bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterization and Optimization of a Lower Extremity Exoskeleton Device for Leg Muscle Rehabilitation
Authors: Elahi Haadi , Perez Marvin , Viswanathan Vimal , Vemuri Aayush , Madireddy Indeever , Zaidi Sohail
Published: January 2022

Robotics-assisted rehabilitation has been one of the popular research areas in recent years. The increase in the elderly population and sports-related injuries, the high cost of physical therapy, and advances in Mechatronics have been crucial factors driving this research. The objective of this project is to provide supplementary motion in knee extension and flexion for lower extremity rehabilitation. The device incorporates pneumatic muscles that closely recreate human muscle movement and surface electromyography (EMG) sensors to activate motions. Recently, tests were carried out to characterize the unit and compare the performance of the pneumatic muscles against the theoretical values provided by the manufacturer. Results indicate limitations in the range of operation of the device, mainly due to the limited contraction ratio of commercially available fluidic muscles. Overall, the project provided vital insights that may be useful for researchers developing exoskeleton devices for rehabilitation. This paper reports the characterization of the EMG sensors and pneumatic-based fluidic muscles used in the ABJ system. To address the shortcomings of the commercially available fluidic muscle, custom muscles are designed and characterized. The results provide significant insights for a redesign of the device. Based on the characterization data, a redesign is proposed for a future generation of the device.
Corrosion Monitoring and Mitigation in Reinforced Concrete Structures Using Novel 3D-Printed Valves and Internet-of-Things Approach
Authors: Escarcega Mario , Bradley Savannah , Campos Gabriel , Randall Parker , Strebe Luke , Momeni Hamed , Ebrahimkhanlou Arvin
Published: January 2022

Reinforced concrete corrosion can be monitored by the acoustic emissions produced by transient mechanical waves of corrosion and damage events. This study aims to use 3D-printed one-way valves to relieve the corrosion-induced internal pressure on concrete structures. This valve is designed in such a way that it can be simply installed to existing structures to increase corrosion resistance in the concrete structure and extend the service life. The impressed current technique is a common technique used to rapidly corrode reinforced concrete samples. To corrode the concrete samples, current was passed through the internal steel strand and copper mesh. This study also investigated the use of an Internet-of-Things device to continuously monitor corrosion in steel-reinforced concrete samples in order to determine the effectiveness of the designed valves. Pressure testing revealed that the designed valves were against backflow and cracked at very low pressure. It was ideal that the valves cracked at a lower pressure so as to release pressure constantly while preventing further corrosion in backflow. This type of valve will prevent reinforced concrete surface cracking and extend the service life of concrete structures by releasing internal pressure build-up without allowing foreign materials to further corrode the steel reinforcement.
Effect of Morphological Changes in Reused AlSi10Mg Powder on the Formation of Defects in Components Manufactured by SLM
Authors: Orozco Sandoval María Guadalupe , Hinojosa Rivera Moisés
Published: January 2022

The continuous reuse of powder in powder bed additive manufacturing techniques is a common practice, however, research focused on morphological changes in the powder as reuse cycles accumulate is relatively scarce, especially if we consider that the quality of the powder is fundamental to obtain components that meet the requirements of demanding industries such as aeronautics, automotive and medical. The continuous reuses of powder offer two important advantages which are reducing manufacturing costs and waste, both fundamental to the continuity of companies. The repercussions of uncontrolled reuse of powder without understanding the impact on final parts is a growing problem for a common practice. The present study focuses on the morphological changes in the particles of reused powder in five cycles of AlSi10Mg and the resulting effects on the components manufactured by the Selective Laser Melting (SLM) technique, varying manufacturing parameters defined as critical by the literature. Scanning Electron Microscopy (SEM) images of the powder were evaluated following the Zingg methodology, categorizing the particles into four shapes: sphere, disc, rod and blade. 80% of the virgin powder particles showed a spherical morphology, unlike the powder reused in five cycles, where this percentage is reduced by 8%. Elongated particles (blade and rod shape) showed an 11% increase in reused after five cycles. The variation in manufacturing parameters had an impact on the relative density of the components manufactured with reused powder, obtaining a maximum value of 94.5% and a minimum of 86%. The variation in the surface defects of the components was mostly influenced by the power of the laser, the scanning speed and the location of the component in the powder bed, where the sample set with the highest percentage of surface defects was where a laser power of 200 W and a scanning speed of 1000 mm/s and the gas flow was lower due to its location in the powder bed. The results allowed to establish the critical manufacturing parameters for components manufactured with reused powder in five cycles of AlSi10Mg; however, the accumulation of reuse cycles requires further investigation.
Design Innovation of Bicycle Frames Exploiting Topology Optimization
Authors: Colombo Zefinetti Filippo , Rossoni Marco , Martinelli Carlo , Regazzoni Daniele
Published: January 2022

In recent years, the use and demand of bicycles have increased thanks to the growing attention to the environment and due to the COVID-19 pandemic situation. At the same time, the design of the bicycles has remained substantially unchanged, improving in materials and components technology. In the off-road sector, two diametrically opposed categories have emerged in terms of comfort and pedalling efficiency. The goal of this research is to introduce a first methodological approach for the optimization of a mountain bike frame. The behaviour of the developed frame aims to combine the pedalling benefits now available only in different and non-comparable bicycle configurations. The first step concerns the modelling of a generic off-road bicycle frame, then its behaviour has been simulated for specific load cases. Subsequently, the part of the bicycle that best performed the double function of compliance and rigidity has been sought through an analysis of the strain energy using FE simulations. Hence, the reference region has been topologically optimized to provide adequate chassis travel performance. The analysis scheme has been iteratively repeated also on other parts of the frame until an acceptable solution is obtained for the utilize presented. The final configuration permits a rear tube and bottom bracket displacement of 10.4 mm and 2.4 mm compared to the 0.5 mm and 0.4 mm of the original frame respectively. The approach described can be proposed as a support for the search for an innovative design for products with unchanged geometries due to the inertia of the designers. At the same time, this methodology aims to expand the possible use of topological optimization, moving away from the classic constraint schemes present in various software.
Solder Paste Additives for Thermal Expansion Control
Authors: Capela P. , Souza M. S. , Costa S. , Fernandes M. , Figueiredo H. , Alves R. , Delgado I. , Teixeira J. , Soares D.
Published: January 2022

Most electronic failures that occur in equipment are due to stresses induced by differences in the Coefficient of Thermal Expansion (CTE) of the different materials in a Printed Circuit Board Assemblies (PCBA). During a thermal cycle, the incompatibility of CTE between the PCB and the components induces shear fatigue that may affect the reliability of the solder interconnections on the PCB, which can eventually lead to fracture and failure of the joints and the PCB. Due to the advancement in the electronic components industry, interest from the researcher’s point of view has grown in studying the influence of additives in the solder alloys, in relation to microstructure, physical and mechanical properties and, mainly in the CTE.

In this work two types of additives (Bi and graphite powder) were tested in order to reduce the CTE of a lead-free solder (SAC305) solder paste for reflow soldering. Because the selected additives have different characteristics, namely different densities, a different method of SAC305 solder additivation was tested for each type of additive. For Bi addition in SAC305 alloy (up to 6.5 wt.%), after a mechanical mixing of it, with the solder paste, a fusion technique (with a thermal cycle similar to the used on the reflow soldering) was used. For composites with graphite (addition up to 0.1 wt.%) a double-printing method was used in order to achieve a homogeneous additive distribution, so that graphite remained in the final ingot.

These additivated solder alloys were chemically analyzed and characterized for thermogravimetric properties. Samples microstructure were characterized by SEM/EDS analysis, and also they were tested for their electrical resistivity.

With graphite addition there is a slight increase on the initial alloy melting temperature (∼1.5°C) and with Bi an almost linear decrease was obtained (∼16 °C for the higher tested Bi addition).

Composites with bismuth have a decrease trend, with the additive increase content until close to 5%. The CTE value decreases almost linearly ((from 25 to ∼14.5 μm/(m·°C); R2 = 0.9905). However, the sample of SAC305 + 6.5% Bi does not follow this trend, which may indicate that for these and higher amounts of bismuth, the composite CTE stabilizes. For composites with graphite there is a reduction of CTE (from 25 to ∼17 μm/(m·°C) for 0.04 wt. % graphite addition). For higher graphite additions the CTE seems to increase.

The obtained results show that both additives can be used in order to achieve a CTE target value close to the PCB copper PAD (17 μm/(m·°C). However, the mixing method used for graphite mixing on solder paste cannot be directly transposed to the reflow soldering technique.
The Prediction of Fatigue Life Basing Random Forest Algorithm
Authors: Yin Chenfei , Yang Yu
Published: January 2022

The fatigue performance of test pieces is sensitive to various influence factors. If one factor changes, the fatigue life will differ greatly. For the changes of each factors, the fatigue test must be carried on, which will increase the test cost. In this paper, in order to solve this problem, basing the machine learning method, we establish the random forest regression model to conduct a material fatigue fracture life prediction research for the 7050-T7451 aluminum alloy. For the 7050-T7451 aluminum alloy standard smooth test pieces considering six detailed factors, the fatigue test is carried out at two stress levels to obtain the fatigue fracture life. Firstly, the fatigue test data are pretreated in this paper. And the fatigue test conditions of each group are different from each other, so there are six attributes of the test conditions, including load, processing technology, roughness, material direction, thickness of the parent metal and raw material position. The test data from sets 2 to 10 are selected and randomly divided into training set and verification set with a ratio of 4:1, and the first set data was reserved as the test set. Secondly, the random forest regression model is established. And then the random forest model is trained. The model is evaluated according to the R2 determination coefficient, and the R2 determination coefficient is 0.49 after adjusting the hyperparameters of the random forest model on the verification set. It is found that the true values of the tests are all within the fatigue dispersion band four times of the predicted values. Considering the fatigue dispersibility, it is a reasonable learning model. Finally, the model is verified by the first set of test data, and the accuracy of the predicted value of the first test set is 87.7% relative to the test mean value, which the predicted result is good. Processing technology, roughness, material direction, thickness of the parent metal and raw material position can form 162 experiment combinations according to these five discrete attributes. This paper involves 10 kinds of test combinations including all kinds of attribute information. For the 152 test combinations that don’t occur, a satisfying life prediction value can be obtained using random forest model by directly importing the experimental properties without conducting experiments in the future. The fatigue fracture life prediction basing on random forest regression algorithm provides a new idea for data mining to solve traditional problems.
A Comparative Study Between a Sharp and a Diffuse Topology Optimization Method for Thermal Problems
Authors: Lamarche-Gagnon Marc-Étienne , Navah Farshad , Ilinca Florin , Molavi-Zarandi Marjan , Raymond Vincent
Published: January 2022

The objective of this work is to compare two topology optimization strategies, i.e. density-based (diffuse) and level-set-based (sharp), in thermal problems involving a heat conductor and an insulation material. The fundamental difference between the two methods lies in the representation of the materials’ interface: the density method allows for transitional regions whereas the level set one does not. Several regularization techniques, such as perimeter restriction, parameter ramping, level set gradient restriction and parametrization, are explored in order to enhance each method’s robustness and to decrease its sensitivity to initial conditions. It is shown that, in the two test problems investigated, the diffuse method was in general more robust than the sharp one. However, when combined with appropriate regularization techniques, the level set method lead to material distributions which were more optimal.
Automated Hardening of Deep Neural Network Architectures
Authors: Beyer Michael , Schorn Christoph , Fabarisov Tagir , Morozov Andrey , Janschek Klaus
Published: January 2022

Designing optimal neural network (NN) architectures is a difficult and time-consuming task, especially when error resiliency and hardware efficiency are considered simultaneously. In our paper, we extend neural architecture search (NAS) to also optimize a NN’s error resilience and hardware related metrics in addition to classification accuarcy. To this end, we consider the error sensitivity of a NN on the architecture-level during NAS and additionally incorporate checksums into the network as an external error detection mechanism. With an additional computational overhead as low as 17% for the discovered architectures, checksums are an efficient method to effectively enhance the error resilience of NNs. Furthermore, the results show that cell-based NN architectures are able to maintain their error resilience characteristics when transferred to other tasks.
A Multi-Attribute Knowledge Criticality Framework for Ranking Major Maintenance Activities: A Case Study of Cement Raw Mill Plant
Authors: Iheukwumere-Esotu Lilian O. , Yunusa-Kaltungo Akilu
Published: January 2022

Systematic failure analysis enhances the ability of decision makers to implement strategies that are beneficial to systems they manage. However, in industrial maintenance activities such as, Major overhauls, outages, shutdowns and turnarounds (MoOSTs) there is scarcity of knowledge and experience, limiting the effectiveness of such failure analysis. Transformation of knowledgeable actions generated from experts’ tacit based knowledge from performing MoOSTs is encouraged. A key step to achieve such transformation is by prioritizing maintenance efforts by critically assessing relevant maintenance attributes. Criticality analysis of tasks is considered as an effective approach for prioritizing MoOSTs activities. This paper combines a traditional approach for analysing attributes of frequency and consequence factor values ranked by experts using a mathematical relationship to determine critical activities as well as a fuzzy logic system to develop a fuzzy inference system (FIS) for generating fuzzy criticality numbers of MoOSTs activities. In this regard, the traditional method qualitative criticality matrix, and boundary settings by experts provide baseline information for the FIS, to establish If-Then rules and map membership functions of two crisp inputs and output. Practical applicability is demonstrated using a Raw Mill System (RMS) from a cement manufacturing plant. The comparison of results from the two methods shows slight variations in criticality numbers, howbeit a consistent ability to capture critical MoOSTs activities. Moreover, the validity of results obtained by the fuzzy logic system is enhanced and more superior because it can demonstrate sensitivity.
Railguns: An Overview
Authors: Sasek Adam , Bellocchio Andrew
Published: January 2022

Railguns are electric weapons that use an electromagnetic force to propel a projectile to very high velocities. The high velocities achieved by railguns increase kinetic energy of the projectile, improve range, and eliminate the need to store dangerous, heavy propellants. The weapon traditionally consists of two parallel conductive rails and a conductive armature that rides between the rails. Designs rely on the Lorentz force of a generated magnetic field to propel the armature and projectile. The railgun proposes unique challenges when it comes to rail, armature, and containment structure design. The rails themselves must be highly conductive, while withstanding large thermal and mechanical shocks. Rail damage such as gouging, grooving, and transition and arc ablation are all a concern when these weapons are subject to the large stresses in launch. There are different configurations of the railgun in development to include the multiple railgun, parallel railgun, and augmented railgun. This paper will review these different configurations and their potential. These weapons are currently being considered for use by naval forces and in space operations.
Modeling of Thrust Force in Ultrasonic Assisted Drilling of DD6 Superalloy
Authors: Zhu Xiaoxiang , Wang Wenhu , Jiang Ruisong , Xiong Yifeng , Liu Xiaofen
Published: January 2022

Nickel-based superalloy DD6 is characterized by high strength, high temperature resistance, which makes it an ideal material for manufacturing aircraft engines. Ultrasonic assisted drilling is an advanced machining method, which greatly improves the machining performance of difficult-to-machine materials. The drilling thrust force has an important effect on the hole surface quality, burr height and bit wear. Therefore, it is necessary to predict the thrust force of ultrasonic assisted drilling. However, there are few reports about the thrust force modeling of ultrasonic assisted micro hole drilling. A thrust force prediction model for ultrasonic assisted micro-drilling is presented in this paper. Based on the basic cutting principle, the dynamic cutting thickness, dynamic cutting speed and acoustic softening effect caused by ultrasonic vibration are considered in this model. The model was verified by the ultrasonic assisted drilling experiments with different spindle speeds, feed rates, amplitudes and frequencies. The maximum error and minimum error of the average thrust force are 10.5% and 2.3%, respectively.
Effect of Hemispherical Protuberance on the Leading-Edge to Control the Laminar Separation Bubble
Authors: Singh Pradeep , Sarkar S.
Published: January 2022

The excitation of a laminar separation bubble (LSB) under the influence of a hemispherical protuberance has been discussed here. The experiments were conducted over a modeled aerofoil of constant thickness with a semi-circular leading-edge at a Reynolds number (Re) of 1.6 × 105, where freestream turbulence (fst) is 1.2%. A protuberance of height, k = 3 mm is placed at an angle of 60 from the theoretical stagnation point. The characteristic Re based on the diameter of the element and freestream velocity is 1442. The protuberance sheds streamwise vortices leading to an unsteady flow near the leading edge, resulting in a reduction of bubble length by 31–78%. Further, the separation bubble length, its fundamental shedding frequency, and the turbulence statistics become highly asymmetric in the spanwise direction, while the influence of protuberance is felt up to 6k.
Comparative Life Cycle Assessment of Various Grinding Strategies for Nickel Base Superalloys
Authors: Singh Aswani Kumar , Sharma Varun
Published: January 2022

During the grinding of advanced materials, several problems such as the generation of high temperatures, more energy consumption, excessive utilization of cutting fluids, and emissions of various harmful gases have been encountered, resulting in negative impacts on the environment. In order to mitigate such problems, this study focuses on the sustainability of the grinding process amalgamated with the introduction of ultrasonic vibration. The effect of ultrasonic vibration has been incorporated in grinding along with optimal machining parameters. Ionic Liquid (ILs) with vegetable oil has been used as a cutting fluid, which is categorized as a green solvent. Ionic Liquids (ILs) have been proved as a favourable sustainable alternative additive in the base oil to obtain the desired cutting fluid performance. A comparative study has been conducted for various grinding strategies to assess their environmental impacts using Life Cycle Assessment (LCA). Initially, Conventional Grinding (CG) and Ultrasonic Assisted Grinding (UAG) have been attempted. In the subsequent attempt, ultrasonically atomized fluid has been used for the CG and UAG processes. The SimaPro LCA software has been used to quantify the environmental impacts associated with these processes. The inputs for the LCA inventory are consisted of material consumption, energy consumption, and cutting fluid consumption. In this study, the ReCiPe 2016 V 1.04 midpoint and endpoint module has been used for carrying out the impact assessment. The results of the LCA impact assessment showed that the ultrasonic-assisted grinding performed using ultrasonically atomized cutting fluid exhibited the least adverse effects on the environment as compared to other mentioned counterparts. The cutting fluid and power consumption has been found significant contributor for the environment. The results obtained from LCA analysis can be utilized as a basis for sustainability assessment framework in the grinding of advanced materials.
Planning and Acquisition of Real-Time Production Data Through the Virtual Factory in Chemical Industry
Authors: Raamets Tõnis , Karjust Kristo , Hermaste Aigar , Mahmood Kashif
Published: January 2022

The various production problems that have arisen are closely linked to the need of the digitize products, production equipment, and their processes. With the increasing use of innovative software and hardware solutions, it is possible to monitor production processes accurately in the real-time and to manage various planning decisions according to these digital models. Such digital models allow us to react quickly to the physical production problems and to solve and also predict them. Furthermore, the virtual factory as an integrated simulation model of production units, provides an advanced decision support capability. On the other hand, Industry 4.0, the new industrial revolution has increasingly been used in the industrial sector and its development has grown exponentially in recent years. Various production equipment and activities are connected via network sensors to the Internet, where a huge amount of data is generated, stored, and analyzed. Industrial Artificial Intelligence (AI) algorithms are being used to evaluate the collected data and to provide valuable information for planning operations. This new industrial age presents new trends and challenges in the data context, such as scalability, cyber-security, and big data.

Therefore, when it comes to collecting data from devices and workplaces in real time, it is also wise to analyze the necessity and efficiency of this data, using different artificial intelligence algorithms. Clean data generally enables to make efficient and effective management decisions in the future based, to ensure the highest possible efficiency in the production unit. This article outlines the principles of Industry 4.0, emphasizing the features, requirements, and challenges of Industry 4.0. Besides, a development of the virtual model of the production line, there is also developed virtual model of the Autonomous Mobile Robots (AMR). This gives a good opportunity to monitor and analyze the entire production cycle, including the throughput, lead time, and utilization of resources in a 3D simulation production environment. Moreover, the article focuses on collecting real-time data from the virtual production unit to analyze the methods and locations of data collection, which would provide the most valuable information about production data. Finally, based on the results of the collected data, the authors present and discuss the challenges and trends that lie ahead when the same data collection methods are being used for physical production units. A case study approach is used to demonstrate the relevance and feasibility of the proposed methods for real-time data acquisition in production, which uses the concept of internet of things technologies and 3D visualization.
A Review of SQL vs NoSQL Database for Nuclear Reactor Digital Twin Applications: With Example MongoDB Based NoSQL Database for Digital Twin Model of a Pressurized-Water-Reactor Steam-Generator
Authors: Mohanty Subhasish , Elmer Thomas W. , Bakhtiari Sasan , Vilim Richard B.
Published: January 2022

In this paper a summary of the differences between structured query language (SQL) based traditional relational database management systems (RDBMS) and recently popular NoSQL based database are presented. The importance of selecting a NoSQL database for the implementation of digital Twin (DT) framework for nuclear reactor predictive maintenance has been discussed. Example of commercially available MongoDB based NoSQL database implementation with storing data from various sources: such as from virtual time-series temperature measurements (based on finite element-based heat transfer models) at thousands of nodal locations and from real sensor measurements (from continuous online monitoring based active ultrasonic sensors and from non-continuous nondestructive testing (NDT) based eddy current measurements is demonstrated.
Geometric Modeling and Characterization of Wall Thickness for Complex Cylindrical Thin-Walled Parts With Uncertain Manufacturing Deviations
Authors: Chen Pengyuan , Liu Shun , Jin Sun , Gu Qunfei
Published: January 2022

The complex cylindrical thin-walled parts have been widely used in manufacturing field with high precision requirement of wall thickness. However, due to the existence of initial uncertain manufacturing deviations, such as blank casting errors, initial shape deviations and clamping deformations, there will be a large geometric deviation between real blank workpiece and theoretical design model. In order to solve the problem in lacking of reliable geometrical model for trajectory planning in face milling of complex cylindrical thin-walled parts, this paper proposes a method for accurate modeling and characterization of wall thickness considering the uncertain geometric deviations based on point cloud reconstruction. First, based on feature extraction and meshing process of point clouds measured with high-definition metrology both for inner and outer surfaces, subregions of each point cloud are adaptively fitted into small curved surfaces. And then NURBS surfaces are applied to connect adjacent subregions in order to form a high-precision solid model of entire workpiece. Furthermore, a characterization method of real wall thickness in milling areas is proposed. Based on the sampling mesh nodes of inner surface being the interesting points for milling areas, wall thicknesses are characterizing as distances between those sampling nodes and the outer meshed surface with an improved KD-Tree algorithm. Based on the proposed method, a meshed CAD model of real workpiece can be constructed with an error range within 0.2mm; and the wall thicknesses of milling areas can be characterized by the extracted sampling nodes. It provided an efficient methodology for geometric modeling and characterization of wall thickness of complex cylindrical thin-walled parts, which is useful for milling trajectory planning.
Recent Advances in Thin-Wall Additively Manufactured Heat Exchangers
Authors: Muley Arun , Stoia Michael , Van Affelen Douglas , Reddy Venkateswara , Duggirala Vyas , Locke Summer
Published: January 2022

Efficient and affordable multidisciplinary thermal management technologies are becoming increasingly important in emerging aviation, power, and electronics cooling applications. High performance compact thermal devices are needed for existing and emerging aerospace systems, which include commercial / military, hybrid-electric, urban air mobility and high-speed air vehicles, among others. Advances in additive manufacturing (AM), materials, and design processes play a critical role in development of next-generation AM-enabled heat exchanger technology. This paper reports recent Boeing-led breakthrough advances in thin-wall (150–175 microns) metallic AM and its use in conformal heat exchangers and cold plates for aircraft engine precooler and electronic cooling applications, respectively. A model based engineering (MBE) approach is used in developing highly efficient AM heat exchanger designs. Their performance benefits are validated using a complimentary numerical simulation and experimental performance measurement method. The measured heat exchanger effectiveness and pressure drop in AM-produced brick-shaped and conformal heat exchangers are in good agreement with model predictions, within ∼5% and 20%, respectively. An overview of steps required for achieving technical maturity and production readiness of metal AM heat exchanger and cold plate technology, along with an envisioned roadmap for adoption of this technology in aviation platforms, is presented.
Recent Developments in Processing and Characterization of Metal Foam: Review
Authors: Zahoor Asima , Mourad Abdel-Hamid I.
Published: January 2022

Metal foam is a novel class of metals that is inspired by naturally occurring, functionally graded, cellular structures like wood and bones. Owing to the diverse physical and mechanical properties that can be tailored to suit a particular need, metal foams have become attractive to researchers and efforts are being made to optimize the methodology to develop the metal foam. This paper presents an overview of all the traditional and the state of art manufacturing methods adopted till date with their advantages and limitations as well. The manuscript further investigates characterization techniques used to characterize metal foam. Also, a summary of the most relevant applications with actual examples is presented.
Solving a Profited 3D Bin Packing Problem Using a Hybrid Genetic Algorithm
Authors: Weiss Cohen Miri
Published: January 2022

This work deals with a problem incorporating two well-known problems: the 3D Bin-packing problem (3D-BPP), and the Knapsack problem. Given the complexity of the problem a hybrid Genetic Algorithm (GA) methodology combining the Deepest Bottom Left Fill (DBLF) heuristics within the GA is presented. The research compares between two methods a basic genetic algorithm based on combining a penalty procedure and the hybrid method. In both cases constraints and various weights are assigned for different characteristics of the 3D packages. Different simulations were tested to find the best GA parameters influencing the convergence of the solutions. The resulted parameters were used when simulating for different number of populations sizes, mutations and number of generations. The hybrid approach achieved much better results by generating satisfying permutation sequences, which improves the efficiency of the 3D packing layouts and maximizing the overall value.
Heave Motion Prediction of Rectangular Floating Barge Using Artificial Neural Network
Authors: Awal Zobair Ibn , Mehtaj Nafisa , Pranto Rakin Ishmam
Published: January 2022

Motion response prediction at the design stage of a vessel can ameliorate the performance of any floating structure. Many naval operations and offshore activities such as oil and gas exploration, aircraft landing, mooring, berthing, etc. are motion-sensitive. Hence, it is apparent, that motion response plays a vital role in these cases and, to keep it to a minimum while designing a vessel, motion prediction is essential. Traditional ways of predicting motion response require a wide range of parameters, which may not be available at the early stage of the design. Besides a significant amount of computation time and human efforts are also necessary. Artificial Intelligence can be beneficial to overcome the aforesaid issues. In this research, the architecture of the neural network model has been explored. A hybrid model is developed using Artificial Neural Network and Lewis Form method along with the numerical solution. The principal particulars of vessels and heave motion responses have been fed to the model to learn the behavior of the vessels with respect to time in presence of excitation force. Based on 15 to 30 seconds of simulation, the trained model can predict the heave motion of a vessel efficiently.
Detection of Fatigue Damage in Aluminum Alloy Structures Using Nonlinear Ultrasonic Modulation
Authors: Yan Ling , Luo Lijia , Zhong Fengping , Zhao Zuming , Fan Jingjing , Huang Liuyi , Bao Shiyi , Mao Jianfeng
Published: January 2022

In this paper, a nonlinear ultrasonic modulation method is developed to detect early fatigue damage in aluminum alloy structures. Seven aluminum alloy specimens with different fatigue damage degrees are prepared by fatigue test. An experimental system is designed for performing nonlinear ultrasound modulation detection of fatigue damage in aluminum alloy specimens. A single piezoceramic transducer is used to emit two superposed sinusoidal waves with different ultrasonic frequencies. The higher frequency is chosen as a non-integer multiple of the lower frequency so as to be distinguished from super-harmonic responses. The dependencies of the nonlinear modulation response on the frequency and amplitude of excitation signals are explored to select appropriate signal excitation parameters. A nonlinear modulation index (NMI) is defined as the amplitude ratio of modulation responses and linear responses, which is used to evaluate fatigue damage of specimens. Experimental results indicate that the NMI increases monotonically with the degree of fatigue damage, and it can be used to quantify the accumulation of fatigue damage in specimens. The proposed nonlinear ultrasound modulation method facilitates the detection of fatigue damage and further assessment of the severity of the damage in aluminum alloy structures.
Virus Detection and Medical Diagnostics Student Projects for the Internet of Medical Things
Authors: Mauk Michael G. , Chiou Yunshun (Richard) , Tseng Tzu-Liang (Bill)
Published: January 2022

The importance of medical diagnostics, and specifically, tests for infectious agents such as viruses, is well recognized, and in fact, are a crucial component of efforts to control pandemics and mitigate their effects on public health. A long-established trend is the development of low-cost, easy-to-use point-of-care (POC) diagnostics to provide pervasive, timely testing independent of laboratories and other medical infrastructure. The technology of POC tests is widely accessible to engineering students, and there are many opportunities and avenues for innovation, including Smartphone-based platforms and integration with the Internet of Medical Things (IoMT). We discuss the design, demonstration, and testing of POC virus testing, including tests applicable to COVID-19, as Senior Design Projects for undergraduate mechanical, electrical, and manufacturing engineering majors.
Topology Optimization of Cracked Structures With Manufacturing Constraints
Authors: Habibian Anahita , Sohouli Abdolrasoul , Suleman Afzal
Published: January 2022

Topology optimization (TO) is a tool used in the design process to improve the structure’s performance, e.g. increasing the stiffness. However, the final topologies typically include complex shapes making them impractical or unrealizable from a fabrication point-of-view. This issue stems from the limitations that the currently available manufacturing methods have, such as milling. Hence, considering these limits as the manufacturing constraints into the optimization problem would shorten the post-processing time. Moreover, the existence of initial damage such as cracks is another common issue of engineering structures, resulting in unreliable topologies if neglected. These two challenges are the main motivations of this study. A projection method is utilized to restrict the range of solutions to a manufacturable design domain by projecting the design variable into a pseudo-density field. The main advantage of this approach is that the manufacturing restrictions are achieved naturally, without adding constraints to the TO problem, which leads to a straightforward calculation of sensitivities. This technique allows the designer to define, for instance, a minimum member size as well as a minimum hole size as geometrical constraints. In order to penalize the intermediate phases, the popular Solid Isotropic Material with Penalty method (SIMP) is employed. The Peri-dynamics theory is used to study the structure’s behaviour because of its ability to introduce discontinuities without requiring complicated mathematical expressions as opposed to its counterpart mesh-based approaches. The robustness of the proposed method is demonstrated through some compliance minimization problems.
In-Situ Scanning Electron Microscope Chemical Vapor Deposition as a Platform for Nanomanufacturing Insights
Authors: Koerner Gordon , Surya Ramakrishna , Palaniappan Kannappan , Calyam Prasad , Bunyak Filiz , Maschmann Matthew R.
Published: January 2022

While the physical properties of carbon nanotubes (CNTs) are often superior to conventional engineering materials, their widespread adoption into many applications is limited by scaling the properties of individual CNTs to macroscale CNT assemblies known as CNT forests. The self-assembly mechanics of CNT forests that determine their morphology and ensemble properties remain poorly understood. Few experimental techniques exist to characterize and observe the growth and self-assembly processes in situ. Here we introduce the use of in-situ scanning electron microscope (SEM) synthesis based on chemical vapor deposition (CVD) processing. In this preliminary report, we share best practices for in-situ SEM CVD processing and initial CNT forest synthesis results. Image analysis techniques are developed to identify and track the movement of catalyst nanoparticles during synthesis conditions. Finally, a perspective is provided in which in-situ SEM observations represent one component of a larger system in which numerical simulation, machine learning, and digital control of experiments reduces the role of humans and human error in the exploration of CNT forest process-structure-property relationships.
Design of a Horizontal Axis Wind Turbine for a Venusian Environment
Authors: Garza Zacharias , Pan Kevin , Izaguirre Anthony , Loza Saul , Serrano Jonathan , Lopez Oscar , Kuo Jim , Sauder Jonathan
Published: January 2022

The atmosphere of Venus is 96% carbon dioxide and contains clouds of sulfur dioxide and sulfuric acid, with surface temperatures in excess of 470°C and pressures 92 times that of Earth. These extreme environmental conditions make planetary exploration difficult, as modern electronics cannot survive for prolonged periods of time. Photovoltaics, a conventional power generation method for Mars rovers, are inefficient on the planet’s surface due to the dense cloud cover and harsh environment. The NASA – JPL Hybrid Automaton Rover Venus proposes using a mechanical wind energy harvester to further explore the Venusian surface. At the proposed landing site, the surface wind speeds range from 0.3 to 1.3 m/s with an average wind speed of 0.6 m/s. These wind speeds, combined with the high density of Venusian air, results in promising potential for power generation. The power goal for the proposed wind harvester is 9W at the average wind speed of 0.6 m/s. A horizontal axis wind turbine (HAWT) is used to avoid dynamic stall experienced by vertical axis wind turbines at low wind speeds.

In the HAWT, existing airfoil profiles were evaluated and chosen using an iterative design process. The blade designs were analyzed using blade element momentum theory (BEM) to predict and improve turbine performance. Testing was performed in water, as the greater fluid density allowed for testing at a lower speed than in air to better simulate Venus surface conditions.

The preliminary water testing was carried out to characterize turbine performance. In this process, a 3D printed PLA 1:4 scale turbine was placed in an open-channel pool with flow supplied through a pump. The turbine was a fixed 2.3 m distance away from the inlet of the flow. The flow speed, turbine rotational speed, and torque produced were recorded. The results yielded turbine efficiencies between 7.7% and 46.1%. These results exceeded design expectations at the designed TSR, where an efficiency of 40% was to be expected.

Based on the preliminary results, modifications are being made to the water testbed to improve the testing process as well as more accurately simulate conditions on the surface of Venus. The collected data and the aforementioned design tools are used to improve the current turbine design.
Physicochemical Characterization of the Rejected Waste From the Mechanical and Biological Treatment of Municipal Solid Waste
Authors: Oliveira Victor , Castro Fernando , Teixeira Jose Carlos , Araujo Jorge , Ribeiro Andre , Carvalho Joana , Vilarinho Maria Cândida
Published: January 2022

Mechanical and Biological Treatment units (MBT), are a key element in the management of municipal solid waste as they are the preferred route for minimization of the amount of waste delivered to landfills. However, taking into account the waste received by the MBT’s, it is observed that a significant part is sent to controlled landfills without any kind of valorization. Within this framework, a characterization of the rejected waste produced in one of these facilities was carried out in order to assess their potential for energy recovery. The rejected waste is composed by different fractions, namely paper, textiles, plastic films, hard plastics, glass, metal, organic matter and undifferentiated waste. In the present work the rejected waste from a MTB unit is sorted into different classes and each one is fully characterized and assessed as an energy source. For the physicochemical characterization of different residues the following tests were performed: weight loss at 105 °C, elemental analysis, the determination of the heating value, thermogravimetric analysis and FTIR spectroscopy. Considering the potential for energetic recovery of the different fractions, only paper, textiles, hard plastics and plastic films were characterized because of the high moisture content of organic material. According to the results, there is a great variability of the physical-chemical properties within the various samples, both in composition and moisture. Most notably the polymer based residues (hard plastics and plastic films) show a heating value that compares favorably with both traditional biomasses and fossil fuels. Therefore, providing their acceptance in terms of toxicity, they can provide a valuable source of energy and contribute to the reduction of waste delivered to landfills.
A Study on Degradation of Lithium-Ion Batteries for In Aircraft Applications
Authors: Alhadri Muapper , Zakri Waleed , Esmaeeli Roja , Farhad Siamak
Published: January 2022

Some lithium-ion batteries (LIBs) applications are mobile and stationary energy storage systems such as electric vehicles (full electric, hybrid and plug-in hybrid), different types of aircraft, and renewable energy technologies. LIBs can play a tremendous role in replacing the considerable dependence of nations on fossil fuels due to the high storage energy density of renewable energy alternatives. They also have a high cost associated with the material costs, as well as the other manufacturing costs. The environmental impact of manufacturing and disposing lithium-ion batteries has triggered actions to reduce the resulting impact by recycling batteries.

Degradation of LIBs is always a concern for any application. To predict the life of cells correctly, it is more efficient to cover all mechanisms leading to capacity-fade or resistance growth. This study discusses the degradation rate of a LIB at two stages of life for both first- and second-uses. Empirical models can be used to measure capacity-fade, resulting from the battery degradation. The duty-cycle of the commercial LIB for a typical passenger aircraft, such as the Bombardier CRJ200, was obtained. For this purpose, the velocity and altitude of the aircraft were monitored during a typical flight, and the instantaneous mechanical power of the aircraft was obtained by modeling. Then, the duty-cycle of a LIB cell in the battery pack was yielded. The life prediction of the LIB in electrical energy storage for aircraft was studied. Although many studies have been done to evaluate performance and durability of LIB cells and packs for vehicle applications, there are very few studies of the application of LIBs in electric and hybrid aircraft. The degradation rate of the battery for a typical lightweight passenger aircraft with a flight range of less than 1000 km was then presented by using an empirical modeling method. The results showed that the A123 battery (20 Ah) degraded after 6 months at 45°C and 80% State of charge (SOC) and after 1.8 years at 45°C and 80% depth of discharge (DOD) by assuming that the battery should be retired when the rate of degradation reaches 15% of its nominal capacity. It was found that a retired first-use LIB cell is durable enough to be utilized in many second-use applications which can have lots of environmental benefits. Several items related to second use of LIBs will be discussed in this paper.