scholarly journals Monte Carlo simulation based prediction model for ultimate tensile strength of Friction Stir Welded AA6063-ETP copper joint

2020 ◽  
Vol 810 ◽  
pp. 012057
Author(s):  
N J Panaskar ◽  
R P Terkar
Keyword(s):  
Monte Carlo Simulation ◽  
Tensile Strength ◽  
Monte Carlo ◽  
Ultimate Tensile Strength ◽  
Prediction Model ◽  
Friction Stir ◽  
Simulation Based

scholarly journals Monte Carlo comparison of estimation methods for the two-parameter lognormal distribution

2019 ◽  
Vol 254 ◽  
pp. 08002
Author(s):  
Ivana Pobočíková ◽  
Zuzana Sedliačková ◽  
Mária Michalková ◽  
Lenka Kuchariková
Keyword(s):  
Monte Carlo Simulation ◽  
Tensile Strength ◽  
Monte Carlo ◽  
Ultimate Tensile Strength ◽  
Lognormal Distribution ◽  
Real Data ◽  
Estimation Methods ◽  
Mean Square ◽  
Data Set ◽  
Two Parameter

In the paper we compare performance of estimation methods for the two-parameter lognormal distribution via the Monte Carlo simulation. The comparison of performances is made with respect to their biases, variances, root mean square error. The methods are applied on real data set representing experimentally obtained values of ultimate tensile strength of material.

An appraisal of characteristic mechanical properties and microstructure of friction stir welding for Aluminium 6061 alloy – Silicon Carbide (SiCp) metal matrix composite

2019 ◽  
Vol 13 (4) ◽  
pp. 5804-5817
Author(s):  
Ibrahim Sabry
Keyword(s):  
Tensile Strength ◽  
Ultimate Tensile Strength ◽  
Metal Matrix Composite ◽  
Matrix Composite ◽  
Metal Matrix ◽  
Welded Joint ◽  
Rotation Speed ◽  
Fusion Welding ◽  
Friction Stir ◽  
Al 6061

It is expected that the demand for Metal Matrix Composite (MMCs) will increase in these applications in the aerospace and automotive industries sectors, strengthened AMC has different advantages over monolithic aluminium alloy as it has characteristics between matrix metal and reinforcement particles.  However, adequate joining technique, which is important for structural materials, has not been established for (MMCs) yet. Conventional fusion welding is difficult because of the irregular redistribution or reinforcement particles.  Also, the reaction between reinforcement particles and aluminium matrix as weld defects such as porosity in the fusion zone make fusion welding more difficult. The aim of this work was to show friction stir welding (FSW) feasibility for entering Al 6061/5 to Al 6061/18 wt. % SiCp composites has been produced by using stir casting technique. SiCp is added as reinforcement in to Aluminium alloy (Al 6061) for preparing metal matrix composite. This method is less expensive and very effective. Different rotational speeds,1000 and 1800 rpm and traverse speed 10 mm \ min was examined. Specimen composite plates having thick 10 mm were FS welded successfully. A high-speed steel (HSS) cylindrical instrument with conical pin form was used for FSW. The outcome revealed that the ultimate tensile strength of the welded joint (Al 6061/18 wt. %) was 195 MPa at rotation speed 1800 rpm, the outcome revealed that the ultimate tensile strength of the welded joint (Al 6061/18 wt.%) was 165 MPa at rotation speed 1000 rpm, that was very near to the composite matrix as-cast strength. The research of microstructure showed the reason for increased joint strength and microhardness. The microstructural study showed the reason (4 %) for higher joint strength and microhardness.  due to Significant   of SiCp close to the boundary of the dynamically recrystallized and thermo mechanically affected zone (TMAZ) was observed through rotation speed 1800 rpm. The friction stir welded ultimate tensile strength Decreases as the volume fraction increases of SiCp (18 wt.%).

scholarly journals Voronoi diagram and Monte-Carlo simulation based finite element optimization for cost-effective 3D printing

2021 ◽  
Vol 50 ◽  
pp. 101301
Author(s):  
A.Z. Zheng ◽  
S.J. Bian ◽  
E. Chaudhry ◽  
J. Chang ◽  
H. Haron ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Finite Element ◽  
Monte Carlo ◽  
3D Printing ◽  
Voronoi Diagram ◽  
Cost Effective ◽  
Simulation Based

Monte Carlo Simulation of Texture and Microstructure Transformation during Annealing of Steel

2007 ◽  
Vol 129 ◽  
pp. 83-87
Author(s):  
Hua Long Li ◽  
Jong Tae Park ◽  
Jerzy A. Szpunar
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Grain Boundary ◽  
Computer Model ◽  
Graphical Representation ◽  
Structural Characteristics ◽  
Polycrystalline Materials ◽  
Virtual Experiments ◽  
Grain Boundary Character ◽  
Simulation Based

Controlling texture and microstructure evolution during annealing processes is very important for optimizing properties of steels. Theories used to explain annealing processes are complicated and always case dependent. An recently developed Monte Carlo simulation based model offers an effective tool for studying annealing process and can be used to verify the arbitrarily defined theories that govern such processes. The computer model takes Orientation Image Microscope (OIM) measurements as an input. The abundant information contained in OIM measurement allows the computer model to incorporate many structural characteristics of polycrystalline materials such as, texture, grain boundary character, grain shape and size, phase composition, chemical composition, stored elastic energy, and the residual stress. The outputs include various texture functions, grain boundary and grain size statistics that can be verified by experimental results. Graphical representation allows us to perform virtual experiments to monitor each step of the structural transformation. An example of applying this simulation to Si steel is given.

scholarly journals A Novel Monte-Carlo Simulation-Based Model for Malware Detection (eRBCM)

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2881
Author(s):  
Muath Alrammal ◽  
Munir Naveed ◽  
Georgios Tsaramirsis
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Information Systems ◽  
Malware Detection ◽  
New Model ◽  
Malicious Activity ◽  
Model Based ◽  
Simulation Based ◽  
Computer Based ◽  
Reinforcement Model

The use of innovative and sophisticated malware definitions poses a serious threat to computer-based information systems. Such malware is adaptive to the existing security solutions and often works without detection. Once malware completes its malicious activity, it self-destructs and leaves no obvious signature for detection and forensic purposes. The detection of such sophisticated malware is very challenging and a non-trivial task because of the malware’s new patterns of exploiting vulnerabilities. Any security solutions require an equal level of sophistication to counter such attacks. In this paper, a novel reinforcement model based on Monte-Carlo simulation called eRBCM is explored to develop a security solution that can detect new and sophisticated network malware definitions. The new model is trained on several kinds of malware and can generalize the malware detection functionality. The model is evaluated using a benchmark set of malware. The results prove that eRBCM can identify a variety of malware with immense accuracy.

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