bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Undergraduate Research Study: Effect of Welding Methods and Weld Puddle Manipulation on the Tensile Strength of Welded Joints
Authors: Kopac Jordan , Mahinfalah Mohammad , Schaefer Mathew 
Published: February 2023

Abstract
Benefits of undergraduate research have been documented by many researchers [1, 2]. This paper presents the results of an undergraduate research project conducted over two terms. An experimental study was conducted to determine the effect of specific MIG welding methods and manipulation of the weld puddle on tensile strength of welded joints. The two investigated methods were, “stringer” which is straight and consistent speed weld that has no manipulation of the puddle and “weaving” where the welder is weaving his or her puddle in a variety of different motions, most commonly a cursive ‘e’ or a back-and-forth motion.
Statistical data analysis was performed on the results from the tensile testing to confirm that the variation between the samples is significant enough to conclude that there is a difference between the groups’ means. Additionally, analysis and observations of the samples after polishing and acid etching was completed to see how well each welding method penetrated and fused during the welding process.
Five Key Attitudes for a Successful Co-Op: the Value of Cooperative Education Within an Undergraduate Program
Authors: Schneider Anakin , Iglesias Patricia 
Published: February 2023

Abstract
Cooperative education programs (Co-ops) and internships are becoming increasingly popular within undergraduate engineering curriculums and with students who participate in them. These programs provide real-world experience and practical applications of key concepts that students learn in the classroom.
In a recent internship, both the academic knowledge of basic engineering principles and preconceived knowledge of workplace attitudes were challenged and evaluated. The internship involved facilitating numerous projects focused on streamlining production and working with various levels of management to complete them. In reflecting on the overall internship experience, five key attitudes were realized as being important to both personal and professional successes as an engineering intern. The attitudes are “Respect the Workday,” “Be Confident, Be Humble,” “Respect the Work Environment,” “Let Go of Things You Cannot Control,” and “Make the Experience Your Own!”
This paper will act as a friendly guide to any undergraduate engineering student as they proceed with their first co-op experience and provide important key takeaways and pragmatic examples from this experience that either exemplify or misrepresent the attitudes. The paper will also outline a framework for engineering industry leaders on how to standardize and promote positive work environments that will realize success for students willing to learn fundamental engineering competencies. It will also call for the creation of more undergraduate engineering programs to require cooperative education experiences, which are key to a student’s application of classroom knowledge. The conclusion of this paper is simple — follow the advice, put it into practical use, and enjoy the experience.
Conveyer-Less Matrix Assembly Layout Design to Maximize Labor Productivity and Footprint Usage
Authors: Verma Ankur , Oh Seog-Chan , Wells James W. , Arinez Jorge , Kumara Soundar 
Published: February 2023

Abstract
The automotive industry has used in-line conveyor systems to move vehicles through the general assembly process since the early days of vehicle manufacturing in the 20th century. With products shifting to EVs (Electric Vehicle) and the emergence of AMRs (Autonomous Mobile Robot), there is an opportunity to transform the assembly process into a more flexible conveyor-less matrix-based system. One key development need is how to structure and optimize such an asynchronous system. This paper presents a new methodology for designing a conveyor-less matrix assembly layout to maximize labor productivity, workstation utilization, and footprint usage, while minimizing the system costs, and cycle times. Specifically, we develop an asynchronous assembly system for the automotive trim area. We aim to answer the question: How do we decide on an optimum number of workstations for the asynchronous assembly system, such that productivity and ROI are maximized? For this, we use cycle times, the number of operations per workstation, reference heights, and precedence graphs as input variables. Similarity matrices are used to quantify the similarity of tools, ergonomics, and human operations between workstations. Workstation utilization percentage and makespan are the metrics used to compare between alternative layouts. Finally, we perform a cost and makespan analysis to evaluate the ratio of trim area costs to total revenue, calculate makespan, and report the best layout found in the study. Quantifying subjective data, repeatability, less setup, and simulation time are the attributes that make this methodology valuable to any virtual commissioning software, an integral part of the smart manufacturing ecosystem.
On the Micromechanical Properties of Conventional and 3D-Printed Rebar
Authors: Allameh Seyed M. , Eckart Alexis , Fonseca Lopez Jose , Miller Roger , Lenihan Avery , Allameh Hadi 
Published: February 2023

Abstract
This study is focused on the micromechanical properties of conventional rebar and how they could be used for comparison with the 3D printed rebar tensile and fatigue properties. Current trends in additive manufacturing hint at the eventual use of 3D printing in the construction of homes and office buildings. Nowadays, 3D printing of homes is being achieved on an experimental basis by depositing extruded concrete in layers up to the ceiling to make walls, and then building a roof on top of the walls by conventional methods. This practice is not suitable to make bridges, multistory office buildings or structures that substantially experience tensile stresses. It is necessary to incorporate steel rebar in otherwise easily printed concrete structures. One way to achieve this is direct welding of steel into concrete by mounting a welder gun on to the 3D printing head and conducting 3D welding of the rebar. This has been accomplished and mild steel weldments have been 3D welded onto concrete. To make it acceptable for construction, the reliability of such printed rebar must be investigated. Early results of microscale tensile and fatigue testing on steel weldments made by additive manufacturing show desirable mechanical properties. However, the comparison has been made with macroscale tensile and fatigue properties of conventional rebar. To ascertain the reliability of 3D printed rebar welded onto concrete, it is essential to conduct a comparison with the micromechanical properties of conventional mild steel rebar. To achieve this, micro-specimens were machined off thick and thin conventional rebar in various orientations including along and across the longitudinal axis of the rebar and at different depths from the surface to investigate their micromechanical properties. Dog-bone shaped specimens 1000-micron in gage length with square gage cross sections measuring 200-micron × 200-micron were extracted from the surface as well as from the center of thick and thin rebar rods using a HAAS CNC. Samples were polished to a mirror finish and then tested in an Instron Electropulse E1000 load frame equipped with microgrippers that allowed monotonic and cyclic loading of the samples at a frequency of 50Hz. The results of micromechanical testing obtained from conventional rebar are compared with the those obtained from testing micro-specimens machined from mild steel weldments deposited by 3D welding on ceramics. The results demonstrate the reliability of mild streel rebar printed by 3D welding onto concrete. The implications of the findings on the use of additive manufacturing in 3D printing reinforced concrete and how it will impact the construction industry are discussed.
A Digital Twin Framework for Mechanical Testing Powered by Machine Learning
Authors: Kahya Müge , Söyleyici Cem , Bakır Mete , Ünver Hakkı Özgür 
Published: February 2023

Abstract
The aviation industry demands innovation in new materials and processes which can demonstrate high performance with minimum weight. Strength-to-weight ratio (STR) is the key metric that drives the value justification in this demand stream. However, aviation’s test and certification procedures are time-consuming, expensive, and heavily regulated. This study proposes a Digital Twin (DT) framework to address the time and high costs of mechanical testing procedures in the aviation industry. The proposed DT utilizes new Machine Learning (ML) techniques such as Transfer Learning (TL). Hence, a proof-of-concept study using TL in the Aluminum material group has been demonstrated. The promising results revealed that it was possible to reduce the test load of new material to 40% without any significant error.
Molecular Dynamics Simulation of the Effect of Hydrogen on the Interaction Between Dislocations in Alpha-Iron
Authors: Oyinbo Sunday Temitope , Jen Tien-Chien 
Published: February 2023

Abstract
In this study, we use extensive molecular dynamics (MD) calculations based on a highly-accurate interatomic potential to examine how hydrogen atoms impact the mechanisms behind the mobilities of edge and screw dislocations in alpha-iron (α-Fe) at a temperature ranging from 300 K to 500 K. The dislocation mobility in α-Fe is shown to be temperature and hydrogen concentration-dependent in this MD investigation. It is demonstrated from the results that hydrogen impurities that are efficient in locking dislocations exist in the form of complexes that are scattered discretely along the dislocation line and that these complexes operate as extremely effective impediments to the mobility of dislocations. The hydrogen impact on the edge dislocation motion from the dislocation velocities versus shear stress reveals that the movement of edge dislocations in α-Fe with hydrogen is much damped as the hydrogen concentration increases. Furthermore, the motion of screw dislocations in the α-Fe is by the process of kink-pair nucleation and migration. according to the simulation results, the locking mechanism of the cross-slip seen along the dislocation path is due to the strong-feature energy landscape and inherent energy fluctuation in the system, resulting in jogs formation.
A New Approach to Nonlinear Dynamic Modeling and Vibration Analysis of Tensegrity Structures
Authors: Yuan Sichen , Zhu Weidong 
Published: February 2023

Abstract
Tensegrity structures have experienced continued research and development interests in the past several decades. Revealing dynamic characteristics of a tensegrity structure, for example: vibration analysis, is an important objective in structural design and analysis. Traditional dynamic modeling methods are inaccurate in predicting dynamic responding of a tensegrity structure, due to their neglection of internal displacements of structure members. To solve this issue, a new nonlinear dynamic modeling method for tensegrity structures is proposed in this paper. This method defines position of a structure member as a summation of boundary-induced terms and internal terms in a global coordinate system. A nonlinear dynamic model of a tensegrity structure is derived from Lagrange equation, as a system of ordinary differential equations. This dynamic model can be linearized at an equilibrium configuration for vibration analysis. As shown in simulation results, the proposed method can predict natural frequencies of a tensegrity structure with a better accuracy than the traditional methods. Unlike the traditional methods that can only predict dynamic responses in a low frequency domain, the proposed method can also reveal dynamic responses of a tensegrity structure in a higher frequency domain by only using a small number of internal terms.
Bolt Loosening Detection for a Steel Frame Multi-Story Structure Based on Deep Learning and Digital Image Processing
Authors: Zhao Yadian , Yang Zhenglin , Xu Chao 
Published: February 2023

Abstract
Bolted joints are widely used in the field of aerospace, civil and mechanical engineering. During their service life, extreme loading or environmental factors can cause the loosening of bolts. In this paper, a bolt loosening detection method based on computer vision and image processing is developed to identify bolt rotation angle in a steel multi-story frame structure. The experimental results show that the bolt target detection accuracy can reach 100% by using the Yolo-V5s deep learning model trained with a self-developed bolt object dataset. The dataset consists of 337 bolt images captured in nature scenes. For the angle calculation, the final result shows that the identification error is less than 5.8°, and at a slight camera angle (0∼20°), the maximum error even does not exceed 2.8°. Thus, the effectiveness of this method for detecting rotary loosening of bolts is well validated.
Research on Mono-Fractal Characteristics of Dynamic Pressure at the Outlet of Centrifugal Compressor Based on EWT
Authors: Liu Yan , Li Yuchen , Geng Jincheng 
Published: February 2023

Abstract
In this paper, the outlet dynamic pressure of 800kw centrifugal compressor is taken as the research object, and the nonlinear characteristics of the system under different working conditions are analyzed by using empirical wavelet transform combined with mono-fractal. Firstly, the characteristics of the dynamic pressure signal when the system enters the surge state from the steady state through the transition process are analyzed, and the spectrum analysis of the dynamic pressure signal is carried out. Secondly, the modal components of the signal are extracted by empirical wavelet transform and the signal is reconstructed according to the correlation coefficient. Finally, the autocorrelation function of the reconstructed signal in different states and its Hurst parameters are studied. The research results show that when the system enters the surge state from the steady state, the spectrum of the dynamic pressure signal gradually concentrates to the low frequency band. And, the autocorrelation function and Hurst parameter of the reconstructed signal can effectively reflect the working state of the system.
Reliability Test of Mobile Embedded Accelerometers in Measuring Postural Stability for People With Parkinson’s Disease
Authors: Thelen Matthew , Mazumder Fardeen , Zhu Linda , Tang Charlotte , Miller Nathaniel S. 
Published: February 2023

Abstract
Parkinson’s Disease (PD) is the second most common neurodegenerative disease in the United States, affecting at least one million people. The cardinal symptoms of PD are tremor, rigidity, slowed movement, and impaired balance. While some symptoms of PD are responsive to anti-PD medications, other symptoms, are less medication responsive, especially walking and balance. Moreover, daily activities, such as writing, using tools, and walking, affect the quality of life (QoL) of people with PD (PwPD). Monitoring PD symptoms is essential for clinical evaluations and adjusting medication to help maintain QoL for PwPD. we are developing a mobile app to conduct at-home PD symptom monitoring with the goal of providing more timely and frequent measurements of PD symptoms for both patients and clinicians. While the tremor and finger tapping results collected in the mobile app have been discussed in previous publications, this paper focuses on the design and testing of postural stability (balance) tests in the app and the validation of the reliability of the mobile embedded accelerometers. During the test, a dual-purpose shaker was employed to provide vibration in amplitude and frequency range similar to human postural stability signals. A head expander was attached to the shaker and the smartphone holder is screwed to it. The tilt and yaw angles of the smartphone holder are adjustable, therefore the smartphone could be tested in an angled position relative to the shaker. Various types of input signals were tested, including sweep and multiple real postural stability data previously collected from a volunteer. Two models of smartphones were used to measure the signal through multiple trials and the results were compared to the input benchmark signal to verify the accuracy of the smartphone measurements. Besides the evaluation of the time domain raw data, we have also employed several signal processing algorithms to extract postural stability factors, such as the root mean square (RMS) value, the derivative of acceleration, frequency factors, etc., with the goal of identifying the patterns of motion signals which could be used as a summary measures of balance for PD. These signal processing algorithms were used to process raw measurement data from multiple trials, on different input signals, and on different devices. The results were compared, and the consistency of these factors through multiple trials with different smartphone models is tested and summarized. These results help us to find the most reliable measure to be used in the smartphone application. Both the results in raw acceleration signals and calculated factors will be discussed to further the current understanding of the reliability of smartphone measurements with embedded accelerometers.
Using Machine Learning Methods Towards Identifying College Campus Load Profiles and Energy Storage Application for Reducing Peak Energy Demand From the Utility Grid
Authors: Sweeny Christopher J. , Smith Jackson R. , Ghanavati Afsaneh , McCusker James R. 
Published: February 2023

Abstract
Efforts to reduce peak energy demand on the utility grid have been a challenge due to unique load profiles for individual customers such as college campuses, businesses, and homeowners. This work illustrates the application of machine learning in the form of Bayes Estimation, Principal Component Analysis (PCA), and Fisher’s Linear Discriminant to identify typical power load profiles for the author’s institution campus buildings. These methods of machine learning are applied to data collected from the campus and focuses on identifying trends in power usage as well as identify optimal times for charging and discharging of an energy storage system (ESS). Application of the algorithms is carried out using MATLAB to better understand the load profiles of various academic and residential buildings on campus. Bayes Estimation is used to determine optimal times for charging and discharging of an ESS using training sets from the power consumption data. Results from the study show Bayes Estimation yields a high accuracy in state estimation for various sample sizes given a limited amount of training data. Principal Component Analysis is used to determine key features from the data that effectively differentiate between the academic and residential buildings being observed. Key features that are observed through PCA include timescales such as hours of the day, days of the week, and months of the year, as well as power demand readings from each of the buildings’ respective electrical meters. Fisher’s Linear Discriminant is applied to the dataset for a similar purpose to Bayes Estimation, however the algorithm is used to determine peak vs non-peak recordings from the hourly power consumption data. Results from Fisher’s Linear Discriminant method proved to be unsuccessful in discriminating between classes of data. Analysis of the results will be used to further understand where and when ESS can be most effective to reduce peak energy demand from the campus on the local utility grid network. The paper presents the process of applying methods of machine learning to the data as well as the results from the mentioned methods.
Cellular Core Square Tube Crushing Analysis With Induced Folding Mechanisms
Authors: Jenson Sean , Ali Muhammad 
Published: February 2023

Abstract
Thin-walled axial crushing members are often employed as energy absorbers in crash attenuators and automobile chassis designs. They improve crashworthiness while maintaining low structural weight addition due to their high strength to weight ratios. The addition of core material has also shown to increase energy absorption capacity and increase structural stability during deformation. Specifically, the addition of functionally graded cellular cores maintained high strength to weight ratios and assisted the increase in strength and stability. The present study aimed to investigate the effects of induced folding of the outer tube structure to maximize energy absorbing performance of the axial crush tube member. This study was conducted through a numerical approach utilizing ABAQUS/Explicit dynamic modeling environment. To facilitate the induced folding mechanism, partial cuts were made around the tube periphery at discrete intervals. The depth of the cuts decreased from impact to distal end to force fold initiation in a descending pattern. Analysis criteria included: crushing force, deformation modes and energy-deformation stroke plots. The induced structural collapse due to the peripheral cuts promoted additional fold initiation at the cut sites providing elevated crushing forces and increase energy absorption. This behavior resulted in a 29.2% increase in reaction forces and 11.1% increase in energy absorption.
Research on Characteristics of Load Distribution and Levitation Clearance Response in High-Speed Maglev Train With Electromagnets Overlapping Structure
Authors: Zhang Jimin , Xue Zhao , Ren Qiao , Zhou Hechao 
Published: February 2023

Abstract
For a certain type of high-speed maglev train with electromagnets overlapping structure, the vertical dynamics model of which was established based on multi-body system dynamics theory, electromagnetic field theory and automatic control theory, the distribution characteristics of the train load on levitation electromagnets were theoretically analyzed. The response characteristics of the levitation clearance, together with the influence of electromagnets overlapping structure on them were studied by numerical calculation. The research results show: it is the electromagnets overlapping structure that causes the uneven distribution of the train load on levitation electromagnets, furthermore leading to the uneven distribution of the levitation clearance at different levitation electromagnets; the larger the load borne by the levitation electromagnet, the smaller the steady-state value of its levitation clearance would be, and the fluctuation range of the levitation clearance would be smaller as well when disturbed by the outside. To avoid the non-uniformity of levitation clearance distribution among different levitation electromagnets, a design method for the vertical stiffness of primary springs based on the characteristics of train load distribution was proposed and proved to be effective by numerical calculation. Through above research, the influence law and mechanism of the electromagnets overlapping structure of high-speed maglev vehicle on its levitation clearance response is revealed, which has guiding significance in terms of operation safety evaluation and levitation control strategy design for the high-speed maglev vehicle. The new design method for the primary springs vertical stiffness shows certain reference for the structural optimization design of the maglev vehicle aiming at higher speed in the future.