bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Calibration Approach for Accelerated Creep Testing for Electron Beam Melted (EBM) Ti-6Al-4V Using the WCS Model
Authors: Hossain Md Abir , Pellicotte Jacob T. , Stewart Calvin M. 
Published: September 2023

Abstract
This study outlines a model calibration approach for an accelerated creep test called the dynamic negative stepped test (DNST) to enable the rapid screening of creep resistant materials. In DNST, stress is stepped decreased based on the attainment of a sufficient minimum-creep-strain-rate (MCSR) at each stress level. Steps are repeated, torturing the material, until rupture occurs. The DNST is advantageous as a screening test for new alloys. Alloys and Heats with superior creep resistance will be able to survive longer and with greater ductility than those with poor creep resistance. The calibration of a constitutive model to DNST data furnishes predictions of the conventional creep response being between 65–6685 hr from the relatively short (&lt; 130 hr) DNST Data.
In this study, DNSTs are performed on electron beam melted (EBM) Ti-6Al-4V at 650°C with stepping through 150, 75, 60, and 50 MPa. Six build orientations are tested including 0°, 30°, 45°, 60°, 90°, and V (vertical) direction. The Wilshire-Cano-Stewart (WCS) model is employed to calibrate the experimental data. A systematic calibration approach is adopted. Each step is calibrated numerically. A unique set of minimum-creep-strain-rate (MCSR) and stress-rupture (SR) related material constants, i.e., the Wilshire and Sinh constants are obtained for each build direction. A non-homogenous objective function is used to numerically optimize the strain trajectory and damage trajectory constants. To find the best-fit curve, the strain trajectory constants and damage trajectory constants are numerically refined for each step. The WCS model shows a near perfect prediction of the DNST data. Based on the calibrated constants, conventional creep curves are generated in order to determine which build orientations are likely to exhibit poor, moderate, and superior creep resistance. Predictions of MCSR and SR curves over a wide stress range are estimated at outside the experimental range to investigate the extrapolation pedigree of the approach. This will allow the material designers to have more confidence in DNST generated test data for predicting long-term creep response and structural lifetime.
Unsteady Flow Structure of Corner Separation in a Highly Loaded Compressor Cascade
Authors: Zhong Weibo , Liu Yangwei , Tang Yumeng 
Published: September 2023

Abstract
Corner separation is an inherently unsteady flow feature in an axial compressor cascade, and it significantly affects the aerodynamic performance of compressors. The flow field of a highly-loaded compressor cascade at the Mach number of 0.59 under the moderate separation condition is simulated based on delayed detached eddy simulation. Comparisons of averaged flow field and transient flow field show that the three-dimensional corner separation flow is highly unsteady and composed of fine-scale vortex structures. The classical recognition of corner separation structures is a consequence of time-averaging. To better understand the contribution of unsteady structures to the averaged flow structures, the evolutions of flow fields in time series and the power spectrums are analyzed. A dominant periodic flow fluctuation is caused by the development of separating vortices with a characteristic frequency around 3500Hz, or at a Strouhal number of 0.75. Further, energy scales and spatiotemporal features of these dominant unsteady behaviors are analyzed using proper orthogonal decomposition and dynamic mode decomposition methods. Results show that the low-frequency behaviors mainly caused by the passage vortex at lower-span regions govern large-scale changes of separation flow in size and intensity and act with a certain intermittency. The vortex developing mode around 3500Hz prevails at higher regions affected by the concentrated shedding vortex. As the separating vortices dissipate approaching the midspan, the effect of the vortex developing mode on axial velocity fluctuation is reduced, although it dominates the pressure fluctuation with good stability in the whole passage.
A Novel Multi-Fidelity Surrogate for Turbomachinery Design Optimization
Authors: Wang Qineng , Song Liming , Guo Zhendong , Li Jun , Feng Zhenping 
Published: September 2023

Abstract
The design optimization of turbomachinery is a challenging task as it involves expensive black-box problems. The sample-efficient multi-fidelity optimization (MFO) algorithm has been proposed as an efficient solution to this problem. By utilizing multi-fidelity surrogates (MFS), the MFO algorithm can use fewer high-fidelity samples aided by low-fidelity samples to establish an accurate surrogate model. However, when MFS is used in sequential sampling optimization, it has been observed that the final optimal solution obtained by single-fidelity optimization (SFO) is better than that of MFO, even though MFO performs better at the early stages. This can be attributed to the assumption of an even and nested distribution of samples, which is incorrect when using a sequential adding strategy. To address these issues, we propose a novel algorithm called multi-single-fidelity optimization (MSFO) to overcome the limitations of the conventional MFO procedures. In the surrogate establishment of MSFO, we use the density-based spatial clustering of applications with noise (DBSCAN) method to detect local areas where low-fidelity samples are no longer effective. A combination of both global MFS and local single-fidelity surrogate model, built using high-fidelity samples alone, is used to establish an ensemble, which improves the anti-interference ability of the algorithm against misleading low-fidelity data. The effectiveness of the MSFO algorithm is verified first on numerical benchmark functions. Then, the algorithm is used to optimize the aerodynamic profile of a turbine and the film cooling layout design of a turbine endwall. Here, high-fidelity sample sources are obtained from fine-mesh CFD simulations, whereas low-fidelity sample sources are obtained from the same simulations run on a coarser mesh. The results demonstrate that our MSFO algorithm performs significantly better than the conventional SFO and MFO processes, with a higher level of robustness. Therefore, the effectiveness of the MSFO algorithm is well demonstrated.
A Computationally Efficient Method for Predicting Tip Clearance Effects on the Natural Frequency of Submerged Discs and Inducers
Authors: Langston Cody , Wray Timothy , Brown Andrew 
Published: September 2023

Abstract
Structures submerged in fluids have a lower natural frequency due to the effective added mass of the attached fluid. Recent studies and experiments have shown that this fluid added mass effect is amplified as the gap between the structure and solid boundary closes. This tight tip clearance effect exists in many common engineering structures including inducers in turbopumps. Previous research has been performed to improve fluid/structure finite element models to predict these effects. These approaches can accurately predict the effect but are not viable for complex geometry and extremely tight tip clearances when meshing and excessive runtime problems occur. These challenges are particularly daunting in the design phase when a multitude of geometries and tip clearances may be considered. To remedy this, the tip clearance effect is first classified into three regions, one with an infinite boundary, an intermediate region, and one with a very tight gap. This paper uses finite element analyses of submerged discs to examine how model properties such as disc thickness affect these regions. The results of these parametric studies are then used to develop an equation using a sigmoid fit that is capable of predicting the natural frequency for all clearances which avoids additional finite element runs. This method is applied to an inducer geometry and compared to both the finite element and previous experimental results. The natural frequency predictions of this method are most accurate for inducer structural mode shapes where the blades are not in phase.
Controlling Rheological Properties of Hybrid Hydrogel Using Short Fiber for Extrusion-Based 3D Bioprinting Process
Authors: Tuladhar Slesha , Clark Scott , Habib MD Ahasan 
Published: September 2023

Abstract
Among various available 3D bioprinting techniques, extrusion-based three-dimensional (3D) bio-printing allows the deposition of cell-laden bio-ink, ensuring predefined scaffold architecture that may offer living tissue regeneration. With a combination of unique characteristics such as biocompatibility, less cell toxicity, and high-water content, natural hydrogels are a great candidate for bio-ink formulation for the extrusion-based 3D bioprinting process. However, due to its low mechanical integrity, hydrogel faces a common challenge in maintaining structural ty. To tackle this challenge, we characterized the rheological properties of a set of hybrid hydrogels composed of cellulose-derived nanofiber (TEMPO-mediated nano-fibrillated cellulose, TONFC), carboxymethyl cellulose (CMC) and commonly used alginate. A total of 46 compositions were prepared using higher (0.5% and 1.0%) and lower percentages (0.005% and 0.01%) of TONFC, 1%–4% of CMC, and 1%–4% of alginate to analyze the rheological properties. The shear thinning coefficients of n and K were determined for each composition from the flow diagram and co-related with the 3D printability. The ability to control rheological properties with various ratios of a nanofiber can help achieve a 3D bio-printed scaffold with defined scaffold architecture.
Multi-Resolution Quality Inspection for Additive Manufacturing
Authors: Yang Hui , Reijonen Joni , Revuelta Alejandro 
Published: September 2023

Abstract
Automated optical inspection (AOI) is increasingly advocated for in-situ quality monitoring of additive manufacturing (AM) processes. The availability of layerwise imaging data improves the information visibility during fabrication processes and is thus conducive to performing online certification. However, few, if any, have investigated the high-speed contact image sensors (i.e., originally developed for document scanners and multi-function printers) for AM quality monitoring. In addition, layerwise images show complex patterns and often contain hidden information that cannot be revealed in a single scale. A new and alternative approach will be to analyze these intrinsic patterns with multi-scale lenses. Therefore, the objective of this paper is to design and develop an AOI system with contact image sensors for multi-resolution quality inspection of layerwise builds in additive manufacturing. First, we retrofit the AOI system with contact image sensors in industrially relevant 95 mm/s scanning speed to a laser-powder-bed-fusion (LPBF) machines. Then, we design the experiments to fabricate nine parts under a variety of factor levels (e.g., gas flow blockage, recoater damage, laser power changes). In each layer, the AOI system collects imaging data of both recoating powder beds before the laser fusion and surface finishes after the laser fusion. Second, layerwise images are pre-preprocessed for alignment, registration and identification of regions of interests (ROIs) of these nine parts. Then, we leverage the wavelet transformation to analyze ROI images in multiple scales and further extract salient features that are sensitive to process variations, instead of extraneous noises. Third, we perform the paired comparison analysis to investigate how different levels of factors influence the distribution of wavelet features. Finally, these features are shown to be effective in predicting the extent of defects in the CT data of layerwise AM builds. The proposed framework of multi-resolution quality inspection is evaluated and validated using real-world AM imaging data. Experimental results demonstrated the effectiveness of the proposed AOI system with contact image sensors for online quality inspection of layerwise builds in AM processes.
Thermal-Image-Enabled Additive Manufacturing Process Monitoring and Extrusion Trajectory Compensation
Authors: Duan Molong , Chen Siqi , Yang Yuexin 
Published: September 2023

Abstract
The additive manufacturing (AM) system today still suffers from low precision, low product stability, low automation level, and low data utilization. The urgent need for enhancements on these fronts leads to the various monitoring and control techniques for AM process. Currently, most existing AM monitoring and corresponding control methods are built on statistical models and used to refine the AM process under a parameter-tuning framework. They help regulate the steady-state or low-frequency behavior in AM process but do not address issues related to AM’s unsteady or dynamic behavior. These unsteady and dynamic behaviors are especially prevalent in high-speed, high-acceleration AM systems with moving parts (e.g., fused filament fabrication, directed energy deposition) and dynamic trajectory regions (e.g., corners). Therefore, in this paper, we proposed a monitoring and control solution that addresses printing inaccuracies with real-time thermal image monitoring. The method depends on a low-order analytical model describing the printed path boundary and an established non-contacting deposition direction. The thermal image observations are thus converted to the dynamic time-domain compensation signals to account for dynamic over-extrusion. The method is verified with a fused filament fabrication printer to alleviate its over-extrusion when printing corners.
Development of an Open-Source Low-Cost Modular Quad-Extrusion 3D Bioprinter
Authors: Zgeib Ralf , Wang Xiaofeng , Zaeri Ahmadreza , Zhang Fucheng , Cao Kai , Chang Robert 
Published: September 2023

Abstract
Advanced additive manufacturing (AM) technologies are being harnessed to capture the complex range and specificity of native tissue properties towards fully functional bioprinted tissue constructs. Such enabling technologies have been reported to recapitulate the complexity and heterogeneity of the native tissues. However, the challenges of cost and scalability hamper broad AM process adoption and implementation for fundamental research in the life sciences as well as for clinical end-use applications. In order to address the cost barrier to AM adoption, an open-source low-cost modular quad-extrusion multi-material 3D bioprinting system is developed herein to enable the fabrication of complex tissue constructs. The developed quad-extrusion bioprinter (QEB) is established with two divergent printing modes, namely in-air printing (IAP) and support bath printing (SBP), using gelatin methacryloyl as a model hydrogel bioink. Bioprinted performance outcomes are then measured for structural fidelity with benchmarking to the computer-aided design models. Moreover, biological outcomes are qualified by way of a LIVE/DEAD cell viability assay over a 3-day time course. In summary, the developed QEB is shown to be a robust platform that enables the scalable fabrication of multi-material complex tissue constructs at an accessible cost under $300, further closing the gap between developmental and clinical AM platforms.
3D Printing of Filefish Inspired Microscale Multifunctional Structure
Authors: He Qingqing , Tang Han , Zeng Yushun , Yang Yang 
Published: September 2023

Abstract
Surfaces that exhibit anisotropy, widely found in nature, promote one-dimensional water control by relying on well-organized features. A comprehensive understanding of the wetting behavior of organic liquids, especially the movement of water and oil liquids on the surface, including the manipulation of water droplets, the separation of water and oil, and the cleaning of the ocean, are of great significance. Here, The filefish Navodon septentrionalis has an anisotropic oleophobicity underwater owing to its distinctive skin, which allows it to self-clean oil droplets from its body. Filefish skin has oriented microscaled hook-like spines arranged in an array pattern that contributes to this prominent wetting anisotropy. Considering the performance at a micron level and the variable arrangement, conventional approaches are ineffective in simulating the natural skin structure. Due to its capabilities of high productivity, cost-effectiveness, rapid production, as well as the ability to fabricate microstructures, additive manufacturing (AM), also known as 3D printing, has gained considerable attention in various academic fields. Specifically, we utilize a 3D printing method to fabricate biomimetic structures that resemble the arrangement of spines on the skin of filefish. In order to quantitatively evaluate the hydrophobicity of a 3D-printed surface, four different arrangements of spines were displayed on the surface, including the original surface arrangement, the surface with movement, the surface with rotation, as well as the surface that has both movement and rotation. Each surface arrangement has a level of surface hydrophobicity, with moving and rotating surfaces possessing the greatest level of hydrophobicity. This study provides an opportunity to investigate the wettability of biological surface structures with specific arrangements and offers good prospects for oleophobicity studies and a variety of subsequent applications such as water-oil separation, and oil collection devices.
Model Calibration in Inkjet Printing Process
Authors: Zuniga-Navarrete Christian , Zhou Chi , Sun Hongyue , Segura Luis Javier 
Published: September 2023

Abstract
Inkjet printing (IJP) is an additive manufacturing process capable to produce intricate functional structures. The IJP process performance and the quality of the printed parts are considerably affected by the deposited droplets’ volume. Obtaining consistent droplets volume during the process is difficult to achieve because the droplets are prone to variations due to various material properties, process parameters, and environmental conditions. Experimental (i.e., IJP setup observations) and computational (i.e., computational fluid dynamics (CFD)) analysis are used to study the droplets variability; however, they are expensive and computationally inefficient, respectively. The objective of this paper is to propose a framework that can perform fast and accurate droplet volume predictions for unseen IJP driving voltage regimes. A two-step approach is adopted: (1) an emulator is constructed from the physics-based droplet volume simulations to overcome the computational complexity and (2) the emulator is calibrated by incorporating the experimental IJP observations. In particular, a scaled Gaussian stochastic process (s-GaSP) is deployed for the emulation and calibration. The resulting surrogate model is able to rapidly and accurately predict the IJP droplets volume. The proposed methodology is demonstrated by calibrating the simulated data (i.e., CFD droplet simulations) emulator with experimental data from two distinct materials, namely glycerol and isopropyl alcohol.
Edge Cognitive Data Fusion: From In-Situ Sensing to Quality Characterization in Hybrid Manufacturing Process
Authors: Hoang Danny , Mannan Nasir , ElKharboutly Ruby , Chen Ruimin , Imani Farhad 
Published: September 2023

Abstract
While hybrid additive manufacturing offers improved material properties, increased design flexibility, and reduced production time, the presence of variation in the process (e.g., torque, current, power, and tool speed), together with the impact of tool degradation (e.g., spindles, holders, and cutters) alters the surface roughness and dimensional accuracy of fabricated parts. Recently, edge sensors are coupled with machine learning techniques (e.g., feature-based support vector machines and end-to-end deep neural networks) to connect process parameters with build quality. However, the lack of interpretable learning, poor sample efficiency, and low accuracy and precision limit their capabilities for reliable analysis. This paper introduces hyperdimensional computing (HDC) to fuse load, current, torque, command speed, control differential, power, and contour deviation which provides robust, sample-efficient, and explainable learning of quality characterization. Experimental results on a real-world hybrid 5-axis CNC Deckel-Maho-Gildemeister (DMG) machine show that HDC achieves a superior 90.5% accuracy in prediction of deviation for a 25.4 mm counterbore feature using multichannel data. Comparing the difference in results with ML methods such as support vector machines, logistic regression, multinomial Naive-Bayes, multilayer perceptron, and residual neural network, HDC outperforms by 55.8%, 28.6%, 53.1%, 11.6%, and 28.0%, respectively. The proposed HDC is shown to be effective for data fusion and training with relatively few iterations and eliminates the necessity of costly and long retraining in various manufacturing processes such as 3D printing and bio-fabrication.
Effect of In-Situ Laser Polishing on Microstructure, Surface Characteristics, and Phase Transformation of LPBF Martensitic Stainless Steel
Authors: Ali Majed , Almotari Abdalmageed , Al Gamal Anwar , Alafaghani Ala’aldin , Abedi Hossein , Qattawi Ala 
Published: September 2023

Abstract
Metal additive manufacturing recently has made advances in terms of mechanical properties and microstructure. Martensitic stainless steel (15-5PH) is well known for its corrosion resistance, high stiffness, and tensile strength. Due to the poor surface roughness of additively manufactured parts, further surface treatment techniques are essential. Using Laser Powder Bed Fusion (LPBF) enables in-situ laser polishing that can be applied after the part is fabricated. This technique results in better surface roughness, an improvement in mechanical properties, and fine microstructure over the final layer. In this study, the influence of laser polishing on surface roughness, hardness, and phase transformation are characterized using microscopy, surface roughness test, Vickers hardness test, and X-ray diffraction (XRD) methods, respectively. The LPBF process is monitored using a thermal camera to understand the effect of thermal history on the surface quality and phase transformation after applying single and double passes of the laser polishing. The results indicate that high energy density results in rougher surfaces, a higher amount of retained austenite phase, and lower material hardness. Performing energy input with 2.27 J/mm2 under one laser pass has shown better values in terms of surface hardness and martensitic phases for martensitic stainless steel.
Degradation Modeling of a Robot Arm to Support Prognostics and Health Management
Authors: Kibira Deogratias , Qiao Guixiu 
Published: September 2023

Abstract
Robots are increasingly being adopted in manufacturing industries and this trend is projected to continue. However, robots, like all equipment, degrade once in operation and eventually fail. Yet today’s manufacturing systems are highly paced requiring high equipment availability. Tools and methods are being developed for monitoring, diagnostics, and prognostics to support maintenance activities. These tools require the presence of data representing both healthy and unhealthy states of the robot. Robot unhealthy state data is usually not available because robots are normally operated in a healthy state. A digital twin, which is a virtual real-time representation of a system, can support generating this data. This paper demonstrates the building of a digital twin of a robot workcell that inputs data from the real system. The most frequent robot degradations are identified as increased bearing friction and gear backlash, which are modeled in the digital twin. The digital twin is then used to generate data representing degraded states of the workcell, which are plotted against healthy state data to reveal patterns associated with the respective types of failure. The results show that modeling degradations in the digital twin can provide data which, when analyzed, can support prognostics and health management.