bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Prediction of Fatigue Life Basing Random Forest Algorithm
Authors: Yin Chenfei , Yang Yu 
Published: January 2022

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.