bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterization of ACC Losses Due to Wind and Design of Optimal Wind-Screens
Authors: Bianchini Cosimo , Da Soghe Riccardo , Giannini Lorenzo , Mazzei Lorenzo , Gardner Andrew , Wilde Jamie
Published: September 2023

Large mechanical draft Air-Cooled Condensers (ACC) are increasingly preferred, as a more environmentally friendly solution than wet cooling, in the cooling processes necessary to condense the working fluid in Rankine cycle-based power production systems. Such heat exchangers are conceived as an assembly of smaller modules each composed of an axial fan and inclined bundles where the fluid (usually steam) condensation actually takes place.

The fan is usually installed suspended above the ground at a height between 10 and 25 m, depending on the size of the ACC, with upward flow. Such a layout makes ACCs prone to a decay of efficiency and the onset of fan blade fluctuations due the crossflow induced by neighbour fans. Such detrimental effects become fundamental (up to 50% reduction in ACC cooling potential at 10 m/s), eventually resulting in fan failures, in case of high-speed winds.

Current work is devoted to the study of the wind effects on the cooling performance of the ACC and on blade load oscillations analyzing in detail the effect of wind speed and wind direction on a mid-sized ACC with 6 rows and 3 streets arrangement for 205.4 MW nominal thermal duty.

The study is conducted by means of high-fidelity CFD consisting of Unsteady RANS computations of the entire ACC with all 18 axial fans under rotation and a direct meshing of all the 6 blades per fan.

A common and relatively simple strategy used to mitigate such wind losses is the installation, directly on the ACC structural columns, of partially porous screens made of plastic fabric that act as wind breakers. The correct design of these wind screens is however quite challenging since they not only help reducing the wind speed below the ACC but also introduce additional aerodynamic losses that the fan needs to sustain to correctly feed the heat exchanger.

This study verifies the gains obtainable with the installation of wind screens, proposing a detailed investigation about different windscreen layouts (e.g. perimeter, cruciform, one-bay-back and skirt), variable screen porosity and dimensions.
Optimizing Micro Gas Turbine Operation in a Microgrid System With Natural Gas and Hydrogen Fuel: An AI-Based Approach
Authors: Banihabib Reyhaneh , Fadnes Fredrik Skaug , Assadi Mohsen , Bensmann Boris
Published: September 2023

In the coming years, decentralized power generation systems with renewables are expected to take a leading role, and micro gas turbines will serve as backup sources to compensate for times of low inputs from other sources. In order to deal with the unpredictable energy inputs from renewables, the micro gas turbine must be capable of running under varying load conditions and making fast transitions between them.

The operation of a micro gas turbine in an integrated microgrid has the potential to reduce operational costs and ensure the delivery of demanded heat and power to consumers. This paper investigates the operation of a micro gas turbine in a microgrid, serving as a supplementary power source for a municipal building. The building’s required energy is initially provided by wind turbine power, and the micro gas turbine serves as a backup source during times of wind power deficiency. The micro gas turbine can operate using a natural gas/hydrogen fuel blend ranging from zero to 100% hydrogen. Furthermore, a water electrolyzer with a hydrogen tank is available to operate as a storage system within the microgrid. The study’s results demonstrate the economic and environmental benefits of using hydrogen storage and optimizing operational planning in the microgrid. The primary objective of the paper is to highlight the feasibility and benefits of employing micro gas turbines and hydrogen storage systems within a microgrid as a renewable energy backup power source.
Effect of Entrance Cross-Section on Bulk Swirl and Performance of an S-Duct Inlet in High Subsonic Flow
Authors: Gilbart Ben , Asghar Asad , Allan William D. E. , Stowe Robert , Pimentel Rogerio , Pistun Timothy
Published: September 2023

For fuselage embedded engines, S-shaped ducts with multiple bends are usually employed to induct oncoming air. The combination of bends and diffusion creates a flow that routinely separates in regions of high-pressure gradient causing a concomitant reduction in static pressure recovery, a drop in total pressure, flow distortion and swirl at the engine face: the aerodynamic interface plane. This affects engine performance, stability margin, and safety of the integrated aircraft-engine system. It has previously been established that for a symmetric S-duct with a rectangular entrance, exit flow showed the presence of counter-clockwise, 10° bulk swirl. It was attributed to the axial transition of cross-section from a rectangular entrance to a circular exit and/or to high entrance Mach number. The present tests are done to investigate the exit flow bulk swirl and S-duct performance at Ma = 0.80 using a circular entrance. In addition, a 0.48 offset-to-length ratio, rectangular-entrance S-duct was tested at Ma = 0.80, 0.60 & 0.40. The S-duct was rapid prototyped in plastic using 3D printing. Stream-wise static surface and exit total pressure were measured using static taps and a 5-hole probe, respectively. The measured parameters were used to determine static pressure recovery, total pressure loss, exit flow swirl and pressure field polar distortion. These experiments provided information on flow mechanism and the evidence of the origin of bulk swirl in a symmetric S-duct with rectangular entrance.
An Analysis of the New ISO 23247 Series of Standards on Digital Twin Framework for Manufacturing
Authors: Shao Guodong , Frechette Simon , Srinivasan Vijay
Published: September 2023

ISO has recently issued a series of standards (ISO 23247) that deals with a digital twin framework for manufacturing. It is a generic framework that can be specialized to enterprises engaged in different manufacturing processes, such as discrete, batch, or continuous. This paper presents an analysis of this series of standards with two main objectives: (1) to inform the manufacturing community at large and (2) to examine this series of standards for applications in emerging industry sectors (e.g., biomanufacturing) and with novel manufacturing technologies (e.g., additive manufacturing). It takes a bottom-up approach, dealing with key terms and concepts first and gradually building up the framework. It includes interpretations and analyses to guide an appreciation of what the ISO 23247 series standardizes and the space it opens up for further development and industrial application.
Fatigue Study of Ti-6Al-4V Fabricated by Laser Powder Bed Fusion: Role of Defect Structure and Residual Stress
Authors: Hosseini Seyedmehrab , Vaghefi Ehsan , Mirkoohi Elham
Published: September 2023

Since the start of the second wave of digital manufacturing, the emergence of additive manufacturing (AM), there have been numerous efforts to explore and establish the linkages between process parameters and the mechanical behavior of additively manufactured metals. Among mechanical properties, fatigue life has received much more attention because of its importance in load-bearing applications. On the other hand, decisive factors like residual stress and defect structure hinder the applicability of AM components from being used in mission-critical load-bearing applications. This paper studies defect structure and residual stress’s impact on fatigue life as a necessary step to understanding the criticality of these factors on fatigue performance of Ti-6Al-4V components manufactured via laser powder bed fusion (LPBF). X-ray computed tomography (XCT) and X-ray diffraction (XRD) were performed to characterize the residual stress and defect structure of AMed Ti-6Al-4V specimens. Next, the samples underwent fatigue testing to correlate the XCT and XRD data to fatigue life. The residual surface stress showed a substantial effect on fatigue life. An obvious reduction was observed in fatigue life with the increase of residual surface stress in both scan and build directions. Additionally, average residual stress tends to reduce the fatigue life, but with a scattered behavior. Furthermore, a significant relationship between defect size and fatigue performance was observed. Maximum defect size showed a strong linear impact on fatigue life reduction. Also, a gradual decrease of fatigue life with average defect size was realized.
Development of a Model for Prediction and Optimization of Hardness of Electrodeposited Cu/SiC Composite Using RSM and ANN-PSO
Authors: Rai Prince Kumar , Gupta Ankur
Published: September 2023

Nanocomposite coating has a substantial impact on thermomechanical characteristics. There are several methods for developing nanocomposite coatings; however, electrodeposition is one of the additive manufacturing approaches that has been shown to be suitable due to its adjustable parameters, cost-effective and ease of process. Copper composite coatings have been extensively utilized in the aerospace and automotive industries due to their superior mechanical, electrical, and thermal properties. In the proposed study, a “In-house pulse electrodeposition setup” was developed and used to effectively deposit Cu/SiC nanocomposite coatings from an aqueous sulphate solution. Scanning electron microscopy (SEM) and x-ray diffractometer (XRD) are used to examine the surface morphology, which confirmed the successful co-deposition. The microhardness of the Cu/SiC composite coatings and effect of process parameters is predicted using response surface methodology (RSM). Artificial neural network-particle swarm optimization (ANN-PSO) is used to train, test, and validate experimental data and to investigate the correlation between electrodeposition process parameters and their effects on microhardness. The process was operated at: pulse frequency (10 Hz-100 Hz), duty cycle (20–80%), bath agitation (200–500 rpm), and SiC concentration (1–5 g/L). The determination coefficient (R2), root mean square error (RMSE), mean bias error (MBE), and mean absolute percentage error (MAPE) is used to validate several ANN-PSO models. The optimal morphology with the highest microhardness is produced under the following conditions: 10 Hz pulse frequency, 50% duty cycle, 350 rpm bath agitation, and 5 g/L SiC concentration. The findings showed that the suggested model is an appropriate, flexible, and reliable way for estimating the microhardness of Cu/SiC nanocomposite coatings.
Combining Agent Based Modeling and System Dynamics to Investigate the Circularity of Plastics
Authors: Sethuraman Sandhya , Walzberg Julien , Ghosh Tapajyoti , Uekert Taylor , Carpenter Alberta
Published: September 2023

The United States currently produces about 1 million metric ton of ocean plastic pollution annually. One proposed solution to combat plastic waste is a circular economy (CE), which aims to transition from today’s take-make-waste linear pattern of production and consumption to a system where the value of resources is maximized over time. Two key methods in industrial ecology are useful in assessing the viability of CE: (1) System Dynamics (SD) and (2) Agent Based Modeling (ABM). In prior work, the plastic life cycle was modeled with SD and ABM. The two models calculate recycling rates and costs in different ways, making it difficult to pinpoint necessary next steps. We integrate the ABM and SD models — linking the emergent patterns from micro-level human decisions to system level processes — which allows a more comprehensive understanding of feedbacks, costs, and environmental impacts. The integrated model is more accurate, and can be used to visualize recycling rates and human health and environmental impacts over time. The difference between the integrated and original SD model prompts a Sobol sensitivity analysis, which is used to understand which behavioral factors most affect plastic recycling patterns. We find that the habitual component is typically the most influential in promoting positive recycling behavior. Additionally, we utilize the combined model to understand and visualize how various behavioral intervention scenarios, like improved access to recycling programs and cart tagging, influence recycling patterns; these results can guide future policy-making.
Manufacturing Processes of Implantable Microelectrode Array for in Vivo Neural Electrophysiological Recordings and Stimulation: A State-of-The-Art Review
Authors: Yi Dongyang , Yao Yao , Wang Yi , Chen Lei
Published: September 2023

Electrophysiological recording and stimulation of neuron activities is important for us to understand the function and dysfunction of the nervous system. To record / stimulate neuron activities as voltage fluctuation extracellularly, microelectrode array (MEA) implants are a promising tool to provide high temporal and spatial resolution for neuroscience studies and medical treatments. Design configuration and recording capabilities of the MEAs have evolved dramatically since their invention and manufacturing process development has been a key driving force for such advancement. Over the past decade, since the White House Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative launched in 2013, advanced manufacturing processes have enabled advanced MEAs with increased channel count and density, access to more brain areas, more reliable chronic performance, as well as minimal invasiveness and tissue reaction. In this state-of-the-art review paper, three major types of electrophysiological recording MEAs widely used nowadays, namely microwire-based, silicon-based, and flexible MEAs, are introduced and discussed. Conventional design and manufacturing processes and materials used for each type are elaborated, followed by a review of further development and recent advances in manufacturing technologies and the enabling new designs and capabilities. The review concludes with a discussion on potential future directions of manufacturing process development to enable the long-term goal of large-scale high-density brain-wide chronic recordings in freely-moving animals.
Prediction of Substrate Distortion in Powder DED Using Finite Element Model
Authors: Burl Alan G. , Fletcher Joseph , Saldaña Christopher
Published: September 2023

Leveraging the capabilities of both additive and subtractive manufacturing to repair tooling and other high value items can result in significant cost and time saving opportunities. However, when dimensional accuracy of the repaired component must be held within a specified tolerance, distortion of the component induced during repair can lead to an out of specification final component. This work presents simulation of a novel method to mitigate part distortion using sacrificial support structures. A blown powder directed energy deposition system was utilized for deposition of sacrificial support structures and clad surface depositions. Sacrificial support structures were deposited on the underside of the substrate with varying geometries prior to clad surface depositions. A finite element model was developed and verified using experimental results. The simulation and experimental control results were found to match within 6%. It has been shown that simulation can be used to predict effects of sacrificial support structures on substrate distortion in a blown powder directed energy deposition process.
Energy Consumption and Carbon Emissions of Additive Manufacturing Using Smart Materials: A Supply Chain Perspective
Authors: Han Muyue , Zhao Jing , Li Lin
Published: September 2023

The use of stimuli-responsive materials in additive manufacturing offers new opportunities for fabricating smart structures with time-evolving properties, which is termed as 4D printing. 4D-printed structures can change their shapes over time when triggered by external stimuli, such as heat, moisture, and light, which leads to promising applications in robotics, prosthetic devices, self-folding products, etc. In the current literature, material discovery and characterization have been extensively reported for 4D printing processes, along with advanced modeling and simulation from the structure-to-functionality perspectives. However, the potential impacts of 4D printing technology on supply chain sustainability, particularly concerning energy use and carbon emissions, remain largely unexplored. In this study, mathematical models are established to quantitively analyze how the adoption of 4D printing technology affects energy consumption and carbon emissions during different stages of supply chains. An exploratory comparison is performed using a protective enclosure for microcontroller board under two scenarios, i.e., the components fabricated using stereolithography with conventional resin needing manual assembly upon use, and a 4D-printed monolithic case with heat-activated self-folding on demand. Case study results indicate that the use of 4D printing contributes to more energy-efficient operations in the supply chain with an approximately 10% reduction in carbon emissions.
Surface Morphology of API 5L X65 Pipeline Steel Processed by Ultrasonic Impact Peening
Authors: Haque M. Merajul , Yeh Li-Hsin , Zhang Xing , Li Beiwen , Liao Yiliang
Published: September 2023

Ultrasonic impact peening (UIP) is one of the most promising surface engineering techniques to enhance the durability (fatigue life, stress corrosion cracking resistance, wear resistance, etc.) of metallic materials by imparting surface hardening and compressive residual stresses. Besides these two beneficial surface modifications, the surface morphology after UIP plays another key role in determining the material’s durability. In literature, the effect of UIP on surface morphology was studied with a focus on average surface roughness (Sa). However, the surface integrity and quality after peening are determined by not only Sa, but also surface skewness (Ssk) and surface kurtosis (Sku). In the present study, surface morphological modifications of pipeline steel processed by UIP are investigated, with emphasize on Sa, Ssk and Sku. Surface morphology was characterized using a structured light scanning system. The effects of processing parameters (energy levels and peening time) are investigated. It is found that Sa increases with the increase of energy levels and peening times, whereas Ssk value remains below zero for all peening conditions, indicating the predominance of the valleys rather than peaks on the surface. In addition, the Sku value remains below three for all measurements, which ensures the absence of extreme surface peaks, indicating the removal of stress concentration points. The in-depth hardness is also tested to observe the surface strengthening effect.
Real-Time Temperature Prediction of a Moving Heat Source Problem Using Machine Learning
Authors: Heydari Mahtab , Kung Pei-Ching , Tai Bruce L. , Tsou Nien-Ti
Published: September 2023

Moving heat source problem is commonly seen in many manufacturing applications, such as machining, laser cutting, welding, and additive manufacturing processes, while numerical modeling often takes time to analyze. This paper presents a neural network (NN) and linear-time invariant (LTI) system-based framework, aiming at real-time temperature prediction both spatially and temporally. Training data are generated from finite element analysis (FEA) and processed with convolution neural network (CNN) to form a surrogate model for location-dependent thermal response. LTI is used to superimpose thermal responses based on the heat source’s path and magnitude. The suitability of this framework is evaluated for materials of both low and high thermal diffusivities as well as adiabatic and nonadiabatic cases. In the training of the model, the low thermal diffusivity and high thermal diffusivity cases both showed training and testing correlations of over 99%. Overall, all validation studies show good agreement between the predicted temperature and the ground truth. More errors are seen when the material has a high thermal diffusivity (< 21.7 %), and the heat is applied adjacent to the boundaries (< 23.6 %).
Grinding Force Prediction Model by Discretizing Stochastic Grains
Authors: Zhang Xin , Li Xin , Zhang Xueping , Yao Zhenqiang
Published: September 2023

Material removal process during grinding is the accumulative effect of numerous stochastic grains’ asperity interactions with workpiece, leading to extreme difficulty to predict grinding force. The existing analytical grinding force prediction models primarily focus on representing the stochastic interactions in contact zone using average effective grains number and undeformed chip thickness or assuming ideal distribution law for the stochastic characteristics of grains, which can result in relatively large error in predicting grinding force, whereas the experimentally empirical methods are usually time-consuming and laborious. To overcome these drawbacks, the research proposed a hybrid methodology to predict grinding force by means of discretizing stochastic grains, matrix calculation and two-dimension (2D) micro-grinding simulations. Consequently, grains’ stochastic geometry, size, position distribution, and corresponding undeformed chip size in contact region were considered to guarantee the authenticity and credibility of the predicted grinding forces. Firstly, the stochastic interaction between a random grain and workpiece was converted equivalently to a series of plane-cutting processes between micro-edges with different rake angles and micro-chip layers with corresponding thicknesses. Then, the rake angle matrix of micro edges and the corresponding cutting depth matrix in contact zone were identified through matrix calculation method based on single grain discretization analysis. Followed, both matrixes were incorporated into the plane-cutting force interpolant function derived from the finite element analysis (FEA) to achieve the grinding force matrix, and further the resultant grinding force along normal and tangential directions. The cross validation with experimental grinding results in literature showed the prediction error falls within 20% to verify the authenticity of proposed model. The model considers both influences of the stochastic grain distribution in grinding tool and their interactions with workpiece. Therefore, it bears significance in improving the prediction accuracy of grinding force, and promoting a better understanding of inherent logic from micro-level abrasive-workpiece interactions to macro-level grinding force prediction during the abrasive-based machining including grinding, honing, and polishing.
Continuous Fiber Path Optimization in Composite Additive Manufacturing via a Finite Element Model With B-Spline Fiber Parameterization
Authors: Duan Molong , He Shuaiyin
Published: September 2023

This paper presents a finite element model of the additive manufactured part with continuous fiber reinforcement. The proposed model exploits the B-spline parameterization of the fiber path while approximating the fiber path within a triangular element to be a straight line. This simplification enables the analytical and computational-efficient calculation of the mechanical properties of the printed parts. The elements in the model are differentiated into three categories to account for the location of fiber: matrix elements, main fiber-reinforced elements, and secondary fiber-reinforced elements. Analytical formulas to calculate the geometry-dependent stiffness matrix are established. With the established finite element model with B-spline fiber parameterization, the continuous fiber path is optimized by manipulating the control points of the B-spline to reduce fiber usage while increasing the stiffness of the part. The parts with the reference and optimized carbon fiber paths are 3D printed with a continuous-fiber fused deposition method printer, with polylactic acid as the matrix material. The estimated mechanical properties of the printed parts have been verified via experimental tensile tests.
Impact of Process Parameters on Laser Beam Machining of Ceramic Material
Authors: T. Geethapriyan , C. Kailaash Pandiyan , Palani I. A.
Published: September 2023

Laser Beam Machining (LBM) is a procedure of directing the laser beam to generate heat energy to vaporize the interacting material on the surface of the workpiece. Ceramics are used in several applications, like hybrid circuits in the automotive industry, environment-friendly LEDs, and other electrical components. The basis of the study is to analyze the machining of alumina ceramic by LBM to enhance process parameters such as Material Removal Rate (MRR), Conicity (CO), and Overcut (OV) by inspecting machining parameters like Power (P), Transverse Speed (TS), Standoff Distance (SOD) and Gas Pressure (GP) precisely. From multiple assessments of varying SOD, high MRR, finer overcut, and conicity are achieved. The deterministic parameters for machining on alumina ceramic are the standoff distance and transverse speed. The SOD between the workpiece surface and nozzle, influences the intensity and divergence of the beam to get better overcut and conicity. At the same time, transverse speed affects the cutting time of the workpiece since increased cutting speed reduces cutting time on the workpiece resulting in high MRR.
Controlling Friction of Rods With Entrained Particles
Authors: Khalil Md Ibrahim , Tong Dezhong , Jawed Mohammad Khalid , Khoda Bashir
Published: September 2023

In this paper, we demonstrate a novel process of controlling the friction of a continuous rod by adhering inorganic micro-particles. A standardized continuous dip-coating protocol has been developed in our laboratory for depositing particles from a liquid carrier system to the cylindrical elastic rod substrate. Polymeric binder is used to deliver the particles as asperities over the rod substrate and by controlling their size and distribution, the coefficient of friction of the rod is determined. Tabletop experiments are designed and performed to measure the friction coefficient following the capstan equation. The friction coefficient of the coated rod increases with the increase of the particle volume fraction in the suspension. The force-extension curve of these experiments shows that the proposed process can successfully tune the friction between rods.
Process Signature Characterization and Anomaly Detection With Personalized PCA in Laser-Based Metal Additive Manufacturing
Authors: Shi Naichen , Al Kontar Raed , Guo Shenghan
Published: September 2023

During Laser-Based Metal Additive Manufacturing (LB-MAM), the interaction between the energy source (i.e., the laser beam) and the material generates spatter tracks along the laser scan path. The spattering behavior is the “process signature” reflecting the process stability and anomalies that potentially lead to defects in the printing results. In-situ thermography captures the spatter patterns in real-time, which are crucial evidence for process monitoring and anomaly detection in LBMAM. A critical aspect of anomaly detection in LBMAM is identifying the unique spatter pattern in a thermal image that differs from the generic spattering behavior in the current layer being deposited (i.e., “outliers”). It is equivalent to separating the “local features” and layer-wise “shared features” from the same thermal image and recognizing the local anomaly, i.e., recognizing the outlier “local features.” In state-of-the-art, thermal-image-based anomaly detection in LBMAM relies on conventional feature extraction procedures, which limit the “view” of information to each thermal image and cannot separate the layer-wise spatter pattern governing these thermal images. Consequently, the features extracted are less interpretable, and the anomaly detection method cannot distinguish the abnormality in the current process signature from the layer-to-layer evolution of spattering dynamics. Targeting the limitation in state-of-the-art literature, this study adopts personalized Principal Component Analysis (PerPCA) as a novel means for process signature characterization and anomaly detection in LBMAM. Designed for heterogeneous data with shared features, PerPCA simultaneously extracts the “local features” and “shared features” from the same image. An outlier detection strategy can be designed upon PerPCA that removes the shared features from a process signature and examines the local features at the current printing time for abnormality. The proposed method is demonstrated with selected data from the 2018 AM Benchmark Test Series from the National Institute of Standards and Technology (NIST). The results show good potential for extending the method to complex geometry built with LBMAM.
Physics-Informed Machine Learning Model for In-Process Estimation of Cutter Runout Parameters in End Milling
Authors: Vaishnav Shubham , Desai K. A.
Published: September 2023

Cutter runout is inevitable in machining operations involving rotary cutting tools, such as end milling. The runout parameters are usually measured offline using a dial indicator or gap measurement sensor. Alternatively, measured cutting force data can also be used to determine these parameters using a non-linear optimization technique. It is an iterative approach sensitive to measured force data and could not determine parameters online. This work proposes utilizing a machine learning algorithm to extract cutter runout parameters from real-time cutting force data. Developing a machine learning model requires a large amount of experimental data over a wide range of conditions which is costly, time-consuming, and impractical. The present work generates synthetic datasets through a physics-based Mechanistic force model instead of performing milling experiments for the model training. The synthetic training datasets generated using the Mechanistic model minimize the effects of process disturbances and measurement outliers on the prediction abilities. A set of end milling experiments are carried out to compare the performance of the proposed approach with experimentally measured parameters and the non-linear optimization model presented in the literature. It has been shown that the proposed approach has robust prediction abilities and can be effectively employed for the online estimation of cutter runout parameters.
Influence of Modified Heat Treatments and Build Orientations on the Microstructure of Additively Manufactured IN718
Authors: Almotari Abdalmageed , Alafaghani Ala’aldin , Ali Majed , Al Gamal Anwar , Abedi Hossein , Qattawi Ala
Published: September 2023

Additive manufacturing of superalloys has significant merits as it produces components with higher complexity and less post-machining. Inconel 718 (IN718) is a well-known high-strength nickel alloy that can be manufactured using laser powder bed fusion (LPBF). During the LPBF process, the thermal gradient and solidification rates vary, resulting in various solidification conditions and microstructures. This study aims to investigate the microstructural characteristics of IN718 manufactured by LPBF, including grain size and orientation. The presented work developed post heat treatment procedures to enhance the mechanical properties of fabricated IN718 and minimize anisotropy associated with the as-built conditions. Three different heat treatments with varying times and temperature ranges were investigated, followed by standard aging in IN718 samples fabricated in the X and Z orientations to homogenize the microstructure. Digital light microscope, X-Ray Diffraction (XRD), and hardness tests were used to characterize the microstructure of additively fabricated IN718. Results indicated that the heat treatment at 980°C improved the hardness. An additional increase in heat treatment temperature increased grain size and undesirable precipitates.
A Human Robot Collaboration Framework for Assembly Tasks in High Mix Manufacturing Applications
Authors: Dhanaraj Neel , Ganesh Niraj , Gurav Rohit , Jeon Minseok , Manyar Omey M. , Narayan Santosh , Park Jaehyun , Yu Zhao , Gupta Satyandra K.
Published: September 2023

Robots are mainly used in mass production applications. Recent advances in collaborative robots make it possible for humans and robots to collaborate on high-mix, low-volume applications. This paper presents a framework for human-robot collaboration in high-mix applications. Tasks that require delicate or complex operations are assigned to humans. Tasks that are straightforward are assigned to robots. Simulation is utilized to design the assembly cell by placing the robots and sensors in the cell. Computer vision is used to localize parts in the cell, and automated motion planning is used to generate robot trajectories to perform assembly tasks. The cell also tracks humans to ensure safe operations. A unique feature of the cell is its ability to detect contingency situations and alert human operators to resolve them. Humans can use a gesture-based interface to communicate with the cell. We use a satellite assembly case study to illustrate the capabilities of the cell and demonstrate the feasibility of performing complex assembly operations using human-robot collaboration.
A Versatile Point Cloud Registration Method for Quantifying Geometric Deviation in Additive Manufacturing
Authors: He Chuan , Okwudire Chinedum
Published: September 2023

One of the advantages additive manufacturing (AM) has over traditional manufacturing techniques is the complexity of the parts it can produce. However, this complexity creates a challenge in quantifying the geometric deviation between the desired and printed shapes due to the lack of clear reference features on the printed part. Existing methods for quantifying shape deviation in parts made via AM make simplifying assumptions about reference features, thus limiting their versatility. To address this shortcoming, in this work, the iterative closest point (ICP) algorithm is adopted to achieve accurate registration between desired and printed shapes. To enhance its versatility, the proposed method makes no assumptions about the reference features. Instead, it determines the best reference features automatically by performing ICP using a weighted sliding window. To validate the proposed method, two simulated case studies are used. In both cases, the proposed method is able to automatically determine reference features, hence accurately represent geometric deviation, without need for simplifying assumptions.
Digital Twin Simulation and Optimization of Manufacturing Process Flows
Authors: Lee Hankang , Yang Hui
Published: September 2023

The new wave of Industry 4.0 is transforming manufacturing factories into data-rich environments. This provides an unprecedented opportunity to feed large amount of sensing data collected from the physical factory into the construction of digital twin (DT) in cyberspace. However, little has been done to fully utilize the DT technology to improve the smartness and autonomous levels of small and medium-sized manufacturing factories. Indeed, only a small fraction of small and medium-sized manufacturers (SMMs) has considered implementing DT technology. There is an urgent need to exploit the full potential of data analytics and simulation-enabled DTs for advanced manufacturing. Hence, this paper presents the design and development of DT models for simulation optimization of manufacturing process flows. First, we develop a multi-agent simulation model that describes nonlinear and stochastic dynamics among a network of interactive manufacturing things, including customers, machines, automated guided vehicles (AGVs), queues, and jobs. Second, we propose a statistical metamodeling approach to design sequential computer experiments to optimize the utilization of AGV under uncertainty. Third, we construct two new graph models — job flow graph and AGV traveling graph — to track and monitor the real-time performance of manufacturing jobshops. The proposed simulation-enabled DT approach is evaluated and validated with experimental studies for the representation of a real-world manufacturing factory. Experimental results show that the proposed methodology effectively transforms a manufacturing jobshop into a new generation of DT-enabled smart factories. The sequential design of experiments effectively reduces the computation overhead of expensive simulations while optimally scheduling the AGV to achieve production throughputs in a cost-effective way. This research is strongly promised to help SMMs fully utilize big data and DT technologies for gaining competitive advantages in the global market.
Understanding Mechanical Response of Porous Polymeric Stamps During Large-Area Metal-Assisted Chemical Imprinting of Silicon
Authors: Dasinor Emmanuel , Sharstniou Aliaksandr , Ding Yifu , Azeredo Bruno
Published: September 2023

The patterning of semiconductors with complex 3D hierarchical structures having sub-100 nm resolution is propelling a new generation of optical metasurfaces for applications in optoelectronic interconnects and hyperspectral imaging. These complex structures are now manufacturable with the advent of Metal-assisted chemical imprinting (Mac-Imprint). This technique involves bringing a 3D patterned catalytic stamp into contact with a semiconductor substrate in the presence of an etching solution. Recently, a novel class of catalytic stamps made of Au-coated porous polymeric membranes was developed in order to allow for etching solution storage and mass-transport of the reactants, all of which are essential for fast and accurate Mac-Imprint. Further, the flexibility and stretchability of these porous membranes allows for the use of pressure-based actuation to bring both planar and non-planar substrates in conformal contact with the stamp, replicating the set-up of modern nanoimprinting lithography equipment. Due to the contact-based nature of the Mac-Imprint process, it is vital to understand the mechanical behavior of the stamp. Thus, this work attempts to computationally elucidate the mechanical behavior of the stamp during conformal Mac-Imprint. In particular, the elastic behavior of a stamps laminate made of commercial porous polyvinylidene fluoride (PVDF) with nominal pore sizes of 100 nm and solid polyimide (PI) is modelled using finite element analysis (FEA) in a commercial ANSYS software with material constants, extracted via uniaxial tensile test. The FEA prediction using 2D axisymmetric model is able to capture the induced biaxial stress, strain, deflection and yield of the porous PVDF. To validate simulation results, a deflection test was experimentally performed as a function of air pressure and the obtained results matched with the FEA simulation.
A Comparison of Microstructure and Mechanical Properties of TIG and MIG Welded Dissimilar AA7075 / AA6061 Aluminium Alloys Subjected to Friction Stir Processing
Authors: Rana Rajeev , Karunakar D. Benny , Karmakar Anish
Published: September 2023

The present work investigates the microstructure and mechanical characteristics of TIG and MIG welded dissimilar AA7075 / AA6061 aluminum alloys subjected to Friction Stir Processing (FSP). Initially, aluminum plates were welded using TIG and MIG processes along the rolling direction. Next, FSP was performed on the TIG and MIG welded plates using a rotating tool with a welding speed of 40 mm/min and a tool rotating speed of 940 rpm. The microstructures of the welded joints have been quantified using optical microscopy, Field Emission–Scanning Electron Microscopy, and Electron Back Scattered Diffraction. Vickers hardness and tensile tests were carryout out to assess the mechanical properties. The findings revealed that the application of FSP on TIG-welded joints resulted in smaller grain sizes. TIG+FSP joint possessed the highest yield strength, ultimate tensile strength, and stir zone hardness compared to the MIG+FSP joint. Lower hardness was found on the heat-affected zone of the AA6061 side for the MIG+FSP joint.
Strong coupling dynamics of driven quantum systems with permanent dipoles
Authors: Burgess Adam , Florescu Marian , Rouse Dominic M.
Published: September 2023

Many optically active systems possess spatially asymmetric electron orbitals. These generate permanent dipole moments, which can be stronger than the corresponding transition dipole moments, significantly affecting the system dynamics and creating polarized Fock states of light. We derive a master equation for these systems with an externally applied driving field by employing an optical polaron transformation that captures the photon mode polarization induced by the permanent dipoles. This provides an intuitive framework to explore their influence on the system dynamics and emission spectrum. We find that permanent dipoles introduce multiple-photon processes and a photon sideband, which causes substantial modifications to single-photon transition dipole processes. In the presence of an external drive, permanent dipoles lead to an additional process that we show can be exploited to control the decoherence and transition rates. We derive the emission spectrum of the system, highlighting experimentally detectable signatures of optical polarons, and measurements that can identify the parameters in the system Hamiltonian, the magnitude of the differences in the permanent dipoles, and the steady-state populations of the system.
The V˙O2max Legacy of Hill and Lupton (1923)—100 Years On
Authors: Millet Grégoire P. , Burtscher Johannes , Bourdillon Nicolas , Manferdelli Giorgio , Burtscher Martin , Sandbakk Øyvind
Published: September 2023

Purpose: One hundred years ago, Hill and Lupton introduced the concept of maximal oxygen uptake (), which is regarded as “the principal progenitor of sports physiology.” We provide a succinct overview of the evolvement of research on , from Hill and Lupton‘s initial findings to current debates on limiting factors for and the associated role of convective and diffusive components. Furthermore, we update the current use of in elite endurance sport and clinical settings. Practical Applications and Conclusions: is a healthy and active centenarian that remains a very important measure in elite endurance sports and additionally contributes as an important vital sign of cardiovascular function and fitness in clinical settings. Over the past 100 years, guidelines for the test protocols and exhaustion criteria, as well as the understanding of limiting factors for , have improved dramatically. Presently, possibilities of accurate and noninvasive determination of the convective versus diffusive components of by wearable sensors represent an important future application. is not only an indicator of cardiorespiratory function, fitness, and endurance performance but also represents an important biomarker of cardiovascular function and health to be included in routine assessment in clinical practice.
Do Fundamental Movement Skill Domains in Early Childhood Predict Engagement in Physical Activity of Varied Intensities Later at School Age? A 3-Year Longitudinal Study
Authors: Kasanen Maria , Laukkanen Arto , Niemistö Donna , Kotkajuuri Jimi , Luukkainen Nanne-Mari , Sääkslahti Arja
Published: September 2023

This study was conducted to determine how total fundamental movement skill (FMS) score and, separately, locomotor skill (LMS), and object control skill scores in children 3–8 years old predicted their specific-intensity physical activity 3 years later. Overall, 441 Finnish children (51.7% female, baseline mean age of 5.6 years) participated in the study. Total FMS, LMS, and object control skill scores were assessed using the Test of Gross Motor Development, third edition. The time spent engaged in physical activity of different intensities (light, moderate, vigorous, moderate-to-vigorous, light-to-vigorous, and sedentary behavior) was determined using accelerometers. A two-level regression model was used in the analysis, considering potential covariates and interactions. The results showed that moderate physical activity, vigorous physical activity, and moderate-to-vigorous physical activity were predicted by the total FMS score (β = 0.177 to 0.203, p = .001–.003) and the LMS score (β = 0.140 to 0.164, p = .004–.014), but not the object control skill score. Moreover, the LMS score inversely predicted sedentary behavior (β = −0.116, p = .042). In conclusion, higher FMS and, specifically, LMS scores seem to predict more engagement in moderate-to-vigorous physical activity and less sedentary behavior over time. However, most of the variance in physical activity remains unexplained.
The Effects of Dry Needling on Hamstring Range of Motion: A Critically Appraised Topic
Authors: Curry Amy L. , Jang Suhyun , Monahan Michael P. , Rivera Matthew J.
Published: September 2023

Clinical Scenario: Hamstring range of motion (ROM) and the influence it has on injury risk is among great discussion in the literature. Hamstring injury may result from hamstring tightness, poor flexibility, or decreased ROM, and many argue that this can be prevented through various intervention strategies. In active populations, risk of further injury, pain, and complications throughout the kinetic chain can occur if minimal hamstring ROM is left untreated. One therapeutic intervention that has been applied to varying parts of the body to help improve function while relieving pain is dry needling (DN). This intervention includes the application of needles to structures to induce responses that might benefit healing and overall stimulation of a neurological response. In this review, the intent is to identify evidence and the effects of DN on hamstring ROM. Clinical Question: What are the effects of DN on hamstring ROM? Summary of Key Findings: Among total 11 articles, 1 single-blinded randomized controlled trial and 2 double-blinded randomized controlled trials were included in this critically appraised topic. All 3 articles had inconclusive evidence to isolate the application of the DN intervention. There was insufficient evidence to identify if DN independently improved hamstring ROM; however, in combination with interventions such as exercise and stretch plans, there were improvements on ROM. Clinical Bottom Line: DN does not significantly increase or decrease the ROM of the hamstrings. When combined with exercise and stretch plans, DN could increase ROM. Strength of Recommendation: The grade of B is recommended by the Strength of Recommendation Taxonomy for inconsistent or limited-quality patient-oriented evidence.
Handling fluorinated gases as solid reagents using metal-organic frameworks
Authors: Keasler Kaitlyn T. , Zick Mary E. , Stacy Emily E. , Kim Jaehwan , Lee Jung-Hoon , Aeindartehran Lida , Runčevski Tomče , Milner Phillip J.
Published: September 2023

Fluorine is an increasingly common substituent in pharmaceuticals and agrochemicals because it improves the bioavailability and metabolic stability of organic molecules. Fluorinated gases represent intuitive building blocks for the late-stage installation of fluorinated groups, but they are generally overlooked because they require the use of specialized equipment. We report a general strategy for handling fluorinated gases as benchtop-stable solid reagents using metal-organic frameworks (MOFs). Gas-MOF reagents are prepared on gram-scale and used to facilitate fluorovinylation and fluoroalkylation reactions. Encapsulation of gas-MOF reagents within wax enables stable storage on the benchtop and controlled release into solution upon sonication, which represents a safer alternative to handling the gas directly. Furthermore, our approach enables high-throughput reaction development with these gases.