bluuebunny/crossref_metadata_2025 huggingface dataset:
Nusselt Number Dependence on Aspect Ratio and Rayleigh Number: A Numerical Study of Rayleigh-Benard Instability
Authors: Siddiqui Wajeeha , Abbas Zafar , Akhtar Imran , Khalid Muhammad Saif Ullah
Published: September 2022

In this paper, Rayleigh-Benard Convection (RBC) is investigated in a two-dimensional vertical cavity with the horizontal temperature gradient for Rayleigh numbers (Ra) up to 20000. The velocity and temperature fields along with the Nusselt number (Nu) are examined and discussed for various cases of aspect ratios (AR); 1 ≤ AR ≤ 100, along with examining the critical aspect ratio (ARcr). It is found that in the case of horizontal temperature gradient, convection starts at critical Rayleigh number ≈ 370. It is also observed that the fluid motion is initially restricted to wall proximity, but gradually as Ra increases, convection covers the whole cavity, including the core zone. Our numerical simulations show that as the AR increases beyond 13, for Ra = 6500 instabilities are observed in the cavity. Soon afterwards, secondary cells are formed and tend to decrease in number as Ra increases until the flow becomes fully turbulent. This computational study demonstrates that laminar and transition regimes get contracted with an increasing AR. Profiles of the averaged Nu for different AR are also provided on log Ra-log Nu map. The key finding of this research relates to the dependence of Nu on AR and Ra.
Performance Analysis and Parametric Study of Vortex Whistle
Authors: Li Ang , Chen Jun , Awan Jordan A. , Eddins David A. , Awan Shaheen N.
Published: September 2022

A vortex whistle is a low-cost but accurate flow meter. As the airflow passes through the whistle, a tone is produced which has a frequency proportional to the inlet air flow rate. Recent investigations using vortex whistles have focused on the use of this relationship to quantify aspects of respiratory function. Despite promising results, there is a lack of understanding of the physical mechanisms underlying vortex whistle function. In the present study, combined CFD and CAA method is applied to investigate the performance of the vortex whistle. A simplified vortex whistle model is simulated by LES with Smagorinskty model to predict the flow field within and outside the whistle, and then Ffocus-Williams and Hawkings analogy is used for aeroacoustic simulation to predict the acoustic response of the vortex whistle and the frequency and level of the signature tones. The result indicates that the spectral peaks are generated by the cylindrical cavity of the vortex whistle. The whistles with different heights and diameters of cylindrical cavity are also simulated to optimize the design. The results show that the height does not affect the harmonic frequency significantly; with the decrease of the diameter, the harmonic shifts to a higher frequency. It is beneficial for low flow rate cases; in addition, an optimal design exists which presents the largest signal-to-noise ratio. The simulation results and analysis in the present study help us determine which part contributes the signature peak, and increased SNR of the acoustic output by design optimization will allow for improved frequency-to-flow mapping and volume estimates during respiratory tasks such as the elicitation of vital capacity.
Modeling Intricate Unsteady Flow Field Development of a Self-Contained Novel Entry, Descent, Landing and Locomotion Planetary Exploration Module
Authors: Nordmann Alexandra , Blackman Trinity , Bayandor Javid
Published: September 2022

In the search for innovative concepts to explore extraterrestrial bodies, work towards modeling a fresh self-contained entry, descent, landing and locomotion (EDL-L) system, developed by CRASH Lab, was conducted. The system designed for atmospheric entry requires that the planetary aerodynamics are utilized on the descent to the planet’s surface to stabilize and land safely on the ground. The multifunctionality of this concept lies in the ability to contain all necessary landing configurations in one system. However, this requires extensive modeling to understand its interaction with the planetary atmosphere and environment during all phases of entry, descent, and landing. As the system has an open frame architecture for landing, it causes an intricate unsteady fluid flow to develop around the system as it falls. Being able to better understand how the flow evolves and how it affects the descending vehicle will allow for a more controlled EDL sequence. The free fall landing phase of the model has been considered at Venus Surface Conditions.

The work reported is centered around non-rotational descent, which will be extended in the future to rotational and multi-directional landing considerations, as well as verification studies with wind tunnel trials. The paper reports on efforts toward developing an ultimate predictive methodology and its verification and validation studies to assess the self-contained EDL-L system. One of the main objectives of the study is to achieve a more comprehensive understanding of the impacts that planetary conditions may have on the safe entry, descent, landing and operability of the concept vehicle on any celestial bodies with atmosphere.
The Impact of Scaling on the Gap Flow Rate of Radial Fans
Authors: Fritsche Manuel , Epple Philipp , Delgado Antonio
Published: September 2022

Due to the gap between the impeller and the casing of a radial fan a gap flow rate occurs, leading to a higher flow rate through the impeller than through the fan. The gap flow rate has an impact on the performance of the radial fan and must be considered in the design process. There is only little known from the literature about the gap flow rate. Furthermore, as to the knowledge of the authors, no information on the effect of scaling on the gap flow rate is reported in literature.

In this work a systematic study of the effect of scaling on the gap flow rate has been performed. For this first a baseline radial fan has been designed. This model and its full characteristics have been computed with the commercial Navier-Stokes solver Ansys CFX. Then the impeller width, the outer diameter, the inner diameter, the gap width, and the rotational speed were varied. These designs have been simulated with the commercial Navier-Stokes solver Ansys CFX. The effect of these parameters on the performance characteristics pressure and efficiency and its impact on the gap flow rate were evaluated.

Based on these results, a dimensional analysis model for the gap flow rate have been developed and evaluated with the full characteristics and the main dimensions of all simulated models. It has been shown that the gap flow rate can be estimated based on data that is typically available, such as pressure, volume flow rate, speed, outlet diameter, outlet width, gap width, fluid density and dynamic viscosity. The developed dimensional analysis model was validated with a test case. These results are presented and discussed in detail.
Numerical Investigation of Automotive Paint Oven for Improving the Thermal Efficiency
Authors: Pendar Mohammad-Reza , Páscoa José Carlos , Lima Rui
Published: September 2022

This study conducts a comprehensive numerical evaluation of conjugate heat transfer in an automotive paint oven to optimize the performance and increase the overall energy efficiency. Computational fluid dynamic (CFD) analysis of parameters in an automotive paint oven and on the coated vehicle surface is essential to achieve better paint quality and manufacturability, although the complicated geometry of vehicles, transient nature of the procedure, varying scales of the flow, and the tightly coupled conjugate heat transfer make the modeling difficult. An efficient computational algorithm, under the framework of the OpenFOAM package, using the Large Eddy Simulation (LES) turbulence model is implemented to numerically model the unsteady heated airflow behavior in the paint curing process. The conjugate heat transfer solver, chtMultiRegionFoam, is validated by heat sink case examination as heat transfer benchmark and then employed for the oven modeling. According to simulation results, the applied low-cost optimization of the intake hot flow rate and small oven geometry variation achieved significant energy efficiency improvement. During the optimization, the fluid dynamic characteristics, e.g., the mean temperature, velocity and streamline patterns, across all six zones of the oven, as well as the temperature and velocity map on a Body in White (BiW), are examined. The optimized arrangement and position of nozzles and panels for transient heat transfer processing of curing along the oven is described. Increasing the heated air provided by the panels on the lower half of the oven improves airflow circulation, resulting in a significant increment in the computed car body temperature.
Using Iterative Learning Control to Improve the Accuracy of Desktop Fused Deposition Modeling Printers: An Experimental Case Study
Authors: Funke Lawrence , Opara Matthew
Published: September 2022

Additive manufacturing (AM) sits poised to make a large impact on the manufacturing sector. Fused deposition modeling (FDM), a type of AM, while versatile, and increasingly appearing in full production systems, has performance limitations in certain geometries, such as arcs and holes. This is especially true for the desktop setups common in College Maker Spaces and other prototyping environments. For these use cases, it is critical to obtain accurate parts quickly, yet often difficult, diminishing the value of using FDM, whether it be to prototype new designs, make final parts, or anything in between. Iterative Learning Control (ILC) has been applied to robot control, plastic extrusion, and other similar processes where disturbances to a system are present and relatively constant, but difficult to model and correct. Since desktop printers perform repetitive tasks subject to nearly constant disturbances that induce inaccuracies, a natural research question arises: can ILC be used to allow desktop printers to learn these inaccuracies and account for them, allowing such printers to create more accurate and useful parts for the average prototyping user?

Details on the printer, a LulzBot Taz 6, and the scanner, an Einscan 3D Scanner, being used to answer this question are first presented with some baseline data to establish the scanner’s nominal accuracy. Subsequently, a simple bounding box approach was developed and tested where only the part’s length, width, and height were monitored and adjusted. This approach determined an error metric for a scalene triangular prism by determining the length, width, and height of a box that bounds the shape. The ILC algorithm used this error metric to generate a new file to print for the next iteration, thus creating parts that became more and more accurate. While this approach exhibited some success, it cannot account for larger, more common issues such as warping (where shrinking occurs as the plastic cools over time causing bending or bowing in the part), or a hole being geometrically inaccurate compared to the desired diameter. To address these concerns, a grid approach was developed where the cardinal dimensions had a grid overlaid so that points along each dimension could be checked and adjusted in subsequent prints to account for such issues. This approach was applied to rectangular bars with relative success. The overall dimensional accuracy (e.g. length, width, height) was not significantly improved, however, warping along the length of the bar was significantly reduced. A similar approach for more complex geometries, (i.e. holes and arcs) is currently under development. Initial thoughts and plans are presented as concluding remarks. Using ILC to account for common issues with desktop FDM printers could enable higher quality parts to be made, without a substantial investment in higher-grade equipment.
Non-Planar Slicing Method for Maximizing the Anisotropic Behavior of Continuous Fiber-Reinforced Fused Filament Fabricated Parts
Authors: Chen Chih-Yu , Freißmuth Leonard , Altug Suat Mert , Colin David , Feuchtgruber Matthias , Drechsler Klaus
Published: September 2022

Fused filament fabrication (FFF), a type of extrusion-based additive manufacturing method, has proven its suitability for the production of highly complex components without costly tooling. However, traditional FFF systems are restricted to planar layer deposition, which results in poor surface smoothness and a reduction in strength and stiffness along the layer-stacking direction. Recent advancements in the FFF process have made it possible to reinforce and strengthen the printed parts with continuous fibers, which significantly increases the material’s anisotropy. Therefore, non-planar printing is necessary to optimize the anisotropic material behavior. This paper proposes a non-planar slicing method for optimizing the performance of continuous fiber-reinforced FFF parts printed using a 6-DOF industrial robot. The computational framework allows for the deposition of material on non-planar surfaces along the direction of the largest principal stress obtained from a finite element analysis following topology optimization. Three parts were successfully sliced and printed in a non-planar manner to generate stress-oriented toolpaths for continuous fiber-reinforced FFF using a 6-DOF robotic arm.
Multiplexed 3D Printing of Thermoplastics
Authors: Cleeman Jeremy , Bogut Alex , Mangrolia Brijesh , Ripberger Adeline , Maghouli Arad , Kate Kunal , Malhotra Rajiv
Published: September 2022

Extrusion-based additive manufacturing of large thermoplastic structures has significant emerging applications. The most popular approach to economically achieving such 3D printing is to increase the polymer flow rate along with the layer height and line width. However, this creates a fundamental compromise between the achievable geometric fidelity and the printing throughput. We explore a Multiplexed Fused Filament Fabrication (MF3) approach in which an array of FFF extruders concurrently prints different sections of the same part using small layer heights and line widths. Mounting all the extruders on one cartesian gantry without individual control of each extruder’s motion enables simple machine construction and control. 3D geometric complexity is realized by rastering the extruder array across the smallest rectangle bounding each 2D layer and by spatially specific deposition via “dynamic” filament retraction/ advancement in the extruders. The dynamic moniker is because, unlike conventional single extruder FFF, the extruder array does not stop during dynamic filament retraction/advancement. This achieves higher throughput at greater resolution without material-intensive overprinting and machining, geometrically-limited throughput of the dual-extruder strategy, cost and geometric limitations of robot-based multiplexing, and the complexity and geometric limitations of previous gantry-based multiplexing efforts. Our experiments reveal the parameters that affect dynamic retraction and advancement, and show a previously unknown coupling between the efficacy of dynamic filament retraction and dynamic filament advancement. We create part-scale thermal simulations to model temperature evolution in the part under the action of multiple concurrently acting extruders, revealing a unique temperature history that can affect bonding and mechanical properties. We show that MF3 can enable resilience to extruder failure by allowing other extruders to take over part fabrication while the damaged extruder is being replaced. We also demonstrate that MF3 enables flexibility in part scale and geometry, i.e., the ability to make multiple smaller parts of similar or distinct geometries in one production run and lesser number of larger parts of similar or distinct geometries in the next production run. Finally, we quantitatively analyze the future potential of MF3 to achieve similar or greater throughput than state-of-the-art Big Area Additive Manufacturing while significantly enhancing the geometric resolution.
The Development and Validation of a Novel Thread Forming Fastener for High Strength Steel Applications
Authors: Ruszkiewicz Brandt J. , Breidenbaugh Eric , Simpson Gene
Published: September 2022

Automotive OEMs continue to lightweight their fleets to increase fuel economy and electric drive range to meet government requirements and consumer expectations. This push towards lightweighting has led to increasing use of advanced high strength steels in both car body and safety critical systems, such as seats. These steels present different challenges for traditional joining technologies, especially thread forming fasteners. Thread forming fasteners are externally threaded fasteners like machine screws that are driven into a pilot hole where they form their own threads and then tighten down to secure the target joint. Traditional thread forming fastener designs struggle to form threads into steels with tensile strengths greater than 600MPa. This paper utilizes simulation coupled with experimental validation to evaluate one of the most popular traditional thread forming fasteners that is currently used in low strength steel to determine why it fails to form threads in high strength steels. A new thread forming fastener design targeted for high strength steels is developed through simulation. The new fastener design is manufactured and evaluated across three different screw diameters, and six different high strength steels (> 600MPa tensile strength) using the same heat treatments, materials, and platings as traditional thread forming fasteners. Validation tests for the new fastener include drive-to-failure to identify a target tightening torque and clamp load at the target tightening torque. Both tests are conducted across a hole size tolerance window. It is shown that the new thread forming fastener design performs well in steels up to 1200MPa without any special heat treatments but can benefit from specialized heat treatments in steels of tensile strength 1200MPa or higher.
Synthesis of Nano-Dots and Lines by Glancing Angle Deposition With Corrals
Authors: Qu Chuang , McNamara Shamus , Walsh Kevin
Published: September 2022

This paper introduces using GLancing Angle Deposition (GLAD) with corral seeds for synthesizing nanodots and nanolines. GLAD is an advanced physical vapor deposition technique for creating three dimensional nanostructures. GLAD is commonly combined with pre-determined seeds on the substrate to create periodic nanofeature arrays; the seeds are usually artificial nucleation sites to rearrange the deposition patterns. However, the concept of corral seeds is different: the incident vapor will be depositing both on and inside the sacrificial layer of the corrals that consist various shapes; the desired nanostructures are grown from the overlapped deposition areas inside the corrals while the substrate rotates, depending on the shape of the corrals, and eventually will be remaining on the substrate when the sacrificial layer of the corral seeds is removed. The thickness of the sacrificial corrals along with the incident angle of the vapor define the shadow areas and deposition areas inside the corrals on the substrate. In this paper, three types of corrals are introduced: circular corrals, dumbbell corrals, and line corrals. The different nanofeatures of nanodots, limited-length nanolines and wafer-length nanolines created by different shaped corrals are presented. The fabricated nanodots and nanolines are potentially used in various optical and sensing applications. The two-step fabrication process of preparing corrals and GLAD provides numerous benefits for the synthesis of the nanofeatures.
Evaluation of Data-Driven Models in Human-Robot Load-Sharing
Authors: Nguyen Vinh , Marvel Jeremy
Published: September 2022

Human-robot load-sharing is a potential application for human-robot collaborative systems in production environments. However, knowledge of the appropriate data-driven models for this application type is limited due to a lack of physical real-world data and validation metrics. This paper describes and demonstrates a load-sharing testbed for evaluating data-driven models in a human-robot load-sharing application. Specifically, the testbed consists of a single operator and single robot relocating a payload to a desired destination. In this work, the operator initially communicates to the robot using audio feedback to initiate and alter robotic motion commands. During the payload relocation, human, payload, and robot state data are recorded. The measurements are then used to train three data-driven models (neural network, naïve Bayes, and random forest). The data-driven models are then used to transmit movement commands to the robot during human-robot load-sharing without the use of audio feedback, thus improving robustness and eliminating audio signal processing time. Evaluation of the three data-driven models shows that the random forest model was demonstrated to be the most accurate model followed by naïve Bayes and then the neural network. Hence, the results of this study provide novel insight into the types of data-driven models that can be used in load-sharing applications in addition to development of a real-world testbed.
The Effect of Composition, Geometry and a Novel Tool Design on Metal Recovery During Aluminum Process Scrap Remelting
Authors: Liao Jiankan , Sharma Ashvin , Cooper Daniel
Published: September 2022

Aluminum recycling requires less energy and releases fewer greenhouse emissions than primary production from naturally occurring ores; however, a significant fraction of the furnace charge is lost to dross generation during remelting. In this article, we use an electric furnace to remelt clean aluminum sheet and machining chip process scrap of varying thickness, surface roughness, and composition. The metal recovery results show that magnesium-containing alloys (e.g., 2xxx, 5xxx, 6xxx, and 7xxx alloys) accelerate dross generation and lower metal recovery. This is likely due to magnesium having a higher reactivity than aluminum, with the magnesium content detected in the dross (using Energy-dispersive X-ray spectroscopy) greater than the magnesium content in the alloy. Metal recovery decreased when remelting thinner scrap. Metal recovery for clean machining chips was lower than for aluminum sheet scrap of the same thickness and composition. This disparity was likely due to the greater surface roughness of the machining chips, which will increase the surface area for oxidation and potentially the wetting of the oxide by the Wenzel effect. The decreased metal recovery for scratch brushed aluminum sheets confirmed the effect of surface roughness. Subsequently, a “squeeze” cutting tool was designed and manufactured, which smooths the otherwise rough back-side of the machining chips. These smoother machining chips exhibited increased metal recovery during remelting.
Physics-Informed Artificial Intelligence for Temperature Prediction in Metal Additive Manufacturing: A Comparative Study
Authors: Ghungrad Suyog , Gould Benjamin , Wolff Sarah , Haghighi Azadeh
Published: September 2022

Prediction of the temperature history of printed paths in additive manufacturing is crucial towards establishing the process-structure-property relationship. Traditional approaches for predictions such as physics-based simulations are computationally costly and time-consuming, whereas data driven approaches are highly dependent on huge, labeled datasets. Moreover, these labeled datasets are mostly scarce and costly in additive manufacturing owing to its unique application domain (mass customization) and complicated data-gathering stage. Recently, model-based or physics-informed artificial intelligence approaches have shown promising potential in overcoming the existing limitations and challenges faced by purely analytical or data driven approaches. In this work, a novel physics-informed artificial intelligent structure for scenarios with limited data is presented and its performance for temperature prediction in the selective laser melting additive manufacturing process is compared with one of the state-of-the-art data driven approaches, namely long short-term memory (LSTM) neural networks. Temperature data for training and testing was extracted from infrared images of single-track layer-based experiments for Ti64 material with different combinations of process parameters. Compared to LSTM, the proposed approach has higher computational efficiency and achieves better accuracy in limited data scenarios, making it a potential candidate for real-time closed-loop control of the additive manufacturing process under limited and sparse data scenarios. In other words, the proposed model is capable to learn more efficiently under such scenarios in comparison to LSTM model.
Bidirectional Bending of Thin Metals With Femtosecond Lasers
Authors: Tessmann Bryce , Li Kewei , Zhao Xin
Published: September 2022

Lasers have a wide range of manufacturing applications, one of which is the bending of metals. While there are multiple ways to induce bending in metals with lasers, this paper examines laser peen forming with femtosecond lasers on thin metals of 75-micrometer thickness perpendicular to the laser. The effects of multiple parameters, including laser energy, scan speed, scan pitch, and material preparation, on the bend angle of the metal are investigated. The bend angles are generated in both concave and convex directions, represented by positive and negative angles, respectively. While it is possible to create angles ranging from 0 to 90 degrees in the concave direction, the largest average convex angle found was only −26.2 degrees. The positive angles were created by high overlapping ratios and slow speeds. Furthermore, the concave angles were made by a smaller range of values than the convex angles, although this range could be expanded by higher laser energy. The positive angles also had a higher inconsistency than the negative angles, with an average standard deviation of 6.8 degrees versus an average of 2.6 degrees, respectively. The characterization of bending angles will allow for more accurate predictions, which will benefit traditional metal forming applications and more advanced applications such as origami structures with metal.
Comparison of Human Skeleton Trackers Paired With a Novel Skeleton Fusion Algorithm
Authors: Flowers Jared T. , Wiens Gloria J.
Published: September 2022

The onset of Industry 4.0 brings a greater demand for Human-Robot Collaboration (HRC) in manufacturing. This has led to a critical need for bridging the sensing and AI with the mechanical-n-physical necessities to successfully augment the robot’s awareness and intelligence. In a HRC work cell, options for sensors to detect human joint locations vary greatly in complexity, usability, and cost. In this paper, the use of depth cameras is explored, since they are a relatively low-cost option that does not require users to wear extra sensing hardware. Herein, the Google Media Pipe (BlazePose) and OpenPose skeleton tracking software packages are used to estimate the pixel coordinates of each human joint in images from depth cameras. The depth at each pixel is then used with the joint pixel coordinates to generate the 3D joint locations of the skeleton. In comparing these skeleton trackers, this paper also presents a novel method of combining the skeleton that the trackers generate from each camera’s data utilizing a quaternion/link-length representation of the skeleton. Results show that the overall mean and standard deviation in position error between the fused skeleton and target locations was lower compared to the skeletons resulting directly from each camera’s data.
Pass-Wise Tool Wear Prediction in Turning Based on Long-Short Term Memory Algorithm Using Current Signals
Authors: Chinomona Benvolence , Chung Chunhui , Wang Po-Chieh
Published: September 2022

A novel tool wear predictive model was developed based on the current signals in this study. The system adapts to different part geometry with accurate prediction of the tool wear during the operation. The current sensor was utilized presenting a practical and better choice for tool wear monitoring which is inexpensive and no need to be attached to the working table or spindle. To avoid interruptions during the machining process, the tool wear was only measured at the end of the operation. The Long Short-Term Memory model was used to develop the tool wear prediction system. The tool wear prediction results indicate 23.92% and 36.41% average error for all the testing samples after 1/3 of the operations for profiling and straight turning, respectively. When the tool wear prediction was carried out after 2/3 of the operations, excellent results are observed with 6.15% error for profiling and 9.44% error for straight turning. The prediction results at the end of the operation shows 0.18% and 0.68% error for profiling and straight turning. The performance of the model using the current sensor shows that the model can predict the tool wear with less than 10% error after 2/3 of the turning operation without interfering with the turning process.
3D Printing of Eco-Friendly Artificial Martian Clay (JMSS-1) for In-Situ Resource Utilization on Mars
Authors: Ghosh Avishek , Favier Jean-Jacques
Published: September 2022

Permanent settlement on the surface of planets like the Moon and Mars is anticipated to be beneficial for long-duration exploration missions. The space agencies have developed several plans, along with other commercial partners, to build operational stations on such planetary bodies, which will be economical and resourceful to execute further missions into deep space. Therefore, the real integration of an advanced manufacturing technique is essentially a matter of further research to design and deliver critical subsystems utilising in-situ resources available on the surface of Mars. The Additive Manufacturing (AM) technique is becoming increasingly promising for developing complex structures by depositing multiple consecutive layers, unlike specific moulds required in the conventional manufacturing process. Therefore, to assess the feasibility of 3D printing with local resources technically, a recently developed artificial Mars soil simulant known as Jining Martian Soil Simulant (JMSS-1) has been processed to formulate clay useful for the extrusion 3D printing process. The developed Martian clay has been fabricated, characterised, and its dielectric properties measured at high frequencies for the first time. A stable aqueous clay has been developed containing less organics (< 10 wt% versus typically 30–40 wt%), which is amenable to resource-efficient 3D printing. A range of solid and porous structures of various shapes and sizes have been fabricated using a custom-developed material extrusion 3D printing system. The 3D printed artificial Martian clay sintered for 2 hours at 1100°C exhibited relative permittivity (εr) = 4.52, dielectric loss (tanδ) = 0.0015, quality factor (Q × f) = 7039 GHz. TCf = −19; and demonstrated similar properties at higher frequencies. This work demonstrates the progress in clay additive manufacturing and illustrates the potential to deliver components with functional properties through a “Powder to Product” holistic approach that can support long-term space exploration by utilising local resources available on Mars.
Layer-Wise Melt Pool Temperature Evolution in Laser Powder Bed Fusion: An Experimental Study Using a Single Camera Based Two-Wavelength Imaging Pyrometry
Authors: Vallabh Chaitanya Krishna Prasad , Hinnebusch Shawn , To Albert C. , Zhao Xiayun
Published: September 2022

In metal additive manufacturing (AM) the layer-wise thermal history is crucial for its effect on the print part properties, such as, microstructure, porosity, and mechanical strength. Literature studies for evaluating the part thermal history are typically based on in-situ infrared thermography and thermal modeling. However, the effect of melt pool temperature on the part thermal history has not been widely studied. In this preliminary work, for the first time we present a large and comprehensive in-situ monitored, layer-wise melt pool temperature evolution data for laser powder bed fusion (LPBF) AM. The melt pool temperature is evaluated using an in-house Single Camera Two-Wavelength Imaging Pyrometry (STWIP) system. The melt pool temperature evolution was evaluated for three different prints with different inter-layer-times and print heights. The melt pool temperature history trends presented in this work are in agreement with literature studies on part-thermal histories. The LPBF process signatures from our STWIP system can help develop more accurate thermal models with the melt pool temperature as the input and the unique capability of the STWIP system to acquire and analyze large amounts data facilitates the development of machine learning models for estimating part properties based on the process signatures.
Acoustic Process Monitoring of Contact Tip to Work Piece Distance in Wire Arc Additive Manufacturing by Random Forest Algorithms
Authors: Hebert Dylan , Thien Austen , Saldana Christopher
Published: September 2022

Wire arc additive manufacturing (WAAM) is a metal additive process that allows for constructing diverse parts using an electric arc and metal wire feed stock. It provides higher deposition rates and larger build areas that make it an attractive technology for industry. An important element of WAAM process control is maintaining specific contact tip to work-piece distance (CTWD). Cumulative errors caused by modeling inaccuracies and thermal conditions can create an increase in CTWD during processing. In the present study, acoustic signals were investigated as a non-invasive form of monitoring this phenomenon and were compared with traditional current-based sensing approaches. Experiments were conducted across a range of controlled CTWD conditions. From the acoustic signals, 35 different statistical features were extracted and machine learning strategies were utilized to correlate them to the CTWD. Random forests composed of 50 trees each were chosen to classify the CTWD into three different levels of CTWD granularity. The ability of random forest algorithms to detect varying levels of CTWD was investigated and the models showed limited performance especially for high granularity CTWD predictions when compared to current sensing approaches. The implications for these results in rapid implementation for high-level process monitoring of process condition are briefly discussed.
Towards a Digital Twin: Simulation and Residual Stress Analysis in Aerospace Composite Structures Assembly
Authors: Lutz Tim , Yue Xiaowei , Camelio Jaime
Published: September 2022

Aerospace composites assemblies/joining demand ultra-high precision due to critical safety requirements, which necessitate adherence to indicators of risk that are often difficult to quantify. This study examines one important indicator, the residual stress that arises as a result of dimensional mismatch between mating components during the composite structures assembly process. Conventional simulations of large components assemblies investigated the process at a local or global scale, but lacked detailed exploitation of multi-layer stress analysis at integrated scale for composite structures. We develop a novel digital twin simulation for joining large composite structures with mechanical fasteners. The digital twin simulation integrates global features and local features for detailed investigation of stresses. We perform a statistical analysis to better understand the numerical properties of residual stresses after the fastening. Goodness-of-Fit tests and normality tests are used to explore the probabilistic distributions of the stresses exceeding a chosen safety threshold. The case study is conducted based on composite fuselage joining. The results show the stresses in composite structures assembly follow extreme value distributions (such as Weibull, Gumbel) rather than the widely used Gaussian distribution. The stresses in joined composite structures differ across layers, which can be attributed to the anisotropic material behavior.
Visualization Error Analysis for Augmented Reality Stereo Video See-Through Head-Mounted Displays in Industry 4.0 Applications
Authors: Yang Wenhao , Zhang Yunbo
Published: September 2022

Under the fourth industrial revolution (Industry 4.0), Augmented Reality (AR) provides new affordances for a variety of applications, such as AR-based human-robot interaction, virtual assembly assistance, and workforce virtual training. The see-through head-mounted displays (STHMDs), based on either optical see-through or video see-through technologies, are the primary AR device to augment the visual perception of the real environment with computer-generated contents through a hand-free headset. Specifically, the video see-through STHMDs process the superimposing of the real environment and virtual contents based on the digital images and output it to users, while optical see-through STHMDs display virtual contents through the optics-based near-eyes display with users’ normal view of the real scene kept. For both types of AR devices, the accuracy of visualization is essential. For example, in AR-based human-robot interaction, the inaccurate rendering of 3D virtual objects with respect to the real environment, will lead to users’ mistaking operations, and therefore, causes an invalid tool path planning result. In spite of many works related to system calibration and error reduction for optical see-through STHMDs, there are few efforts at figuring out the nature and factors of those errors in video see-through STHMDs. In this paper, taking consumer-available AR video see-through STHMDs as an example, we identify error sources of registration and build a mathematical model of the display progress to describe the error propagation in the stereo video see-through systems. Then, based on the mathematical model of the system, the sensitivity of each error source to the final registration error is analyzed. Finally, possible solutions of error correction are suggested and summarized in the general video see-through STHMDs.
Research on Synchronous Control Strategy of Dual-Drive Feed System Based on Fuzzy PID Control
Authors: Wang Ben , Lu Hong , Liu Qi , Wang Shaojun , Pan Hengchen , Dai Jiashun
Published: September 2022

Dual-drive feed system (DDFS) is widely used in computer numerical control (CNC) machine tools. In the process of machining, it is necessary to ensure the complete synchronization of the two axes of the feed system, otherwise it will affect the machining accuracy and shorten the life of the machine tool. Due to the structure error of the DDFS and the uneven distribution of the load on the two axes in the process of machining irregular workpiece, there are synchronization errors between the two axes. Therefore, it is of great significance to reduce the synchronization errors by studying the dual-drive synchronous control strategy. In this paper, fuzzy control is introduced into traditional PID synchronous control strategy. Compared with traditional PID control, fuzzy control has the characteristics of high robustness and high control performance. Firstly, the PID model of single-axis servo feed system is established. Then, the master-slave control strategy is selected as the dual-drive synchronous control strategy and the model of master-slave control strategy based on conventional PID (MSCS-CPID) is established. Next, the fuzzy PID control is introduced into the current loop of the servo feed system and the model of master-slave control strategy based on fuzzy PID (MSCS-FPID) is established. The simulation results of the MSCS-CPID and the MSCS-FPID show that the DDFS under the MSCS-FPID has faster response speed and smaller synchronization errors. Moreover, the DDFS under the MSCS-FPID has better synchronization performance after external interference. Experiment confirmed that the synchronization performance of the MSCS-FPID is better than that of the MSCS-CPID.
Shape Error Analysis in Ultra-Precision Grinding of Optical Glass by Using Motor-Current-Based Grinding Force Monitoring
Authors: Iinuma Naoki , Chen Boshi , Kawasato Tappei , Kakinuma Yasuhiro
Published: September 2022

4K and 8K technologies are attracting attention in optical industries. The most important mechanical element to enhance the imaging performance is the aspherical lens requiring higher surface quality and higher form accuracy. Currently, the production process of optical lenses consists of brittle-mode grinding and pro-longed polishing process, which play a role of shaping the form and producing the fine surface, respectively. However, this process is not considered to be suitable for manufacturing such higher-quality lenses for 4K and 8K imaging devices because a required form accuracy could not be ensured, and the polishing time gets longer. To enhance the form accuracy and production efficiency, application of ductile-mode grinding is expected to reduce polishing amount. However, the shape error generated by the ductile mode grinding is not clear. Therefore, the purpose of this research is to analyze the relation between the shape error and the grinding force estimated from motor-current in the grinding machine. The motor-current acquisition system in all translational axes and the work spindle is constructed and implemented into a 4-axis ultra-precision aspherical machine. The grinding force in each axis is derived by subtracting the motor current during non-grinding previously obtained in air-grinding test from the current during grinding. Firstly, the behavior of the motor current in each axis is investigated from the viewpoint of repeatability and position dependency. While the periodic fluctuation of the motor current affected by the influence of permanent magnet in the linear motor is confirmed, it shows high repeatability at each position. This result indicated that grinding force is easily calculated from the motor current with less uncertainty. Then, influence of grinding condition in the range of ductile mode grinding on the shape error is analyzed by monitoring the motor current. Toward the outside of the workpiece, the shape error gradually increases with the increase of motor current, which means larger grinding force at the outer side causes the deformation of the resin grinding wheel.
A Novel Welding Method for Repairing Surface Defects of Large-Type Rotary Machinery Based on Line Structured Light Detection
Authors: Fu Hao , Lu Hong , Zhang Yongquan , Wu Zidong , Huang He , Liu Shijie , Wang Shaojun
Published: September 2022

Large-type rotary machinery is the core components of national major projects which is widely used aviation, electric power, metallurgy, energy and construction machinery industries. Surface defects of Large-type rotary machinery such as cracks and pits are usually processed into groove with a certain shape first, and then the processed groove is repaired by manual welding. This manual welding repair method has a low level of automation, and the repair quality of the groove is difficult to guarantee. Therefore, this paper proposes a novel welding method for repairing surface defects of Large-type rotary machinery which uses the Kollmorgen Joint Modular Robot to complete the welding repair of the processed groove. Firstly, the groove point cloud data collected by Line structured light sensor is processed by the designed algorithm to obtain the contour characteristics of the groove. Then, the arrangement of welding pass is completed based on contour characteristics of the groove. Finally, the trajectory of the welding robot is determined by the position of welding pass. The planned trajectory verification is completed on the simulation experiment platform and the result shows the accuracy and reliability of the planned trajectory which has certain theoretical and practical significance for realizing the automation of on-site maintenance of Large-type rotary machinery.
Electric Vehicle Battery End-Of-Use Recovery Management: Degradation Prediction and Decision Making
Authors: Zhao Yixin , Behdad Sara
Published: September 2022

Electric vehicles (EVs) are spreading rapidly in the market due to their better responsiveness and environmental friendliness. An accurate diagnosis of EV battery status from operational data is necessary to ensure reliability, minimize maintenance costs, and improve sustainability. This paper presents a deep learning approach based on the long short-term memory network (LSTM) to estimate the state of health (SOH) and degradation of lithium-ion batteries for electric vehicles without prior knowledge of the complex degradation mechanisms. Our results are demonstrated on the open-source NASA Randomized Battery Usage Dataset with batteries aging under changing operating conditions. The randomized discharge data can better represent practical battery usage. The study provides additional end-of-use suggestions, including continued use, remanufacturing/repurposing, recycling, and disposal; for battery management dependent on the predicted battery status. The suggested replacement point is proposed to avoid a sharp degradation phase of the battery to prevent a significant loss of active material on the electrodes. This facilitates the remanufacturing/repurposing process for the replaced battery, thereby extending the battery’s life for secondary use at a lower cost. The prediction model provides a tool for customers and the battery second use industry to handle their EV battery properly to get the best economy and system reliability compromise.
Hydrogel and Graphene Embedded Piezoresistive Microcantilever Sensor for Solvent and Gas Flow Detection
Authors: Ghosh Dipannita , Rahman Md. Ashiqur , Ashraf Ali , Islam Nazmul
Published: September 2022

Piezoresistive microcantilever sensor is widely used in sensing applications including liquid and gas flow detection. Microcantilevers can function as an embedded system if they are coated with polymers or nanomaterials to improve sensing performance. In this paper, we investigated the performance of piezoresistive microcantilevers (PMC) with and without additional coating. We studied the sensitivity of the PMC sensor after coating it with a three-dimensional porous hydrogel and piezoresistive graphene oxide layer. Hydrogel embedded piezoresistive microcantilever (EPM) showed better results than PMC during solvent sensing application. The resistance change for hydrogel embedded PMC was higher compared to bare PMC by 430% (3.2% to 17%) while detecting isopropyl alcohol (IPA), by approximately 1.5 orders of magnitude (0.19% to 5.7%) while detecting the presence of deionized water. Graphene Oxide coated PMC showed a wider detection range by 30 milliliter/min and 24% better sensitivity than bare PMC during the gas detection experiment. Additionally, we compared the experiment result with COMSOL simulation to develop a model for our embedded PMC sensing. Simulation shows significantly higher deflection of the EPM compared to the bare PMC (66.67% higher while detecting IPA, consistent with the trend observed during the experiment). The facile drop casting-based embedded microcantilever fabrication technique can lead to improved performance in different sensing applications. Our future work will focus on detecting biomolecules by using our constructed embedded systems.