bluuebunny/crossref_metadata_2025_split huggingface dataset:
Performance Characteristics of a Rotating Detonation Combustor Exiting Into a Pressurized Pleunum to Simulate Gas Turbine Inlet
Authors: Talukdar Shaon , Langner Dalton , Gupta Apurav , Agrawal Ajay K. 
Published: September 2023

Abstract
The present study aims to experimentally characterize the performance of a rotating detonation combustion (RDC) system integrated with a pressurized downstream plenum to simulate the high-pressure inlet conditions of power generating gas turbines. A thorough understanding of the operational behavior including wave mode behavior, static pressure profile along the combustor length, and dynamic features of pressure fluctuations is crucial for successful integration of RDC with the turbine. In this study, two RDC configurations are investigated, RDC with a constant area annulus and RDC with a converging nozzle. In both cases, the RDC flow exited into a plenum chamber kept at pressures varying from 155 kPa to 330 kPa. RDC was operated on methane and oxygen-enriched air to represent reactants used in land-based power generation. Experiments were conducted for the two RDCs configurations operated at three reactant mass flow rates (0.23, 0.32, 0.46 kg/s). The RDC performance is characterized by time-averaged static pressures measurements, and wave velocity determined by ionization probes. In addition, dynamic pressure measurements were recorded both inside and near the exit of RDC channel to investigate wave interactions between RDC and downstream plenum. Results show that the RDC with the converging nozzle achieved superior performance while minimizing detrimental interactions with the reflected shock and/or acoustic waves from the downstream plenum.
Diagnostic Strategies for Microturbines Operating Status Applied to Predictive Maintenance: Experimental Test Case
Authors: Álvarez Eduardo Á. , Yosry Ahmed G. , Fernández Jiménez Aitor , Calleja Rodríguez Antonio , Rico Secades Manuel 
Published: September 2023

Abstract
The current study represents a preliminary step towards detecting operating problems in wind microturbines automatically. The electrical frequency signal generated by the turbine allows for the detection of operating issues during its power stage. To conduct a comprehensive study, a vertical-axis microturbine has been tested, under various failure conditions, inside a closed-loop wind tunnel. An electronic data acquisition system has been developed to continuously measure the frequency signal during the experiments. The output data, including the power and the frequency signals, has been analyzed in terms of average frequency and standard deviation. The collected and stored data is accessible through an operation and maintenance system, which uses an AI-based algorithm and a Jupyter Notebook tool that is executed in Python.
A Robustness Assessment of a Fiber Optic Particle Sensor for Aerospace Turbomachinery
Authors: Overbaugh Malcolm , Esquivias Brandon , Li Alice , McDonell Vincent , Waldherr Gregor A. , Lin Hai 
Published: September 2023

Abstract
Various regions globally are prone to specific weather events and contain geologic features that promote high concentrations of airborne particles, consequently causing the aircraft operating in these regions to endure reduced safety and inferior performance. The ingestion of airborne particles of unknown type and measure into aerospace turbomachinery can cause unpredictable failures and subsequent reductions of propulsion, indicating the need for more accurate preemptive maintenance schedules for aircraft engines to avert most major failures. The installation of an on-board sensor capable of determining the type and concentration of particles entering the engines of an aircraft would provide the data necessary to adjust maintenance schedules and make operating aircraft in particle laden conditions safer. This study demonstrates the methods used in the design of a proprietary fiber optic particle sensor for non-intrusive real time measurement, which sizes the ingested material and measures its distribution and concentration directly in an engine’s flow path. A test rig was devised to provide high velocity and high temperature, contaminated flow using ISO 12103-1 A3 Medium Test Dust. Durability tests using this system consisted of exposing the sensor probe to prolonged periods of high velocity dust exposure, high temperature flow up to 275 °C and modeled aircraft vibration using MIL STD 810H: Method 514.8 – Vibration, Annex D, Category 22, and Method 516.8 – Shock, High Speed Craft II and Functional Shock – Procedure I. The results from these tests were compared to cold flow test data to determine the operational limits of the sensor and assess the feasibility of incorporating such a sensor into current and future aerospace turbomachinery. The results indicate that the fiber optic sensor probe is robust to dust exposure, temperature, and vibration, and was able to consistently size and count the test dust, thus building confidence towards demonstration in a high-fidelity environment.
Performance Characterization of a Small-Scale Pumped Thermal Energy Storage System
Authors: Smith Natalie R. , Just Joshua , Johnson Jeremy , Karg-Bulnes Fernando 
Published: September 2023

Abstract
Long-duration, grid-scale energy storage technologies provide a potential pathway to enable full penetration of renewables on the electricity grid and still maintain grid reliability and security. Among these technologies, pumped thermal energy storage (PTES), which stores energy thermally using a heat pump and discharges the stored thermal energy with a heat engine when needed, is an advantageous solution with high round trip efficiency, no geographical constraints, and modularity for various use cases. To support the technology development, a first-of-a-kind small scale demonstration facility of an air-based PTES has been designed, built, and tested with the objective of better understanding controls strategies and overall system operation. The PTES demonstration facility operates simple-recuperated cycles for charge and discharge mode with separate drivetrains for each mode and shared heat exchangers and storage systems. At a discharge mode rating of 5kW, this facility storages heat in a thermal oil up to 350 °C and cold in a water-glycol mixture down to −10 °C. In this paper, full system performance characterization is described with steady state and transient data from both charge and discharge modes during start-up, shut down, steady state, and system balance. Time series temperature data comparisons of a cold and warm startups are compared. These data highlight the importance of transient control tuning, the influence of ambient effects, and challenges associate with first implementation of a technology, particularly heat loss. This study documents the first of a kind operation of a full air-based PTES system.
Super-Critical Carbon Dioxide Power Cycle for Waste Heat Recovery Utilizing Hermetic Oil-Free Turbomachinery: Cycle and Conceptual Turbomachinery Design
Authors: Ertas Bugra , Zierer Joseph , McClung Aaron , Torrey Dave , Bidkar Rahul A. , Hofer Doug , Rallabandi Vandana , Singh Rajkeshar , Zhang Xiaohua 
Published: September 2023

Abstract
This paper focuses on the conceptual design of a supercritical carbon dioxide (sCO2) waste heat recovery (WHR) power drivetrain for application to natural gas (NG) compressor stations. The first goal of this work was to determine the potential benefits from operating a sCO2 WHR unit with a novel oil-free hermetically sealed dual-spool power drivetrain. A second objective was to identify existing technology gaps and risks that surfaced through the design process. The drivetrain configuration utilizes process gas lubricated bearings and an immersed electric machine, which enables oil-free and leakage-free operation of the power system. The concept adopts a cascaded Brayton bottoming cycle powered from the 510 °C and 89 kg/s exhaust of a 34MW aeroderivative gas turbine used in NG compression stations. Operating boundaries for the proposed bottoming cycle reside between 237 bar, 485 °C and 85 bar, 133 °C. The proposed concept utilizes a dual spool drivetrain configuration possessing a high-speed gas-generator rotor aerodynamically coupled to a low-speed power turbine. The results from the study showed that the oil-free drivetrain configuration has the potential to increase the aeroderivative gas turbine simple cycle efficiency of 41% to a combined cycle efficiency of 51% with a potential of generating an additional 8.5 MWe of emission-free power from the bottoming cycle. Key risks identified for advancing the concept include bearing load capacity, rotordynamics, and thermal management. Finally, the proposed oil-free hermetic sCO2 power system is compared to an installed Organic Rankine Cycle (ORC) specific case study, which was particularly useful when comparing output power, recovered energy, and improved efficiency of the present concept.
Industrial Wireless Cyber-Physical Systems Performance Using Deep Learning
Authors: Kashef (Hany) Mohamed , Candell Richard , Montgomery Karl 
Published: September 2023

Abstract
Industrial wireless communications networks have a major role in the future industrial cyber-physical systems (CPSs) by allowing higher flexibility and massive machine connectivity. However, the reliability and latency of industrial wireless networks need to be evaluated accurately to ensure satisfying the industrial CPS requirements and analyze degradation factors. In order to measure the impact of deploying industrial wireless on cyber-physical system performance, a deep learning framework based on the generative adversarial network (GAN) is introduced. The GAN is used to model the performance of the system and identify the impact of various wireless impairments on the system performance. The GAN model can deploy features from both the wireless network and operational performance spaces. The loss function of the discriminator part of the GAN is deployed as a performance metric that can be used to model the average performance of a specific wireless scenario and compare to the normal behavior of the industrial wireless system. The proposed GAN-based methodology is validated using a dual-robot collaborative lift use case in which IEEE 802.11ac is employed.
Multi-Axis 3D Printer Design Challenges for In-Situ Additive Manufacturing
Authors: Rudd Liam , Campbell Matthew I. , Alonayni Ghazi 
Published: September 2023

Abstract
Multi-axis Additive Manufacturing (AM) is a prevalent technology that is currently limited to machines that only operate within enclosed volumes. Such an enclosed volume limits the size of additively manufactured parts. However, if the printing happens outside of the printer, the print-volume is unbounded. Furthermore, in adjacent printing, one could create and join a part at its eventual assembly — thus removing a step in the manufacturing process. A mobile machine like this would allow the operator to print infinitely sized parts on any surface and printing parts directly onto the work surface removes the post-print assembly costs and times. Robotic manipulators are commonly used in research applications of adjacent multi-axis 3D printing due to the accessibility and kinematic freedom of these machines. But, while robotic manipulators can print in many different orientations and on a variety of surfaces, the lack of rigidity in the rotational joints and complex inverse kinematics associated with them means that printing with these machines can be overly complex and yield imprecise results. This paper examines both the hardware and software design challenges in creating a novel multi-axis 3D printer designed specifically to print onto arbitrary surfaces directly adjacent to itself. Kinematics and joint rigidity are carefully examined in order to design an optimal configuration. Given the chosen kinematics, the computational systems needed to control the printer are presented in detail including calibration, part slicing, and toolpath creation.
Review of Recent Methods and Learning Techniques in Prediction of Tool Life
Authors: Izdihar Ulfa Fairuz , Patel Brijesh , Huang Zih Fong , Yeh Chih-Ho , Lin Po Ting 
Published: September 2023

Abstract
The reliability of the cutting tool is a key factor in accomplishing the objective of the machining processes. The methods and learning techniques to predict the tool life have been studied for several decades on basic machine tools, including milling, turning, and also drilling as a part of a smart manufacturing system. The current state of the cutting tool is used for modeling the tool’s life. The undesired condition throughout machining, such as excessive vibration, will affect the tool life directly due to the tool failure, including chatter, wear, and breakage. The prediction of tool life using Tool Condition Monitoring Systems (TCMS) is fundamental for manufacturing productivity and also cost-effectiveness. It is implemented by using advanced sensors for data acquisition and determining the process parameters as well as vibration, sound, and cutting force are collected, either directly or indirectly. Signal processing methods and Artificial Intelligent (AI) classifiers are used to obtain the current state of cutting tools during machining processes. This paper reviews various methods and learning techniques of TCMS in predicting the tool life for different machining processes. An experimental tool life prediction was performed by using K-mer signal recognition as feature extraction for sound signal data collected by the microphone for the CNC turning process on alloy steel. Then, Support Vector Machine (SVM) is used to show the accuracy of the method. The experimental results have shown that it is feasible to apply K-mer signal recognition as feature extraction and SVM as machine learning to predict the tool life with 95% accuracy.
Semi-Automated Soft Robotic Stamp Transfer Machine for Van Der Waals Heterostructure Device Assembly
Authors: Ahn Seong-Hyo , Chavarria Juan , Mu Haoxuan , Liao Yifeng , Warner Jamie , Zhou Lei 
Published: September 2023

Abstract
Monolayer materials can be vertically stacked into artificial solids, known as the van der Waals heterostructures (vd-WHs) [1], to realize a new class of ultrathin optoelectronic, electronic, and quantum devices, which have significant potential to revolutionize the field of nanoelectronics and impact a wide range of application areas including transparent displays, sensor arrays, and logic and memory circuits. However, today’s assembly of vdWH devices is still primarily through manual manipulation, which lacks the precision and repeatability needed for the scalable manufacturing of wafer-scale vdWH device arrays outside a research setting. Aiming to enable the automated, scalable, and repeatable manufacturing of vdWH device arrays, this paper presents the design, prototyping, and preliminary tests of a novel semi-automated soft-robotic stamp transfer system for thin-film materials. The system uses a dry elastomer stamp with its adhesion controlled by temperature and peeling speed for material transfer. A combination of electromagnetic and pneumatic actuation is used for the soft-robotic stamp to realize a gentle and uniform pressing of the stamp over the material. An optical microscope, force sensors, and temperature sensors are integrated to enable instrumentation of the transfer process. Preliminary experiments were conducted using our system to conduct for exfoliated graphite transfer. Test results demonstrate the reliable and repeatable transfer of 2D crystal flakes, which show promise to enable the deterministic and scalable assembly of vdWH-based device arrays at wafer scale.
Data Requirements for Digital Twins in Additive Manufacturing
Authors: Feng Shaw C. , Jones Albert T. , Shao Guodong 
Published: September 2023

Abstract
The number and types of sensors used to monitor additive manufacturing (AM) processes and parts in real time are growing. The emerging digital twins (DTs) associated with the data collected by those sensors and the functions that use that data as inputs are becoming increasingly important research topics. There are fast growing demands across several industry sectors to develop software tools that can create, fuse, and measure both AM DTs and digital threads across the entire AM part lifecycle. While some tools are available, they are not well correlated to part-qualification functions in that lifecycle. The accuracy of the DT tools, the fidelity of their inputs, and the ability to create digital threads are still open research questions. The goal of this paper is to identify the data requirements and technical barriers that are limiting the ability to qualify real AM parts using DTs and digital threads. The data requirements can be used as a guide for AM users to create both. This paper uses laser-based powder bed fusion AM for metals as a case study to help identify those requirements and create those digital twins and threads.
Normalizing Flows for Intelligent Manufacturing
Authors: Russell Matthew , Wang Peng 
Published: September 2023

Abstract
Monitoring machine health and product quality enables predictive maintenance that optimizes repairs to minimize factory downtime. Data-driven intelligent manufacturing often relies on probabilistic techniques with intractable distributions. For example, generative models of data distributions can balance fault classes with synthetic data, and sampling the posterior distribution of hidden model parameters enables prognosis of degradation trends. Normalizing flows can address these problems while avoiding the training instability or long inference times of other generative Deep Learning (DL) models like Generative Adversarial Networks (GAN), Variational Autoencoders (VAE), and diffusion networks. To evaluate normalizing flows for manufacturing, experiments are conducted to synthesize surface defect images from an imbalanced data set and estimate parameters of a tool wear degradation model from limited observations. Results show that normalizing flows are an effective, multi-purpose DL architecture for solving these problems in manufacturing. Future work should explore normalizing flows for more complex degradation models and develop a framework for likelihood-based anomaly detection. Code is available at https://github.com/uky-aism/flows-for-manufacturing.
Tunable Stiffness in Material Extrusion of Thermoplastic Urethane
Authors: Van Domelen Ryan , Le Duy , Broman Andrew , Hawley Sandra , Wentz John 
Published: September 2023

Abstract
Thermoplastic polyurethanes (TPU) are increasingly being used in additive manufacturing design due to their abilities to exhibit extreme elastic properties under an applied stress. In this work, processing parameters in material extrusion (ME) of TPU filaments were investigated to provide a model for component stiffness as a function of the printing conditions. This model allows designers to target processing parameters and conditions to meet constraints of stiffness within flexible components. In this study, a full factorial design of experiments methodology was employed to provide a systematic method for experimentally analyzing the influence and effect of number of outer shells, infill percentage, and infill pattern on compressive strength. Based on the data collected from these experiments, a statistical regression model is presented that can be used to predict rigidity in TPU parts manufactured through material extrusion. Based on the results of the initial model, additional investigations were carried out into impact of number of infill line intersections, exterior corners, and TPU hardness (shore 85A and 95A). It was found that shore hardness and infill percentage had the largest impact with respect to stiffness but that all factors were significant and the only interaction not significant at α = 0.05 was the two-factor interaction of infill pattern and number of external shells. The effect of exterior corners were highlighted due to their additional stiffness as they essentially act as thicker columns in the perimeter of the parts.
Computational Modeling of Nanosecond Laser Ablation of Ti-6Al-4V
Authors: Shim Sally , Aguilar Lesly , Ma Jianfeng , Jahan Muhammad 
Published: September 2023

Abstract
Ti-6Al-4V is an alpha-beta titanium alloy having excellent strength, low modulus of elasticity, high corrosion resistance, good weldability and heat treatability. Consequently, it is the most commonly used titanium alloy with a wide use for applications in aerospace, automotive, and biomedical industries. However, Ti-6Al-4V is very difficult to be machined using traditional manufacturing processes. Laser machining is one nontraditional manufacturing process to machine Ti-6Al-4V. However, the laser-machined surface usually experiences phase transformation during the laser machining process because of the heating and cooling in laser machining. In this paper, the commercial Finite Element Method (FEM) software package ABAQUS is employed to assess the effect of nanosecond laser machining conditions and operating temperature on crater size, phase transformation in the Heat Affected Zone (HAZ), and residual stress distribution in laser machining of Ti-6Al-4V. The energy levels: 60mJ, 121mJ, 201mJ, 309mJ, 407mJ, 621mJ, 667mJ were tested. For the predictive modeling, three energy levels: 1000mJ, 2000mJ, and 4000mJ were tested. The phase transformation of Ti-6Al-4V at different temperatures was investigated to see material property change. The temperature used were: 200°C, 400°C, 600°C, 800°C, and 100C. Phase transformation kinetics is used to model the phase transformation of Ti-6Al-4V in laser machining process and rule of mixtures is employed to describe flow stress. Based on the results, the increasing the temperature will induce more HAZ. This research can provide insightful guidance for laser machining of Ti-6Al-4V.
A Study of the Laser Powder Bed Fusion Manufactured Surface Roughness Prediction and Optimization Based on Artificial Neural Network
Authors: Yan Dongqing , Lee Eddie Taewan , Pasebani Somayeh , Fan Zhaoyan 
Published: September 2023

Abstract
Laser powder bed fusion (L-PBF) is one of the most popular additive manufacturing methods in metal additive manufacturing. The surface roughness of the L-PBF manufactured parts is significantly influenced by major process parameters adopted, such as laser power, laser scanning speed, laser hatch space, and layer thickness of powder. This paper presented a machine-learning approach to determine the control parameters for reducing the surface roughness of the L-PBF products. In this study, a three-level full factorial experiment was designed with four major process parameters to examine the corresponding influence of surface roughness. The roughness of the fabricated samples was acquired by a laser profilometer. An artificial neural network model was trained to establish the relationship between the controllable parameters and finished surface roughness. The trained model was validated through additional experiments. The study provided an effective model for predicting the surface quality of L-PBF fabricated parts and a guideline for the process parameter selection.