bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bruise Development Measurement in Apples Using Thermal Imaging Technique
Authors: Gurupatham Sathish K. , Bailey Caleb 
Published: February 2023

Abstract
Apple is an important fruit worldwide with nutrition and a huge market value. However, due to the mechanical damage during its handling after the harvest, millions of dollars are lost in the apple industry. Bruise (subcutaneous) is a mechanical damage caused to a fruit without any tearing of the fruit-skin. It could be caused by a mechanical impact, vibration, or compression which the fruit might come across during its handling after the harvest. It results in local degradation of the flesh tissue with browning effect with time. Normally, apple bruises take place beneath the peel and are difficult to detect by either visual or automatic color sorting. Thermal imaging offers a promising, contact-free, alternative technique for bruise detection in apples. Thermal imaging technique measures infrared energy emitted from the object surface which is converted into a thermal map called a thermogram. In the literature, there has been some work done on the influence of temperature and relative humidity of the surrounding on bruise susceptibility of fruits. In this paper, the influence of relative humidity in bruise development on apple was investigated applying thermal imaging technique. To accomplish this, two batches of healthy, almost same-sized, yellow and green apples with ten apples per batch were considered. The apples were subjected to a mechanical impact by dropping them from a constant height ensuring the fruit skin is not torn by the impact and the impact is same is for all the apples. Then, they were thermal imaged with the focus on their bruises. They were kept and observed at two different temperatures and relative humidity. The first set involved the relative humidity of 40–43% with room conditions whereas the second one involved the relative humidity of 80%–82% in the freezer compartment of a domestic refrigerator. For all the trials, the same condition was maintained for the fruits throughout the three-day observation. At the end of stipulated observation period, the fruits were thermal imaged to capture the bruise development for the different relative humidity levels for two different temperatures. The observation was repeated for three times to obtain the repeatability.
The results show that the bruise development is faster for the higher relative humidity than for the lower one. This could be due to the lower transpiration rate of water from the fruits at higher relative humidity. Also, the water potential under the bruised skin decreases faster for lower relative humidity condition which results in decreased bruise development.
Using Deep Convolutional Neural Networks (DCNN) to Learn by Sensor Signal Classification Critical Local Regions of a Physical Shaft-Rotor System: A Complex Mechanical System Example for Machine Learning Considerations
Authors: Papageorgiou Pantelis Panagiotis , Georgiou Ioannis T. 
Published: February 2023

Abstract
Complex machinery contains critical regions, such as revolute joints-ball bearings-journal bearings, that are prone to damage initiation and growth. If not detected early, damage in critical local regions leads to premature failure. The overall complexity of an integrated system limits developed classical methods from detecting early damage in complicated local areas. A pure experimental data environment could provide solutions given the broad impact of machine learning. Here an interesting idea is introduced to support a machine learning framework for damage detection in local critical regions. The vibration field developed in a local area surrounding a ball bearing support of a lab flexible shaft-rotor system was measured by a set of accelerometers to form a dataset environment. It was used as an experience for machine learning by a deep convolutional neural network adapted from the AlexNet architecture. Our main result is the casting of a solid mechanics prediction problem into a classification problem and eventually computing a solution by a deep machine learning technique. Technology innovations improve computer speed, data storage media, and graphics processing units. These factors are turning existing machine learning techniques into state-of-the-art prediction tools that can be adapted and developed to exploit large volumes of vibration data for diagnostics. Data-driven predictive-diagnostics results in improved condition monitoring of the complex machinery system with economic gains form estimated low-cost maintenance and energy savings. Classical condition monitoring techniques cannot learn from experiences predictive models of the dynamics-diagnostics of onboard ship and aircraft machinery operating under varying environmental conditions.
A Computationally Efficient Approach for the Simulation of Silicon Anodes in Lithium-Ion Cells
Authors: Webb Rebecca , Chen Xiaoling , Mazumder Sandip , Canova Marcello 
Published: February 2023

Abstract
Silicon has emerged as a frontrunner for next generation anode materials due to its high theoretical gravimetric capacity (∼4200 mAh g-1). The presence of volume changes and stress in silicon anodes introduces strong, nonlinear couplings with the lithiation and delithiation process, requiring a significant increase in the complexity of the mathematical framework describing its behavior.
A mathematical description of the multiphysics coupling process is presented, requiring the simultaneous solution of the spherical diffusion equations for a binary system with volume change and stress applied to a representative particle. The resulting model description is in the form of a nonlinear set of index-2 Partial Differential and Algebraic Equations (PDAEs).
This paper proposes a computationally efficient approach to solve the PDAE system, with the objective of predicting the lithium concentration, volume change, and stress generation during galvanostatic charge and discharge conditions. A semi-explicit scheme is proposed to reformulate the original system into decoupled sets of nonlinear ordinary differential equations and nonlinear algebraic equations. After a grid sensitivity analysis in the space and time domains, the proposed approach results into a computationally efficient implementation that ensures the numerical accuracy in solving this problem, for use in lithium-ion battery control and estimation applications.
This study shows that a semi-explicit scheme can produce results at a rate 2.5–3.5 times faster with comparable accuracy when compared to traditional fully implicit solution methods. Limiting the number of Newton iterations in the semi-explicit scheme further reduces the semi-explicit computation time by 25 minutes.
Simplified Geometries for Intracranial Acoustic Modeling
Authors: Cites Marianne E. , Dumm Christopher M. , Hiers Anna C. , Klinzing George E. , Balaban Carey D. , Vipperman Jeffrey S. 
Published: February 2023

Abstract
The geometric complexity of the contents of the head confounds mechanical analysis of intracranial structures. Conventional models for computational analysis are typically created through a laborious segmentation and reconstruction process highly dependent on expert labor and anatomical insight. This study explores a deterministic process for construction of a simplified, anatomically-relevant head model appropriate for acoustical modeling. Various key anatomical features with acoustical significance are reviewed. Models of increasing complexity are generated, spanning a range from coupled concentric spheres to more advanced geometries incorporating ventricles, brain structures, and other anatomical landmarks. Geometric relevance of the models is assessed by comparison to a high-fidelity computational geometry derived from medical imagery. These techniques and models are useful for a variety of studies investigating phenomena such as traumatic brain injury mechanics and industrial safety.
Damaged Apple Detection Using Artificial Intelligence
Authors: Gurupatham Sathish K. , Bailey Caleb 
Published: February 2023

Abstract
The field of mechanical engineering is evolving with latest technologies such as artificial intelligence. the blend of AI technologies such as deep convolutional neural network (DCNN), convolutional neural network (CNN), artificial neural network (ANN) which contributes more to control the process parameters, process planning, machining, quality control and optimization for a better product or system. The implementation of AI in mechanical engineering applications results in minimizing the rejection of machine components which helps the whole process to be economical with better quality outputs. Considering the stiff competition among the manufacturers in the market, increasing the production rate while maintaining stringent quality control is a big challenge. In this perspective, artificial intelligence is gaining popularity in production lines to maintain a high quality for the products. A CNN is a deep learning algorithm, that is analogous to that the connectivity pattern of neurons in the human brain, has become popular and effective to image classification problems recently. It takes in the image of the object and assigns importance to various aspects/objects in the image so as to differentiate one from the other. In fruit-sorting process, manual classification is time-consuming, expensive, and requires experienced experts whose availability is often limited. To address these issues, various machine learning algorithms have been proposed to support the automated classification of fruits. In this paper, to classify “regular apples” and “damaged apples”, deep learning algorithm is applied. The pre-trained, deep learning models namely, VGG 16, ResNet50, Inceptionv3, Mobilenet_v2 along with a basic sequential convolutional model are applied to differentiate the damaged apples from regular ones and their performance variation is also analyzed. For this work, the data set containing damaged and regular apples was garnered from various local stores and farms. The data set consisted of 400 color images of both regular and damaged apples. Though the number of samples is smaller, the above-mentioned deep learning models demonstrated to overcome this deficit. For the training of model, 80% of the total sample (280) images were utilized while 20% and 10% of the sample (80 & 40) were applied for the validation and testing the model. The results show more than 90% accuracy for all the models except ResNet 50. The performance of these models can be improved even further by increasing the size of data set by adding more fruit images through better training of the models. Our experimental study demonstrates the application of artificial intelligence through four different transfer learning techniques works well for deep neural network-based fruit classification. It minimizes the labor and human errors involved in the fruit-sorting process which results in saving money and time.
Methods for Evaluating the Effect of Slack Rope Condition in a Nuclear Material Handling Equipment
Authors: Gupta Deepak K. , Kanakasabai Pugazhendhi 
Published: February 2023

Abstract
Electric overhead & gantry multiple girder cranes with top running bridge & trolley used at nuclear facilities are typically designed in the US per the American Society of Mechanical Engineers Nuclear Operating Guide-1 [1] (NOG-1) Standard which is approved by the US Nuclear Regulatory Commission (NRC). Per NOG-1, seismic qualification of the material handling equipment is a major design consideration based on the equipment’s safety classification. Nuclear material handling devices typically use steel wire ropes to hoist the critical load. Seismic events can cause excessive vibrations in the crane and the critical load that is hoisted by the crane hook leading to a potential slack rope condition, which is an important safety consideration per NOG-1.
The work presented in this paper compares two analyses methodologies — the linear response spectrum analysis and the non-linear time-history analysis — as it pertains to slack ropes. A simplified crane finite element model with linear springs representing the ropes is employed for the linear response spectrum analysis. A similar crane finite element model with tension only springs representing the ropes is employed for the non-linear time-history analysis. Equivalent sets of response spectra and five time-history input data are utilized. The aim of this paper is to investigate if the linear response spectrum analysis always yields conservative results for the slack rope condition as compared to the non-linear time-history analysis, and if so, the expensive non-linear time-history analysis required per NOG-1 for the slack rope condition can be avoided without compromising safety.
Numerical and Experimental Qualification of Seal Integrity of Rotary Shouldered Connections Under Combined Loads
Authors: Song Fei , Du Michael , Li Ke 
Published: February 2023

Abstract
The bottom hole assembly (BHA) of a drill string consists of various components and tools such as a drill bit, a mud motor, a directional drilling tool, measurement-while-drilling tools, and logging-while-drilling tools. It operates at the bottom of the wellbore and physically drills the formation. A rotary shouldered threaded connection is composed of a pin element and a box element and serves as a critical component to screw the tools and subs used on the BHA together. Functionally, the rotary shouldered connection upon makeup must be able to maintain sealability between internal and annular pressures at the seal face, while transmitting torque and withstanding bending loads when drilling through a curved well section. Leaks of these connections while drilling can be catastrophic. The drilling and measurement tools could be flooded with invasion of drilling mud into the inside, which could result in severe damage of the expensive measurement devices, sensors, and electronics inside the tools as well as a significant amount of nonproductive time. Hence, sealability assurance of the connections subjected to combined loads in difficult drilling conditions is essential for qualification of advanced rotary shouldered connections.
In this study, a numerical model based on three-dimensional finite element analysis (FEA) of the threads is developed for numerically qualifying the seal integrity of the connection. Specifically, the FEA model can describe the nonlinear interactions between the pin and box threads and on the seal face subjected to combined loads of preload, pressure, and bending. The resulting contact pressure distribution on the seal face predicted by the FEA model can be utilized as a performance metric for evaluating the risk of leaking. A full-scale sealability test rig with three-point bending configuration is also developed for validating the prediction from the numerical model. In the test, the test sample is held at its two ends onto a rigid beam with holding chains. A remotely controlled hydraulic jack is placed between the beam and the connection to generate the bending moment. It is observed in the test that the pressure drop after shutting down the pump is less than 1% within the last 15 minutes and there is no fluid leaking out of the connection after the pressure is released, which favorably agrees with the FEA prediction. Once validated, the numerical modeling method has been successfully applied to virtually qualify the sealing capacity of advanced rotary shouldered connections of various sizes without performing physical tests, which has resulted in a tremendous amount of cost and time savings for advanced rotary shouldered connection development.
Inclusion of Continuous Annuities in Engineering Economics Instruction
Authors: Armstrong Aaron 
Published: February 2023

Abstract
Practicing engineers are often required to conduct financial analyses of engineering projects and related business ventures. The financial impacts of engineering work often involve repeated payments spread out over time rather than simple single transactions. Usually these payments occur monthly, quarterly, biannually, or annually. However, with advances in transactional and computational capability, systems with increasingly frequent payment structures are becoming more common. The greater efficiencies gained with these more frequent payment structures have led to the possibility of using continuous payment streams where the payment amount and time between payments goes to zero. This paper advances the case for the inclusion of this material in undergraduate engineering economics courses and presents an introduction to the topic and suggested methodology for how it can be integrated and presented in such a curriculum. The inclusion of this material in these courses will improve the effectiveness of engineering economic analysis of current transactional systems as well as to potentially advance the use of continuous annuities in professional engineering-related commerce.
Development of a Haptic Glove for the Index and Middle Fingers of the Right Hand With Force Feedback
Authors: Caballero Moreno Jhon A. , Roa Prada Sebastián 
Published: February 2023

Abstract
Due to the growth in the amount and types of information that surrounds us, it is important to explore other areas and ideas and ways to interact with information. Haptic systems with their tactile, kinesthetic and motor capabilities, together with the associated cognitive processes, present a bidirectional channel of information to our brain. This channel provides a way to interact with the information with which a large number of applications can be generated. Haptics covers many disciplines; Therefore, a wide variety of applications is generated that includes those areas of human needs such as products, manufacturing, education, health, among others. For teleoperation and virtual reality applications, force feedback is important for the user, since due to this it can improve the interaction between the user’s real environment and the virtual environment.
For this reason, the human hand is capable of obtaining information while executing complex tasks. This has a variety of functions among the most important are the sensory and motor functions. That is why there is an interest in creating a haptic device for the hand.
Haptic research and technology have created and evaluated different prototypes with their characteristics and capabilities for the use of virtual environments, some of which are currently marketed. In this way, different applications of this technology have been made in areas such as: graphical interface, medicine, games, engineering, telerobotics, simulators and medical rehabilitators. Focusing on the haptic interface for the upper limbs with force feedback, the process of integrating exoskeletons and haptic interface is a great advance to be able to transmit force to the joints of the hand.
Traditional methods for the rehabilitation of patients with motor disabilities in the upper limbs are slow, costly and in many cases ineffective, considerably reducing their quality of life. Therefore, it seeks to develop a low-cost haptic force feedback technology with its three main elements: a haptic device, a computer where a virtual environment (interface) is generated, and a human operator that closes the loop, which complements the recovery of these patients and thus achieve better results. Previous studies that integrate 3D printing technologies, virtual reality (VR), microcontrollers and actuation through tensors demonstrate the successful development of different prototypes with similar characteristics, although most of them are expensive. For the development of the prototype, the V methodology was used, performing instrumentation, interface-prototype connection and force feedback tests. The integration of these subsystems after the tests, result in a final prototype of the haptic glove for two fingers (index and middle) of the right hand, managing to generate feedback forces on the user based on the information provided by the interface, complying with the characteristics of being a functional and low-cost glove.
Body Armor - Current and Potential Materials
Authors: Thakkar Nishant , Piovesan Davide , Steinbrink Scott 
Published: February 2023

Abstract
Military forces have faced the ballistic threat in many forms for hundreds of years, ranging from spears to bomb fragmentation. Soldiers have historically worn body armor to protect themselves on the battlefield. The goal of modern lightweight body armor development has always been to develop lighter and stronger materials so that performance can be improved while reducing weight and making the mobility of the personnel easy. Body armor ballistic testing follows strict guidelines provided by the National Institute of Justice (NIJ) in the United States of America. According to the new testing standards, innovative products are being released. This review elaborates on various materials and composites that are being used in the making of body armor that eventually help eliminate the threats from high-velocity bullets, shell fragments, and knives. In this paper, the science of body armor materials is quickly reviewed with emphasis on current knowledge of relevant energy-absorbing mechanisms in fibers, fabrics, polymeric laminates, and ceramics. The drive to create lightweight and comfortable armor systems for military personnel has led to the development of various composite materials. The paper reviews the major features of materials used in body armor and focuses on the development of intriguing new potential materials.
Ultrasonic Characterization of AlSi10Mg Specimens Printed By Direct Energy Deposition Technology
Authors: Chukovenkova Mariya , Zagrai Andrei , Halliday H. Scott , Toddy Joshua , Murphy Nylana J. 
Published: February 2023

Abstract
Additive manufacturing (AM) process has different parameters, their combination, and powder composition which could affect the mechanical properties of printed material. The layer by layer manufacturing approach influences microstructure of the material, and hence, the anisotropy of the mechanical properties. Also, defects such as porosity or cracks could be caused by non-optimized printing parameters.
In order avoid wasting of sample while inferring mechanical properties of the printed material, it beneficial to utilize the nondestructive evaluation (NDE) methods. Implementation of NDE methods for additively manufactured parts plays a great role in evaluating and ensuring the reliability of the printed part.
In this work, ultrasonic technique was utilized to determine the elastic properties and anisotropy of additively manufactured AlSi10Mg and conventionally fabricated Al 6061 samples.
An ultrasonic measurement approach which allowed for the accurate measurement of the material properties was established and implemented. Longitudinal and shear transducers were used and the sound speed was calculated by analyzing the position of the arrived pulses in the pulse-echo configuration. Elastic properties were calculated from the longitudinal and shear sound speeds and measured density. Also, the correlation between elastic properties and sample’s location within the printed block, and spatial distribution of the elastic properties were explored.