scholarly journals Analytical Modelling of Temperature Distribution in SLM Process with Consideration of Scan Strategy Difference between Layers

Materials ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 1869
Author(s):  
Linger Cai ◽  
Steven Y. Liang
Keyword(s):  
Temperature Distribution ◽  
Large Scale ◽  
Heating And Cooling ◽  
Melt Pool ◽  
Proposed Model ◽  
Starting Point ◽  
Layer Effect ◽  
Strategy Difference ◽  
Property Changes ◽  
Analytical Thermal Model

In the practical selective laser melting (SLM) manufacturing process, the scan strategy often varies between layers to avoid overlapping of the melted area, which affects the residual stress and deflection of the final build. Yet not much modelling work has been done to accommodate the angle between layers. The paper proposed an analytical thermal model to address the scan strategy difference, such as laser scan direction difference between layers, which brings the model closer to the practical scan situation. The analytical transient moving point heat solution is adopted in this model. The laser movement is first considered in a laser coordinates, which originates at the laser radiation spot, and then transferred into a stationary coordinate, which originates at the starting point of the build. The model takes account of multi-track and multi-layer effect by considering thermal property changes caused by remaining heat, which is further adopted for temperature distribution calculation. The scan direction difference leads to different laser path at each layer, and alters heating and cooling time for a specific point on the build. The proposed model is validated by comparing the predicted melt pool geometries to documented experimental data. The effect of scan direction difference between layers is further discussed in the later part. It is found that the uni- and bi- directional scan leads to diverse temperature profile but its effect on melt depth is not significant. Although the laser rotation angle between layers leads to changes in the melt depth, it is not in a large scale. The proposed model shows that scan strategy does not change melt pool geometry in a significant scale but affects the thermal profile as well as thermal history. It can be used as a step for further modelling work for porosity and deflection.

scholarly journals Thermal Modeling of Temperature Distribution in Metal Additive Manufacturing Considering Effects of Build Layers, Latent Heat, and Temperature-Sensitivity of Material Properties

2018 ◽  
Vol 2 (3) ◽  
pp. 63 ◽  
Author(s):  
Elham Mirkoohi ◽  
Jinqiang Ning ◽  
Peter Bocchini ◽  
Omar Fergani ◽  
Kuo-Ning Chiang ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Temperature Distribution ◽  
Temperature Profile ◽  
Latent Heat ◽  
Temperature Sensitivity ◽  
Material Properties ◽  
Metal Additive Manufacturing ◽  
Melt Pool ◽  
Proposed Model ◽  
Metal Additive

A physics-based analytical model is proposed in order to predict the temperature profile during metal additive manufacturing (AM) processes, by considering the effects of temperature history in each layer, temperature-sensitivity of material properties and latent heat. The moving heat source analysis is used in order to predict the temperature distribution inside a semi-infinite solid material. The laser thermal energy deposited into a control volume is absorbed by the material thermodynamic latent heat and conducted through the contacting solid boundaries. The analytical model takes in to account the typical multi-layer aspect of additive manufacturing processes for the first time. The modeling of the problem involving multiple layers is of great importance because the thermal interactions of successive layers affect the temperature gradients, which govern the heat transfer and thermal stress development mechanisms. The temperature profile is calculated for isotropic and homogeneous material. The proposed model can be used to predict the temperature in laser-based metal additive manufacturing configurations of either direct metal deposition or selective laser melting. A numerical analysis is also conducted to simulate the temperature profile in metal AM. These two models are compared with experimental results. The proposed model also well captured the melt pool geometry as it is compared to experimental values. In order to emphasize the importance of solving the problem considering multiple layers, the peak temperature considering the layer addition and peak temperature not considering the layer addition are compared. The results show that considering the layer addition aspect of metal additive manufacturing can help to better predict the surface temperature and melt pool geometry. An analysis is conducted to show the importance of considering the temperature sensitivity of material properties in predicting temperature. A comparison of the computational time is also provided for analytical and numerical modeling. Based on the obtained results, it appears that the proposed analytical method provides an effective and accurate method to predict the temperature in metal AM.

Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

2018 ◽  
pp. 45-51
Author(s):  
A. V. Ponomarev
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Preference Relation ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Comparison Method ◽  
Characteristic Matrix ◽  
Image Tagging ◽  
Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems). 

Identification of N-Substituted Triazolo-azetidines as Novel Antibacterials using pDualrep2 HTS Platform

2019 ◽  
Vol 22 (5) ◽  
pp. 346-354
Author(s):  
Yan A. Ivanenkov ◽  
Renat S. Yamidanov ◽  
Ilya A. Osterman ◽  
Petr V. Sergiev ◽  
Vladimir A. Aladinskiy ◽  
...  
Keyword(s):  
Antibacterial Activity ◽  
Large Scale ◽  
Underlying Mechanism ◽  
Luciferase Assay ◽  
Reporter System ◽  
E Coli ◽  
Starting Point ◽  
High Concentrations ◽  
Mic Value

Aim and Objective: Antibiotic resistance is a serious constraint to the development of new effective antibacterials. Therefore, the discovery of the new antibacterials remains one of the main challenges in modern medicinal chemistry. This study was undertaken to identify novel molecules with antibacterial activity. Materials and Methods: Using our unique double-reporter system, in-house large-scale HTS campaign was conducted for the identification of antibacterial potency of small-molecule compounds. The construction allows us to visually assess the underlying mechanism of action. After the initial HTS and rescreen procedure, luciferase assay, C14-test, determination of MIC value and PrestoBlue test were carried out. Results: HTS rounds and rescreen campaign have revealed the antibacterial activity of a series of Nsubstituted triazolo-azetidines and their isosteric derivatives that has not been reported previously. Primary hit-molecule demonstrated a MIC value of 12.5 µg/mL against E. coli Δ tolC with signs of translation blockage and no SOS-response. Translation inhibition (26%, luciferase assay) was achieved at high concentrations up to 160 µg/mL, while no activity was found using C14-test. The compound did not demonstrate cytotoxicity in the PrestoBlue assay against a panel of eukaryotic cells. Within a series of direct structural analogues bearing the same or bioisosteric scaffold, compound 2 was found to have an improved antibacterial potency (MIC=6.25 µg/mL) close to Erythromycin (MIC=2.5-5 µg/mL) against the same strain. In contrast to the parent hit, this compound was more active and selective, and provided a robust IP position. Conclusion: N-substituted triazolo-azetidine scaffold may be used as a versatile starting point for the development of novel active and selective antibacterial compounds.

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

scholarly journals Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1670
Author(s):  
Waheeb Abu-Ulbeh ◽  
Maryam Altalhi ◽  
Laith Abualigah ◽  
Abdulwahab Ali Almazroi ◽  
Putra Sumari ◽  
...  
Keyword(s):  
Data Analysis ◽  
Structural Equation ◽  
Large Scale ◽  
Review Paper ◽  
Essential Element ◽  
Routine Activities ◽  
Criminological Theory ◽  
Equation Modeling ◽  
Future Research ◽  
Proposed Model

Cyberstalking is a growing anti-social problem being transformed on a large scale and in various forms. Cyberstalking detection has become increasingly popular in recent years and has technically been investigated by many researchers. However, cyberstalking victimization, an essential part of cyberstalking, has empirically received less attention from the paper community. This paper attempts to address this gap and develop a model to understand and estimate the prevalence of cyberstalking victimization. The model of this paper is produced using routine activities and lifestyle exposure theories and includes eight hypotheses. The data of this paper is collected from the 757 respondents in Jordanian universities. This review paper utilizes a quantitative approach and uses structural equation modeling for data analysis. The results revealed a modest prevalence range is more dependent on the cyberstalking type. The results also indicated that proximity to motivated offenders, suitable targets, and digital guardians significantly influences cyberstalking victimization. The outcome from moderation hypothesis testing demonstrated that age and residence have a significant effect on cyberstalking victimization. The proposed model is an essential element for assessing cyberstalking victimization among societies, which provides a valuable understanding of the prevalence of cyberstalking victimization. This can assist the researchers and practitioners for future research in the context of cyberstalking victimization.

scholarly journals Urban Policies and Large Projects in Central City Areas: The Example of Madrid (Spain)

Urban Science ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 42
Author(s):  
Dolores Brandis García
Keyword(s):  
Urban Planning ◽  
20Th Century ◽  
Large Scale ◽  
Central City ◽  
Global Market ◽  
Late 20Th Century ◽  
Public And Private ◽  
Urban Policies ◽  
Starting Point ◽  
The City

Since the late 20th century major, European cities have exhibited large projects driven by neoliberal urban planning policies whose aim is to enhance their position on the global market. By locating these projects in central city areas, they also heighten and reinforce their privileged situation within the city as a whole, thus contributing to deepening the centre–periphery rift. The starting point for this study is the significance and scope of large projects in metropolitan cities’ urban planning agendas since the final decade of the 20th century. The aim of this article is to demonstrate the correlation between the various opposing conservative and progressive urban policies, and the projects put forward, for the city of Madrid. A study of documentary sources and the strategies deployed by public and private agents are interpreted in the light of a process during which the city has had a succession of alternating governments defending opposing urban development models. This analysis allows us to conclude that the predominant large-scale projects proposed under conservative policies have contributed to deepening the centre–periphery rift appreciated in the city.

scholarly journals Stator Non-Uniform Radial Ventilation Design Methodology for a 15 MW Turbo-Synchronous Generator Based on Single Ventilation Duct Subsystem

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2760
Author(s):  
Ruiye Li ◽  
Peng Cheng ◽  
Hai Lan ◽  
Weili Li ◽  
David Gerada ◽  
...  
Keyword(s):  
Finite Element ◽  
Temperature Distribution ◽  
Design Methodology ◽  
Large Scale ◽  
Synchronous Generator ◽  
Axial Direction ◽  
Scale Model ◽  
Element Analysis ◽  
Axial Temperature ◽  
Ventilation Duct

Within large turboalternators, the excessive local temperatures and spatially distributed temperature differences can accelerate the deterioration of electrical insulation as well as lead to deformation of components, which may cause major machine malfunctions. In order to homogenise the stator axial temperature distribution whilst reducing the maximum stator temperature, this paper presents a novel non-uniform radial ventilation ducts design methodology. To reduce the huge computational costs resulting from the large-scale model, the stator is decomposed into several single ventilation duct subsystems (SVDSs) along the axial direction, with each SVDS connected in series with the medium of the air gap flow rate. The calculation of electromagnetic and thermal performances within SVDS are completed by finite element method (FEM) and computational fluid dynamics (CFD), respectively. To improve the optimization efficiency, the radial basis function neural network (RBFNN) model is employed to approximate the finite element analysis, while the novel isometric sampling method (ISM) is designed to trade off the cost and accuracy of the process. It is found that the proposed methodology can provide optimal design schemes of SVDS with uniform axial temperature distribution, and the needed computation cost is markedly reduced. Finally, results based on a 15 MW turboalternator show that the peak temperature can be reduced by 7.3 ∘C (6.4%). The proposed methodology can be applied for the design and optimisation of electromagnetic-thermal coupling of other electrical machines with long axial dimensions.

scholarly journals Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Junshu Wang ◽  
Guoming Zhang ◽  
Wei Wang ◽  
Ka Zhang ◽  
Yehua Sheng
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Medical Service ◽  
Rapid Development ◽  
Medical Knowledge ◽  
Language Models ◽  
Computing Environment ◽  
Computing Power ◽  
Cloud Computing Environment ◽  
Proposed Model

AbstractWith the rapid development of hospital informatization and Internet medical service in recent years, most hospitals have launched online hospital appointment registration systems to remove patient queues and improve the efficiency of medical services. However, most of the patients lack professional medical knowledge and have no idea of how to choose department when registering. To instruct the patients to seek medical care and register effectively, we proposed CIDRS, an intelligent self-diagnosis and department recommendation framework based on Chinese medical Bidirectional Encoder Representations from Transformers (BERT) in the cloud computing environment. We also established a Chinese BERT model (CHMBERT) trained on a large-scale Chinese medical text corpus. This model was used to optimize self-diagnosis and department recommendation tasks. To solve the limited computing power of terminals, we deployed the proposed framework in a cloud computing environment based on container and micro-service technologies. Real-world medical datasets from hospitals were used in the experiments, and results showed that the proposed model was superior to the traditional deep learning models and other pre-trained language models in terms of performance.

scholarly journals Contracting in land and natural resources: a tale of exclusion

2021 ◽  
Vol 17 (1) ◽  
pp. 145-153
Author(s):  
Federica Violi
Keyword(s):  
Natural Resources ◽  
Large Scale ◽  
Sustainable Development Goals ◽  
Host Countries ◽  
Developmental Needs ◽  
Starting Point ◽  
The World ◽  
Development Goals ◽  
Short Essay ◽  
Public Environment

By browsing the website of Land Matrix, one can measure the extent of land-related large-scale investments in natural resources (LRINRs) and place it on the world map. At the time of writing, the extent of these investments covers an area equal to the surfaces of Spain and Portugal together – or, for football fans, around 60 million football pitches. These investment operations have often been saluted as instrumental to achieve the developmental needs of host countries and as the necessary private counterpart to state (and interstate) efforts aimed at (sustainable) development goals. Yet, the realities on the ground offer a scenario characterised by severe instances of displacement of indigenous or local communities and environmental disruptions. The starting point of this short essay is that these ‘externalities’ are generated through the legal construct enabling the implementation of these investment operations. As such, this contribution lies neatly in the line of research set forth in the excellent books of Kinnari Bhatt and Jennifer Lander, from the perspective of both the development culture shaping these investment operations and the private–public environment in which these are situated. The essay tries and dialogues with both components, while focusing at a metalevel on the theoretical shifts potentially geared to turn a ‘tale of exclusion’ into a ‘tale of inclusion’.

scholarly journals Assessing the Response of Snow Avalanche Runout Altitudes to Climate Fluctuations Using Hierarchical Modeling: Application to 61 Winters of Data in France

2010 ◽  
Vol 23 (12) ◽  
pp. 3157-3180 ◽  
Author(s):  
N. Eckert ◽  
H. Baya ◽  
M. Deschatres
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Hierarchical Modeling ◽  
Control Measures ◽  
Level Shift ◽  
Snow Avalanches ◽  
Winter Climate ◽  
Climate Fluctuations ◽  
High Magnitude ◽  
Proposed Model

Abstract Snow avalanches are natural hazards strongly controlled by the mountain winter climate, but their recent response to climate change has thus far been poorly documented. In this paper, hierarchical modeling is used to obtain robust indexes of the annual fluctuations of runout altitudes. The proposed model includes a possible level shift, and distinguishes common large-scale signals in both mean- and high-magnitude events from the interannual variability. Application to the data available in France over the last 61 winters shows that the mean runout altitude is not different now than it was 60 yr ago, but that snow avalanches have been retreating since 1977. This trend is of particular note for high-magnitude events, which have seen their probability rates halved, a crucial result in terms of hazard assessment. Avalanche control measures, observation errors, and model limitations are insufficient explanations for these trends. On the other hand, strong similarities in the pattern of behavior of the proposed runout indexes and several climate datasets are shown, as well as a consistent evolution of the preferred flow regime. The proposed runout indexes may therefore be usable as indicators of climate change at high altitudes.

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