scholarly journals Mechanical Properties and Microstructure of TIG and ATIG Welded 316L Austenitic Stainless Steel with Multi-Components Flux Optimization Using Mixing Design Method and Particle Swarm Optimization (PSO)

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7139
Author(s):  
Abdeljlil Chihaoui Hedhibi ◽  
Kamel Touileb ◽  
Rachid Djoudjou ◽  
Abousoufiane Ouis ◽  
Hussein Alrobei ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Stainless Steel ◽  
Particle Swarm Optimization ◽  
Design Method ◽  
Weld Zone ◽  
Particle Swarm ◽  
Inert Gas ◽  
Swarm Optimization ◽  
316L Austenitic Stainless Steel ◽  
The Impact

In this study, the effects of pseudo-ternary oxides on mechanical properties and microstructure of 316L stainless steel tungsten inert gas (TIG) and activating tungsten inert gas (ATIG) welded joints were investigated. The novelty in this work is introducing a metaheuristic technique called the particle swarm optimization (PSO) method to develop a mathematical model of the ultimate tensile strength (UTS) in terms of proportions of oxides flux. A constrained optimization algorithm available in Matlab 2020 optimization toolbox is used to find the optimal percentages of the selected powders that provide the maximum UTS. The study indicates that the optimal composition of flux was: 32% Cr2O3, 43% ZrO2, 8% Si2O, and 17% CaF2. The UTS was 571 MPa for conventional TIG weld and rose to 600 MPa for the optimal ATIG flux. The obtained result of hardness for the optimal ATIG was 176 HV against 175 HV for conventional TIG weld. The energy absorbed in the weld zone during the impact test was 267 J/cm2 for the optimal ATIG weld and slightly higher than that of conventional TIG weld 256 J/cm2. Fracture surface examined by scanning electron microscope (SEM) shows ductile fracture for ATIG weld with small and multiple dimples in comparison for TIG weld. Moreover, the depth of optimized flux is greater than that of TIG weld by two times. The ratio D/W was improved by 3.13 times. Energy dispersive spectroscopy (EDS) analysis shows traces of the sulfur element in the TIG weld zone.

scholarly journals Enhanced Particle Swarm Optimization for Path Planning of Unmanned Aerial Vehicles

2020 ◽  
Vol 14 (1) ◽  
pp. 67-78
Author(s):  
Kai Yit Kok ◽  
Parvathy Rajendran
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Aerial Vehicles ◽  
Trial And Error Method ◽  
The Impact ◽  
Enhanced Particle Swarm Optimization ◽  
Error Method

This paper presents an enhanced particle swarm optimization (PSO) for the path planning of unmanned aerial vehicles (UAVs). An evolutionary algorithm such as PSO is costly because every application requires different parameter settings to maximize the performance of the analyzed parameters. People generally use the trial-and-error method or refer to the recommended setting from general problems. The former is time consuming, while the latter is usually not the optimum setting for various specific applications. Hence, this study focuses on analyzing the impact of input parameters on the PSO performance in UAV path planning using various complex terrain maps with adequate repetitions to solve the tuning issue. Results show that inertial weight parameter is insignificant, and a 1.4 acceleration coefficient is optimum for UAV path planning. In addition, the population size between 40 and 60 seems to be the optimum setting based on the case studies.

scholarly journals Decision-Making of Underwater Cooperative Confrontation Based on MODPSO

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2211
Author(s):  
Na Wei ◽  
Mingyong Liu ◽  
Weibin Cheng
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Communication Delay ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Multi Objective ◽  
Decision Making Model ◽  
The Impact

This paper proposes a multi-objective decision-making model for underwater countermeasures based on a multi-objective decision theory and solves it using the multi-objective discrete particle swarm optimization (MODPSO) algorithm. Existing decision-making models are based on fully allocated assignment without considering the weapon consumption and communication delay, which does not conform to the actual naval combat process. The minimum opponent residual threat probability and minimum own-weapon consumption are selected as two functions of the multi-objective decision-making model in this paper. Considering the impact of the communication delay, the multi-objective discrete particle swarm optimization (MODPSO) algorithm is proposed to obtain the optimal solution of the distribution scheme with different weapon consumptions. The algorithm adopts the natural number coding method, and the particle corresponds to the confrontation strategy. The simulation result shows that underwater communication delay impacts the decision-making selection. It verifies the effectiveness of the proposed model and the proposed multi-objective discrete particle swarm optimization algorithm.

Shape Optimization Design of Gravity Buttress of Arch Dam Based on Asynchronous Particle Swarm Optimization Method

2014 ◽  
Vol 662 ◽  
pp. 160-163
Author(s):  
Lei Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Design Method ◽  
Particle Swarm ◽  
Optimization Method ◽  
Arch Dam ◽  
Swarm Optimization ◽  
Safety Coefficient ◽  
Parametric Description ◽  
Particle Swarm Optimization Method

The optimization design method was rarely used to design the gravity buttress of arch dam in the past. With this in mind, the parametric description of gravity buttress is given, and the auto-calculation of its exerting loads and the safety coefficient of anti-slide stability are realized subsequently. Then, the optimization design model of gravity buttress and the procedures of optimization design are presented using the asynchronous particle swarm optimization method. Finally, ODGB software, which is short for Optimization Design of Gravity Buttress software, is developed and verified.

Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization

2011 ◽  
Vol 2 (3) ◽  
pp. 43-69 ◽  
Author(s):  
Shi Cheng ◽  
Yuhui Shi ◽  
Quande Qin
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Population Diversity ◽  
Premature Convergence ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Boundary Constraints ◽  
Constraints Handling ◽  
The Impact ◽  
Search Information

Premature convergence happens in Particle Swarm Optimization (PSO) for solving both multimodal problems and unimodal problems. With an improper boundary constraints handling method, particles may get “stuck in” the boundary. Premature convergence means that an algorithm has lost its ability of exploration. Population diversity is an effective way to monitor an algorithm’s ability of exploration and exploitation. Through the population diversity measurement, useful search information can be obtained. PSO with a different topology structure and a different boundary constraints handling strategy will have a different impact on particles’ exploration and exploitation ability. In this paper, the phenomenon of particles gets “stuck in” the boundary in PSO is experimentally studied and reported. The authors observe the position diversity time-changing curves of PSOs with different topologies and different boundary constraints handling techniques, and analyze the impact of these setting on the algorithm’s ability of exploration and exploitation. From these experimental studies, an algorithm’s ability of exploration and exploitation can be observed and the search information obtained; therefore, more effective algorithms can be designed to solve problems.

scholarly journals Optimal Block Diagonalization precoding scheme for MU-MIMO Downlink channel Using Particle Swarm Optimization

2018 ◽  
Vol 7 (2.17) ◽  
pp. 90
Author(s):  
Shivangini Saxena ◽  
Dr R.P. Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Mimo Systems ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Base Station ◽  
Block Diagonalization ◽  
Swarm Optimization ◽  
Data Rates ◽  
Mimo Downlink ◽  
The Impact

As wireless communication turns out to be more common, the interest for higher rates of data transfer and continuous availability is expanding. Future wireless systems are provisioned to be very heterogeneous and interconnected. Higher data rates and Quality of Service (Qos) are two major expectations from any wireless technology. Fading is the main phenomenon which restricts the realization of Qos demand and higher data rates in wireless technologies. Fading is caused by obstacles in signal path which degrades the received signal’s quality. To mitigate the impact of fading on communication system the application of precoding techniques can be used. In this regard, this paper presents optimization of Block-Diagonalization (BD) based linear precoding scheme for multi-user multiple-input multiple output (MU-MIMO) systems. Simulation environment consists of a MIMO downlink scenario where a single base station (BS) with  antennas transmits to K receivers each with  antenna. The application of Particle Swarm Optimization (PSO) is used to find the optimal number of received antennas so as to reduce system complexity while maintaining Bit Error Rate (BER) performance of the system. MATLAB based simulation scenario is presented and evaluated over Rayleigh fading environment. Simulation results validate that the performance of Block– Diagonalization scheme can be improved up to 5dB with the application of Particle Swarm Optimization technique. 

scholarly journals Parameter Optimization of Gas Metal Arc Welding Process on AISI: 430 Stainless Steel Using Meta Heuristic Optimization Techniques

2020 ◽  
Author(s):  
P. Ravichandran ◽  
Meenakshipriya B ◽  
R. Parameshwaran ◽  
C. Maheswari ◽  
E.B. Priyanka ◽  
...  
Keyword(s):  
Stainless Steel ◽  
Particle Swarm Optimization ◽  
Process Parameters ◽  
Firefly Algorithm ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Heuristic Optimization ◽  
Depth Of Penetration ◽  
Swarm Optimization ◽  
Aisi 430

Abstract The superiority and profile of the weld obtained through Gas Metal Arc Welding (GMAW) are not only depends on the chemical configuration of the flux, but also on the choice of welding parameters. Since variety of process parameters influence the results, a proper empathetic of process performance and identification of suitable welding conditions (i.e. optimum setting of process parameters) are indeed essential to enhance quality. The present work highlights the application and comparison of single-response optimization using Response Surface Methodology (RSM) with Meta Heuristic Optimization techniques namely Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). The experimental analysis is conducted by optimizing the input parameters like Current Rating (Amp), Feed Rate (m/min), Welding Speed (mm/sec) and Gas Flow (l/m). An attempt has been made in the present research work by taking AISI: 430 stainless steel specimens to compare and analyse the performance in terms of weld bead geometry (Bead Width (mm), Bead Height (mm) and Depth of Penetration (mm)), Hardness (VHN) and Tensile Strength (N/mm²) using IRB 1410 Industrial manipulator. The effect of process parameters on ferritic stainless steel of series 400 (AISI: 430) grade has been analysed using Response Surface Methodology (RSM) method. Further, Meta Heuristic Optimization techniques namely Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) have been developed further to minimize the bead width, bead height and maximize the depth of penetration. While fairly similar results were achieved with the implementation of Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) were computationally efficient. Experimental validation of the single-objective as well as multi-objective optimization results indicates that the empirical models for the quality prediction with proposed optimization results are better for the GMAW process by IRB 1410 Industrial manipulator.

scholarly journals A Novel Hybrid Algorithm of Max-Min Ant System with Quadratic Programming to Solve the Unit Commitment Problem

2021 ◽  
Vol 54 (5) ◽  
pp. 699-712
Author(s):  
Henri-Joël Akoue ◽  
Pascal Ntsama Eloundou ◽  
Salomé Ndjakomo Essiane ◽  
Pierre Ele ◽  
Léandre Nneme Nneme ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Quadratic Programming ◽  
Ant Colony Optimization ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Ant Colony ◽  
Ant System ◽  
Unit Commitment Problem ◽  
Swarm Optimization ◽  
The Impact

In this paper, we propose a novel hybrid algorithm based on MAX-MIN Ant System version of ant colony optimization coupled with quadratic programming (MMAS-QP). Quadratic programming is used to optimize the Economic Dispatching process and MMAS for planning the switching schedule of a set of production units. The algorithm is implemented in MATLAB software environment for two systems, one is 4 generating units running for 8 hours, and the other is 10 generating units running for 24 hours. The impact of heuristic parameters on the behavior of the algorithm is highlighted through the parameters setting. Results obtained shows improved solution compared to several methods such as Modified Ant Colony Optimization (MACO), particle Swarm Optimization combined with Lagrange Relaxation (PSO-LR), Swarm and Evolutionary Computation (SEC), Particle Swarm Optimization combined with Genetic Algorithm (PSO-GA). The proposed method improves sufficiently the quality of the solution as well as the execution time.

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