far from perfect and therefore new solutions capable to provide more accurate estimations are thus required. In this study, a novel approach based on image processing techniques is explored. Concretely, the feasibility of using morphological operations (MOs) is explored to examine its usefulness in the context of SAE. By means of extensive simulations, the performance of different MOs (erosion, dilation, opening, and closing) in the context of SAE is investigated under various configurations, including different shapes and sizes of the structuring element (SE), when used both as standalone SAE methods and in combination with other SAE methods from the literature. Based on the obtained results, an MO-based SAE method is formulated based on the optimum MO and SE configuration for each specific SNR regime, which can improve substantially the performance of other proposed SAE methods when used as a pre- or postprocessing technique. Concretely, the accuracy improvement in terms of F1 score is up to 40% in the low-SNR regime while achieving a perfect accuracy of 100% in the high-SNR regime. This is achieved without having a noticeable impact on the associated computational cost (and even reducing it by up to 15% at high SNR). The performance improvement is thus particularly significant in the low-SNR regime, where most methods’ performances are limited, and as a result the proposed SAE approach can extend the operational SNR range of the existing SAE methods.

Qualitative and quantitative comparison of two semi-manual retinal vascular density analyzing methods on optical coherence tomography angiography images of healthy individuals

The aim of this study was to evaluate qualitative and quantitative differences in vascular density analysis of an established and a novel alternative for post-processing on optical coherence tomography angiography (OCTA) images in healthy individuals. OCTA examinations of 38 subjects were performed. After extracting the images, two semi-manual post-processing techniques, the already established Mexican hat filtering (MHF) and an alternative, the Shanbhag thresholding (ST) were applied. We assessed Vessel Density (VD), Skeleton Density (SkD) and Vessel Diameter Index (VDI). We analyzed the results in order to establish similarities or potentially relevant differences. Regarding SkD and VD, MHF generally gave higher values than ST. Simultaneously, mean values were also predominantly higher by MHF; however, standard deviations (SD) were higher by ST (range [mean ± SD]: 0.054 ± 0.038 to 0.134 ± 0.01 and 0.134 ± 0.095 to 0.362 ± 0.028 vs 0.012 ± 0.014 to 0.087 ± 0.03 and 0.039 ± 0.047 to 0.4 ± 0.095 for SkD and VD with MHF vs SkD and VD with ST, respectively). Values of VDI were considerably higher with ST than with MHF, while standard deviation was still significantly higher with ST (range [mean ± SD]: 2.459 ± 0.144 to 2.71 ± 0.084 and 2.983 ± 0.929 to 5.19 ± 1.064 for VDI with MHF and ST, respectively). The noise level reduction of the two methods were almost identical (noise levels: 65.8% with MHT and 65.24% with ST). Using MHF, the vascular network gets more fragmented by an average of 40% compared to ST. Both methods allow the segmentation of the vascular network and the examination of vascular density parameters, but they produce largely inconsistent results. To determine if these inconsistent results are clinically meaningful, and which method is more suitable for clinical use, our results provide further evidence that detailed understanding of the image analysis method is essential for reliable decision making for patients with retinal pathology. For longitudinal monitoring, use of the same image processing method is recommended.

Automatic classification of microplastics and natural organic matter mixtures using a deep learning model

Several preprocessing procedures are required for the classification of microplastics (MPs) in aquatic systems using spectroscopic analysis. Procedures such as oxidation, which are employed to remove natural organic matter (NOM) from MPs, can be time- and cost-intensive. Furthermore, the identification process is prone to errors due to the subjective judgment of the operators. Therefore, in this study, deep learning (DL) was applied to improve the classification accuracies for mixtures of microplastic and natural organic matter (MP-NOM). A convolutional neural network (CNN)-based DL model with a spatial attention mechanism was adopted to classify substances from their Raman spectra. Subsequently, the classification results were compared with those obtained using conventional Raman spectral library software to evaluate the applicability of the model. Additionally, the crucial spectral band for training the DL model was investigated by applying gradient-weighted class activation mapping (Grad-CAM) as a post-processing technique. The model achieved an accuracy of 99.54%, which is much higher than the 31.44% achieved by the Raman spectral library. The Grad-CAM approach confirmed that the DL model can effectively identify MPs based on their visually prominent peaks in the Raman spectra. Furthermore, by tracking distinctive spectra without relying solely on visually prominent peaks, we can accurately classify MPs with less prominent peaks, which are characterized by a high standard deviation of intensity. These findings demonstrate the potential for automated and objective classification of MPs without the need for NOM preprocessing, indicating a promising direction for future research in microplastic classification.

Surface Quality Optical Measurement of Part Inclined Planes Manufactured by FDM Technology

Fused Filament Deposition (FDM) additive manufacturing technology allows the generation of three-dimensional parts by overlapping layers of an extruded polymer. The staircase effect caused by overlapping is well known and makes the manufactured parts have poor surface quality. This problem has been focused on by numerous works trying to optimize the surface quality by studying the process technological parameters or part geometry. Also, post-processing techniques have been developed that improve this roughness, but which involve an increase in manufacturing time and cost. In this work, a methodology to measure the roughness based on laser confocal microscopy is proposed. To evaluate the methodology, an experimental study is carried out that relates the surface roughness of parts manufactured with FDM with the building orientation and the layer height. The main objective of this study is to compare two roughness measurement methodologies: a mechanical measurement with a conventional contact roughness meter, and an optical measurement with a confocal microscope. The contact roughness meter provides a direct value of the roughness of the part wall profile, while the confocal microscope provides an image of the three-dimensional surface of the part wall, which must be processed. The data from the confocal microscope are evaluated with the Internet-based software Surface Metrology Algorithm Testing System (SMATS) developed by the National Institute of Standards and Technology (NIST) of the USA. The SMATS will provide an average value of the roughness of the analyzed surface. The test results with both methodologies are very similar, with an average difference of 5%. These results show the influence of the printed plane inclination angle on the roughness, which is higher for low values of the angle. It can also be seen that this influence decreases for low-layer heights.

Material Extrusion of 3D Printed Ceramics Parts: Parameters, Structures and Challenges

AM technologies have been developed for different applications in sectors such as aeronautics, automotive, or healthcare. Additionally, the range of materials that can be 3D printed has increased considerably since these technologies began to be used in the 80s of the XXth century. Nowadays, it is possible to use both polymers and composite materials (ceramic-filled, metal-filled, etc.) in extrusion technologies, both with fused filament fabrication (FFF) and with direct ink writing (DIW). Unfortunately, even though 3D printing technologies offer much more freedom than conventional manufacturing technologies (molding, machining, etc.), before printing it is necessary to optimize the process, regarding the printing parameters that are recommended with each material. In addition, post-processing techniques are often required. This study aims to show not only the different parameters that are to be considered to optimize material extrusion 3D printing of ceramic materials, mainly regarding dimensional accuracy and surface finish, but also the types of structures that can be manufactured, as well as the challenges that are nowadays faced regarding 3D printing of ceramics.

Unconditional superconvergence analysis of an energy conservation scheme with Galerkin FEM for nonlinear Benjamin–Bona–Mahony equation

In this paper, a general energy conservation Crank–Nicolson (CN) fully-discrete finite element method (FEM) scheme is developed for solving the nonlinear Benjamin–Bona–Mahony (BBM) equation, and the unconditional supercloseness and superconvergence behavior are discussed with the nonconforming EQ1rot element. Different from the time–space splitting technique, the boundedness of numerical solution in the broken H1-norm is obtained directly based on the above energy conservation property, and the well-posedness of numerical solution is proved rigorously through the Brouwer fixed point theorem. Then, with the help of the special characters of this element and the interpolation post-processing technique, the unconditional superclose and superconvergence results are deduced strictly without any restriction between mesh size h and time step τ. Further, our analysis is extended to some other popular conforming and nonconforming finite elements. Finally, two numerical experiments are implemented to confirm the theoretical analysis. It is worth mentioning that this paper not only improve the previous results, but also can be regard as a framework for solving the BBM equation and some other PDEs with Galerkin FEMs.

Superconvergence of some nonconforming brick elements for the 3D Stokes problem

In this work, we study the superconvergence of three nonconforming brick elements for the 3D Stokes problem: a nonconforming rotated Q1 type element, the reduced version of this element and an edge-face-based element. The superclose interpolations are different from the canonical ones, and superclose analyses for the consistency error are also given, ensuring an O(h2)-order for both the velocity and the pressure for all these elements. By applying suitable postprocessing techniques, global superconvergence can be derived. Numerical examples are presented to confirm our theory.

Spectral CT imaging versus conventional CT post-processing technique in differentiating malignant and benign renal tumors.

Quantitative comparison of the diagnostic value of spectral CT imaging and conventional CT post-processing technique in differentiating malignant and benign renal tumors. A total of 209 patients with renal tumors who had undergone CT enhancement were assigned to three groups-clear cell renal cell carcinoma (ccRCC, n = 106), non-ccRCC (n = 60), and benign renal tumor (n = 43) groups. Parametric CT enhancement of each tumor based on spectral CT and conventional CT was performed using in-house software, and the iodine concentration, water content, slope, and density values among the three groups were compared. The receiver operating characteristic (ROC) curve analysis was performed to determine the optimum diagnostic thresholds, the area under the ROC curve (AUC), sensitivity, specificity, and accuracy of the above parameters. The iodine concentration, slope, and density values were higher in the ccRCCs group compared to the non-ccRCCs and benign renal tumor groups (p < 0.05). Moreover, the iodine concentration, slope, and density values were higher in benign renal tumors compared to non-ccRCCs (p < 0.05). According to the ROC curve analysis, iodine concentration presented the highest diagnostic efficacy in differentiating ccRCCs/non-ccRCCs from benign renal tumors. The pairwise comparisons of the ROC curves and the diagnostic efficacies revealed that ROI-based CT enhancement was worse than the spectral CT imaging analysis in terms of density (p < 0.05). Iodine concentration presented the highest diagnostic efficacy in differentiating ccRCCs/non-ccRCCs from benign renal tumors. 1. The iodine concentration, slope, and density values were higher for the ccRCCs compared to non-ccRCCs and benign renal tumors.2. Iodine concentration presented the highest diagnostic efficacy in differentiating ccRCCs/non-ccRCCs from benign renal tumors.3. Spectral CT imaging analysis performed better than conventional CT in differentiating malignant and benign renal tumors.

A post processing technique to achieve nanofinishing for functionality enhancement of Ti-6Al-4V femoral head fabricated by Laser Powder Bed Fusion

A post processing technique to achieve nanofinishing for functionality enhancement of Ti-6Al-4V femoral head fabricated by Laser Powder Bed Fusion

Dark-probe scanning near-field microscopy

Scanning near-field optical microscopy (SNOM) is a well-known powerful optical technique for visualization of surface nanostructures and fields far beyond the diffraction limit and thus indispensable in material- and nanoscience. While the SNOM resolution is theoretically unlimited, the SNOM performance is in practice constrained by the signal-to-background ratio, simply because of light scattering scaling down as the sixth power of a nanoparticle size and useful signals rapidly drowning in the background for very small objects. In modern instruments, this problem is usually ameliorated through advanced post-processing techniques. Here, we suggest using, instead or in parallel, a ‘dark’ SNOM probe designed to suppress the background light scattering, so that the scattering occurs only when the probe is very close to a nanoscopic object. We argue and demonstrate with simulations that the dark-probe SNOM imaging is much more sensitive to the presence of tiny nanoparticles or any other nanoscale features, allowing thereby for superior resolution and sensing capabilities that are invaluable for nano-optical characterization.

Impact of Transverse Acoustic Excitation and Air Swirl on a Confined Hollow Cone Sheet

Abstract This study investigates the effect of imposing symmetric transverse acoustic excitation and counter-rotating air swirl flow on a confined hollow cone spray sheet. We study the impact of these forcing conditions on two unstable modes—sheet flapping and sinuous wave growth modes—and their phasic relationship. We used the shadowgraphy technique, and only the sheet edges were analyzed using the method of snapshot proper orthogonal decomposition (POD). In this study, we used postprocessing techniques to estimate the nondimensional breakup length, phase averaged cone angle, and continuous wavelet transform (CWT) frequency. Results show that imposing acoustic excitation alone causes the sheet edges to flap, axisymmetrically, at the pressure antinode, while at the pressure node and base flow condition, the asymmetric sinuous wave mode is dominant. At the pressure intermediary, we find the possible dominance of both modes. While the dominant effect of air swirl alone was difficult to ascertain, results from CWT analysis show that POD mode 1 and mode 3 have very similar spectral content, with mode 3 showing a flapping mode. Finally, air swirl flow has a more dominant effect than acoustics in causing sheet breakup.