Laser powder bed fusion is among the most promising methods for fabricating metal parts with complex geometries; however, ensuring consistent melting remains challenging, as highly dynamic heat accumulation from previous layers and melt tracks alters the pre-melting surface temperature for the subsequent ones. Such a phenomenon will result in inconsistent melt pool behavior, leading to shape inaccuracies and porosities. Existing works have attempted to address this issue by regulating printing parameters, either via feedforward modeling or sensor-based feedback control. Yet, a practical framework for stabilizing multiscale heat variation remains missing. This work combines two control approaches by linking finite element modeling-based heuristic tuning with sensor-based feedback laser power control. Specifically, a photoresistor-based PD laser power control method is used to maintain consistent melting within each layer, and the control target for each layer is determined via feedforward tuning and calibration. The method is validated through a proof-of-concept case study with parts featuring geometries that exhibit high global and local heat-accumulation risks. The maximum interlayer temperature has been reduced from 182 °C to 158 °C with combined feedback and feedforward control. Comprehensive inspections demonstrated a significant improvement in geometric accuracy and porosity reduction. For fine features such as thin-wall structures, the thickness error is reduced from 0.36 mm to 0.08 mm. Porosity count improved from 1.36 counts/mm2 to almost 0 in the sub-inspection window at the heat-accumulated regions.

Additive Manufacturing is an innovative technology that fabricates parts layer by layer. However, in Laser Powder Bed Fusion (LPBF), printed metal parts often exhibit residual stresses, deformations, and other defects due to non-uniform temperature distribution during the printing process. To mitigate these issues, an optimized scan sequence within each layer can improve thermal uniformity. Traditional optimization methods, which rely on domain knowledge and employ trial-and-error or heuristic approaches, often fail to achieve optimal solutions due to the complex nature of the problem. One major challenge in improving scan strategies lies in the vast search space required to optimize the scan sequence for individual scan tracks within each layer, making it difficult to identify the best solution. To overcome this challenge, this work proposes an innovative scan strategy, Reinforced Scan, that leverages reinforcement learning to intelligently determine the optimal scan sequence. The method introduces a novel reward function that accounts not only for temperature variance but also for the spatial uniformity of the temperature field. By structuring the optimization problem into multiple hierarchical levels, the approach significantly reduces computational demand and enhances the manageability of the optimization process. The effectiveness of the proposed Reinforced Scan is validated through Netfabb™ Local Simulation and real-world laser scanning experiments on a Ti-6Al-4V thin plate. Its performance is compared against conventional heuristic scan sequences. Both simulation and experimental results demonstrate that Reinforced Scan achieves superior outcomes, notably reducing residual stress compared to traditional methods.

Additive manufacturing (AM) is a transformative technology that enables the fabrication of complex geometries layer by layer. However, metal parts produced via AM processes such as laser powder bed fusion (LPBF) are prone to various defects, including porosity and deformation. These defects often result from suboptimal printing parameter settings. Traditional approaches typically aim to reduce defects by optimizing a fixed set of parameters for the entire part. However, such methods do not account for layer-wise variations in printing conditions caused by changes in geometry, heat transfer, and re-heating effects. While optimizing parameters for each layer could improve part quality, it would require an impractically large number of experiments for parts composed of hundreds or thousands of layers. To address this challenge, this paper proposes a novel approach in which printing parameters along the build direction are modeled using a parameter function. This function is constructed as a weighted combination of several basis functions, substantially reducing the number of decision variables. The weights are optimized using a response surface method to minimize a defect index, which aggregates multiple quality metrics, including residual stress, displacement, lack of fusion, and overheating. The proposed method significantly reduces porosity in an inverted-cone geometry compared to conventional approaches, as demonstrated through finite element analysis simulations and physical experiments that optimize laser power. This research presents a robust framework for efficiently optimizing layer-wise printing parameters to minimize process-induced defects in LPBF.

Engineering design problems typically require optimizing a quality measure by finding the right combination of controllable input parameters. In additive manufacturing (AM), the output characteristics of the process can often be non-stationary functions of the process parameters. Bayesian Optimization (BO) is a methodology to optimize such “black-box” functions, i.e., the input-output relationship is unknown and expensive to compute. Optimization tasks involving “black-box” functions widely use BO with Gaussian Process (GP) regression surrogate model. Using GPs with standard kernels is insufficient for modeling non-stationary functions, while GPs with non-stationary kernels are typically over-parameterized. On the other hand, a Deep Gaussian Process (DGP) can overcome GPs’ shortcomings by considering a composition of multiple GPs. Inference in a DGP is challenging due to its structure resulting in a non Gaussian posterior, and using DGP as a surrogate model for BO is not straightforward. Stochastic Imputation (SI) based inference is promising in speed and accuracy for BO. This work proposes a bootstrap aggregation based procedure to effectively utilize the SI-based inference for BO with a DGP surrogate model. The proposed BO algorithm DGP-SI-BO is faster and empirically better than the state-of-the-art BO method in optimizing nonstationary functions. Several analytical test functions and a case study in metal additive manufacturing simulation demonstrate the applicability of the proposed method.

AbstractMicrostructures of additively manufactured metal parts are crucial since they determine the mechanical properties. The evolution of the microstructures during layer-wise printing is complex due to continuous re-melting and reheating effects. The current approach to studying this phenomenon relies on time-consuming numerical models such as finite element analysis due to the lack of effective sub-surface temperature measurement techniques. Attributed to the miniature footprint, chirped-fiber Bragg grating, a unique type of fiber optical sensor, has great potential to achieve this goal. However, using the traditional demodulation methods, its spatial resolution is limited to the millimeter level. In addition, embedding it during laser additive manufacturing is challenging since the sensor is fragile. This paper implements a machine learning-assisted approach to demodulate the optical signal to thermal distribution and significantly improve spatial resolution to 28.8 µm from the original millimeter level. A sensor embedding technique is also developed to minimize damage to the sensor and part while ensuring close contact. The case study demonstrates the excellent performance of the proposed sensor in measuring sharp thermal gradients and fast cooling rates during the laser powder bed fusion. The developed sensor has a promising potential to study the fundamental physics of metal additive manufacturing processes.

Laser-induced-forward-transfer (LIFT) bioprinting technology has been viewed as a regenerative medicine technology because of its high printing quality and good cell viability. To stabilize the jet to achieve high-quality printing, an energy-absorbing layer (EAL) can be introduced. In this study, three materials (graphene, gelatin, and gold) were utilized as the EAL. The effect of each EAL on the jet generation process was investigated. Besides, the effect of graphene EAL thickness was addressed for various experimental conditions. The jet generation process using sodium alginate solutions with different concentrations (1 and 2 wt. %) was also discussed to investigate the effect of viscosity. The time sequence images of the formed jets utilizing three EALs showed that both graphene EAL and gelatin EAL can promote the formation of jet flow. For the gold EAL, no jet flow was observed. This study provides experimental verifications that the interaction between laser and EAL materials can result in different jets due to various dominant interaction mechanisms. For example, strong absorption in the infrared range for the graphene EAL, strong scattering loss for the gelatin EAL, and strong absorption in the ultraviolet range but weak absorption in the infrared for the gold EAL. We also observed the holes left on the EAL after the printing was completed. The thermal effect is dominant to create regular and round shape holes for the graphene EAL, but it changes to the mechanical effect for the gold EAL because of the existence of irregular and unorganized holes. In addition, we identified the existence of an input laser energy threshold value for a certain thickness graphene EAL. More laser energy is required to break down thicker graphene EALs, which will result in a higher initial jet velocity. Furthermore, we explored the effect of sodium alginate (SA) solution's viscosity on the generated jet. We found that a high-viscosity SA solution can result in a low initial jet velocity, a short jet, and small droplets on the receiving substrate. The findings from this study help determine the mechanisms of EAL-laser interaction with different EAL materials in the LIFT process. This work aims to facilitate the development of new EAL and bioink to achieve stable jet formation and high printing quality in future LIFT bioprinting.

Abstract Additive manufacturing (AM) is a revolutionary technology that fabricates parts layerwise and provides many advantages. This article discusses polymer AM processes such as material extrusion, vat photopolymerization (VPP), powder-bed fusion (PBF), binder jetting (BJ), material jetting (MJ), and sheet lamination (SL). It presents the benefits of online monitoring and process control for polymer AM. It also introduces the respective monitoring devices used, including the models and algorithms designed for polymer AM online monitoring and control.

Abstract Additive manufacturing (AM) is a revolutionary technology that fabricates parts layerwise and provides many advantages. This article discusses polymer AM processes such as material extrusion, vat photopolymerization (VPP), powder-bed fusion (PBF), binder jetting (BJ), material jetting (MJ), and sheet lamination (SL). It presents the benefits of online monitoring and process control for polymer AM. It also introduces the respective monitoring devices used, including the models and algorithms designed for polymer AM online monitoring and control.

Additive manufacturing (AM) is one of the most effective ways to fabricate parts with complex geometries using various materials. However, AM also suffers from printing quality issues resulting from the defects such as over-melt, lack of fusion, swelling, etc. One of the root causes of those issues is that the process parameters remain constant during the entire printing process, regardless of the dynamic heat accumulation and various printing feature sizes. For instance, raster is the most common scanning strategy in the laser powder bed fusion (L-PBF) process. The length of the raster line varies depending on the printing feature size. When scanning small features, the raster line is short, resulting in heat accumulations and over-melt. These variabilities may cause severe quality issues and thus suggest adaptive process parameters be applied. Aiming to address this challenge, this study develops a closed-loop control system to regulate the laser power based on melt pool thermal emission to avoid over-melt, balling, and high surface roughness. The control target is determined by correlating the printing quality (dimensional printing error in this study) with the thermal emission through thin-line printing trials using variable power. A high-speed thermal sensor and controller are designed, tuned, and implemented on a newly developed L-PBF testbed. The system successfully maintains a low dimensional error by regulating the laser power at 2 kHz. A significant improvement in printing quality was achieved, as validated by both microscopic imaging and 3D scanning.

Cerebral ischemia-reperfusion injury (CIRI) is a complex pathological condition with high mortality. In particular, reperfusion can stimulate overproduction of reactive oxygen species (ROS) and activation of inflammation, causing severe secondary injuries to the brain. Despite tremendous efforts, it remains urgent to rationally design antioxidative agents with straightforward and efficient ROS scavenging capability. Herein, a potent antioxidative agent was explored based on iridium oxide nano-agglomerates (Tf-IrO2 NAs) via the facile transferrin (Tf)-templated biomineralization approach, and innovatively applied to treat CIRI. Containing some small-size IrO2 aggregates, these NAs possess intrinsic hydroxyl radicals (•OH)-scavenging ability and multifarious enzyme activities, such as catalase (CAT), superoxide dismutase (SOD) and glutathione peroxidase (GPx). Moreover, they also showed improved blood-brain barrier (BBB) penetration and enhanced accumulation in the ischemic brain via Tf receptor-mediated transcytosis. Therefore, Tf-IrO2 NAs achieved robust in vitro anti-inflammatory and cytoprotection effects against oxidative stress. Importantly, mice were effectively protected against CIRI by enhanced ROS scavenging activity in vivo, and the therapeutic mechanism was systematically verified. These findings broaden the idea of expanding Ir-based NAs as potent antioxidative agents to treat CIRI and other ROS-mediated diseases. STATEMENT OF SIGNIFICANCE: (1) The ROS-scavenging activities of IrO2 are demonstrated comprehensively, which enriched the family of nano-antioxidants. (2) The engineering Tf-IrO2 nano-agglomerates present unique multifarious enzyme activities and simultaneous transferrin targeting and BBB crossing ability for cerebral ischemia-reperfusion injury therapy. (3) This work may open an avenue to enable the use of IrO2 to alleviate ROS-mediated inflammatory and brain injury diseases.

Laser powder bed fusion is a promising technology for local deposition and microstructure control, but it suffers from defects such as delamination and porosity due to the lack of understanding of melt pool dynamics. To study the fundamental behavior of the melt pool, both geometric and thermal sensing with high spatial and temporal resolutions are necessary. This work applies and integrates three advanced sensing technologies: synchrotron X-ray imaging, high-speed IR camera, and high-spatial-resolution IR camera to characterize the evolution of the melt pool shape, keyhole, vapor plume, and thermal evolution in Ti-6Al-4V and 410 stainless steel spot melt cases. Aside from presenting the sensing capability, this paper develops an effective algorithm for high-speed X-ray imaging data to identify melt pool geometries accurately. Preprocessing methods are also implemented for the IR data to estimate the emissivity value and extrapolate the saturated pixels. Quantifications on boundary velocities, melt pool dimensions, thermal gradients, and cooling rates are performed, enabling future comprehensive melt pool dynamics and microstructure analysis. The study discovers a strong correlation between the thermal and X-ray data, demonstrating the feasibility of using relatively cheap IR cameras to predict features that currently can only be captured using costly synchrotron X-ray imaging. Such correlation can be used for future thermal-based melt pool control and model validation.

Abstract Laser powder bed fusion still suffers from defects such as delamination and porosities due to the lack of understanding of melt pool dynamics. To study the fundamental of the melt pool behavior, both geometrical and thermal sensing with high spatial and temporal resolutions are necessary. This work applies and integrates three advanced sensing technologies: synchrotron X-ray imaging, high-speed IR camera, and high-spatial-resolution IR camera to characterize the melt pool dynamics, keyhole, porosity formation, vapor plume, and thermal evolution in Ti-64 and 410 stainless steel. This paper develops an effective algorithm for high-speed X-ray imaging data to identify melt pool geometries accurately. Pre-processing methods are also implemented for the IR data to estimate the emissivity value and extrapolate the saturated pixels. Quantifications on boundary velocities, melt pool dimensions, thermal gradient, and cooling rates are performed, which provides a thorough understanding of the melt pool with integrated geometrical and thermal perspectives. The study discovers a strong correlation between the thermal and X-ray data, enabling the feasibility of using relatively cheap IR cameras to predict features that currently can only be captured using the costly synchrotron X-ray imaging. Such correlation enables the thermal-based melt pool control as well.

Oxidative stress and a series of excessive inflammatory responses are major obstacles for neurological functional recovery after ischemic stroke. Effective noninvasive anti-inflammatory therapies are urgently needed. However, unsatisfactory therapeutic efficacy of current drugs and inadequate drug delivery to the damaged brain are major problems. Nanozymes with robust anti-inflammatory and antioxidative stress properties possess therapeutic possibility for ischemic stroke. However, insufficiency of nanozyme accumulation in the ischemic brain by noninvasive administration hindered their application. Herein, we report a neutrophil-like cell-membrane-coated mesoporous Prussian blue nanozyme (MPBzyme@NCM) to realize noninvasive active-targeting therapy for ischemic stroke by improving the delivery of a nanozyme to the damaged brain based on the innate connection between inflamed brain microvascular endothelial cells and neutrophils after stroke. The long-term in vivo therapeutic efficacy of MPBzyme@NCM for ischemic stroke was illustrated in detail after being delivered into the damaged brain and uptake by microglia. Moreover, the detailed mechanism of ischemic stroke therapy via MPBzyme@NCM uptake by microglia was further studied, including microglia polarization toward M2, reduced recruitment of neutrophils, decreased apoptosis of neurons, and proliferation of neural stem cells, neuronal precursors, and neurons. This strategy may provide an applicative perspective for nanozyme therapy in brain diseases.

As one of the three-dimensional (3D) bioprinting techniques with great application potential, laser-induced-forward-transfer (LIFT) based laser assisted bioprinting (LAB) transfers the bioink through a developed jet flow, and the printing quality highly depends on the stability of jet flow regime. To understand the connection between the jet flow and printing outcomes, a Computational Fluid Dynamic (CFD) model was developed for the first time to accurately describe the jet flow regime and provide a guidance for optimal printing process planning. By adopting the printing parameters recommended by the CFD model, the printing quality was greatly improved by forming stable jet regime and organized printing patterns on the substrate, and the size of printed droplet can also be accurately predicted through a static equilibrium model. The ultimate goal of this research is to direct the LIFT-based LAB process and eventually improve the quality of bioprinting.

Bioprinting is an additive manufacturing technology with great potential in medical applications. Among available bioprinting techniques, laser-assisted bioprinting (LAB) is a promising technique due to its high resolution, high cell viability, and the capability to deposit high-viscousity bioink. These characteristics allow the LAB technology to control cells precisely to reconstruct living organs. Recent developments of LAB technologies are reviewed in this paper, covering various designs of LAB printers, research progresses in energy-absorbing layer (EAL), the physical phenomenon that triggers the printing process in terms of bubble formation and jet development, printing process parameters, and major factors related to the post-printing cell viability. The latest studies on LAB technologies are highlighted, expounding their advantages and disadvantages, and some potential applications are presented. The potential technical challenges and future research trends for LAB technologies are also discussed.

Real-time monitoring of vessel dysfunction is of great significance in preclinical research. Optical bioimaging in the second near-infrared (NIR-II) window provides advantages including high resolution and fast feedback. However, the reported molecular dyes are hampered by limited blood circulation time (~ 5–60 min) and short absorption and emission wavelength, which impede the accurate long-term monitoring. Here, we report a NIR-II molecule (LZ-1105) with absorption and emission beyond 1000 nm. Thanks to the long blood circulation time (half-life of 3.2 h), the fluorophore is used for continuous real-time monitoring of dynamic vascular processes, including ischemic reperfusion in hindlimbs, thrombolysis in carotid artery and opening and recovery of the blood brain barrier (BBB). LZ-1105 provides an approach for researchers to assess vessel dysfunction due to the long excitation and emission wavelength and long-term blood circulation properties.

OBJECTIVES:It is hypothesized that unilateral high-grade tears damage levator ani muscle (LAM) integrity and increase LAM distensibility. This study aimed to investigate how a unilateral high-grade tear caused overdistension of LAM and whether tear positions affect the degree of distension.METHODS:A total of 209 women were screened by translabial ultrasonography. Then, 18 nulliparous women with an intact LAM and 26 postpartum women with unilateral levator ani defects were recruited. The anteroposterior diameter (AP), coronal diameter (LR), and hiatal area (HA) of the minimum levator hiatus were recorded and compared for assessing the distensibility. All 44 subjects underwent magnetic resonance imaging for the diagnosis of levator ani defect and detection of tear positions. Within the software, 3-dimensional pelvic models were developed from magnetic resonance imaging scans for the direct visualization and measurements.RESULTS:Of the 26 postpartum women, 15 were diagnosed with unilateral high-grade tear (caudad in 9, cephalad in 6). The △AP, △LR, and △HA (Valsalva-rest) values of women with a unilateral high-grade tear were significantly larger than those of women with an intact LAM (P < 0.05). The △AP, △LR, and △HA (Valsalva-rest) values of cephalad tears were larger than those of women with caudad tears (P < 0.05).CONCLUSIONS:A unilateral high-grade tear caused the overdistension of LAM. It was validated that cephalad tears caused higher degree of LAM distension than caudad tears.

As a promising 3D bioprinting process, laser induced forward transfer (LIFT) has attracted attention in the last decade due to its advantages of non-contact, nozzle-free, high dropping rate and high resolution. However, the mechanism of bubble/jet formation under laser inducement has not been well comprehended yet. To better understand the multiphase process, the bubble formation and jet process under single laser pulse was explored in this study, using both the Computational Fluid Dynamics (CFD) model and experimental study. The results showed that under a laser pulse with the Gaussian distribution, a vapor bubble was formed around 0.10, then the bubble was expanded over time. During the bubble expansion process, the maximum magnitude of velocity could reach as high as 22m/s. The pressure near the laser interaction area was around 4.72 x10(7) Pa, which is 470 times of the ambient pressure. After increasing the pulse energy and focal spot area, the liquid bubble layer moved downward to complete the bioink transfer process after the collapse of glycerol vapor bubble, which showed similar flow characteristics as the experimental results under the same laser fluence (1.4J/cm(2)). When the laser fluence was decreased to 0.8 J/cm(2), a regular jet flow could be observed. The proposed multiphase numerical model can be used to understand the mechanism of bubble/jet formation under laser inducement and provide some insights into the bioink transfer during LIFT process, in order to eventually optimize the LIFT 3D-printing process with greater cell viability.

To explore the value of high-frequency two-dimensional (2D) ultrasound on demonstrating the morphology of puborectalis muscle and detect muscle avulsion. High-frequency 2D ultrasound and tomographic ultrasound image (TUI) were peformed to demonstrate puborectalis muscle and detect muscle avulsion respectively among 158 women with or without significant pelvic organ prolapse (POP) (POP quantification grade 2 or higher). Mean values were compared using student’s t test between women with or without avulsion defects. We performed Cohen’s Kappa analysis to examine the test agreement between high-frequency 2D ultrasound and TUI mode. Pearson correlation analysis was performed to explore the relationship between the thickness of puborectalis muscle and the measurements of levator–urethra gap (LUG). The result of high-frequency 2D ultrasound in detecting muscle avulsion agreed well with TUI mode (Kappa 0.88, P < 0.05). Women with muscle avulsion had thinner muscles and larger LUG measurements than those with normal muscle insertion (P < 0.05). Pearson correlation analysis revealed the negative relationship between the thickness of puborectalis muscle and LUG measurements (r = − 0.73). The study confirmed that it was feasible to observe the morphology of puborectalis muscle and detect muscle avulsion by high-frequency 2D ultrasound.

Introduction and hypothesis To explore the feasibility of three-dimensional (3D) transperineal tomographic ultrasound in evaluating pelvic floor support of the urethra in women. Methods Three-dimensional transperineal ultrasound volume data sets of 50 women with stress urinary incontinence (SUI) and 25 women without SUI were obtained for analysis. Pelvic floor support of the urethra was evaluated by studying the relationship between the urethra and vagina in vaginal cross section and quantified by estimating the urethral depression (UD) rate. The extent of paravaginal support at level II was also evaluated in tomographic ultrasound imaging (TUI) mode in all participants. Two-sample t-test and Mann-Whitney U test were used for statistical analysis. Results The extent of paravaginal support at level II showed no difference between the two groups. Posterior depression of the urethra into the anterior vaginal wall was increased in SUI (P < 0.05). When the UD rate value was 0.53 (CI 85%) combined with three continuous "abnormal slices," the maximum Youden Index value (sensitivity 0.82, specificity 0.88) was obtained to screen dysfunctional support of the urethra. Conclusions The pelvic floor support of the urethra can be evaluated indirectly by studying the relationship between the urethra and anterior vaginal wall in the vaginal cross section by TUI. The obvious posterior depression of the urethra into the anterior vaginal wall could be indirect evidence of a defect in the support of the urethra.