scholarly journals Multiresponse Particle Swarm Optimization of Wire-Electro-Discharge Machining Parameters of Nitinol Alloys

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mohammed Yunus ◽  
Mohammad S. Alsoufi
Keyword(s):  
Particle Swarm Optimization ◽  
Metal Removal ◽  
Removal Rate ◽  
Particle Swarm ◽  
Ideal Condition ◽  
Machining Parameters ◽  
Swarm Optimization ◽  
Electro Discharge Machining ◽  
Wide Range ◽  
Wedm Process

The conventional process of machining of nitinol alloy which possesses excess strain hardening and low thermal conductivity makes a complex process that leads to extensive wear on the tool and inadequate surface quality. Wire-electro-discharge machining (WEDM) is widely accepted for machining this alloy involving various input factors, namely, P (pulse-on-duration), Q (pulse-off-duration), C, (maximum-current), and V (voltage). Using the PSO (particle swarm optimization) method, the effect of WEDM process factors on multiresponses such as MRR (metal removal rate) and SR (surface roughness) has been investigated. ANOVA was used to create a relationship model between input factors and response characteristics, which was then optimized using response surface methods (RSM). ANOVA revealed that variables A and C are the most significant. When investigated individually, the influence of WEDM process parameters on SR and MRR is contradictory, as no response provides the best process quality. To find the optimal ideal condition for decreasing SR and maximizing MRR, the MOOPSO approach was used. P = 25.47051 μs, Q = 10.84998 μs, C = 2.026317 A, and V = 49.50757 volts were used to calculate the optimal universal solution for machining characteristics (MRRmax = 3.536791 mm3/min and SRmin = 1.822372 μm). PSO enhanced MRR and SR for every optimal combination of variables, according to the findings. Based on the findings, a wide range of optimal settings for achieving maximum MRR and minimum SR are given, depending on the product requirements.

scholarly journals Multi response Particle Swarm Optimization of Wire Electro Discharge Machining parameters of Nitinol alloys

2020 ◽  
Author(s):  
Mohammed Yunus ◽  
Mohammad S. Alsoufi
Keyword(s):  
Particle Swarm Optimization ◽  
Metal Removal ◽  
Removal Rate ◽  
Particle Swarm ◽  
Optimization Method ◽  
Machining Parameters ◽  
Swarm Optimization ◽  
Electro Discharge Machining ◽  
Process Factors ◽  
Wedm Process

Abstract The conventional process of Machining of Nitinol alloy leads to extensive wear on the tool and deprived surface quality. Wire electro discharge machining (WEDM) is widely accepted for machining this alloy involving various input factors, namely, P, (pulse-on-duration), Q, (pulse-off-duration), C, (maximum-current), and V, (voltage). The factor’s effect on MRR (metal removal rate) and SR (surface roughness) responses and multi-response optimization of the WEDM process by employing PSO (particle swarm optimization) method are studied. The relationship model between factors and response characteristics were generated by ANOVA and optimized by response surface methodology has shown more significant factors (A and C). Though the effect of WEDM process factors on SR and MRR are contradictory when studied individually. MRPSO method was employed to get the best optimum condition for minimizing SR and maximizing MRR. MRPSO results improved the responses for vast combination of optimal setting of factors to meet the product requirements.

scholarly journals Multi response Particle Swarm Optimization of Wire Electro Discharge Machining parameters of Nitinol alloys

2020 ◽  
Author(s):  
Mohammed Yunus ◽  
Mohammad S. Alsoufi
Keyword(s):  
Particle Swarm Optimization ◽  
Metal Removal ◽  
Removal Rate ◽  
Particle Swarm ◽  
Optimization Method ◽  
Machining Parameters ◽  
Swarm Optimization ◽  
Electro Discharge Machining ◽  
Process Factors ◽  
Wedm Process

Abstract The conventional process of Machining of Nitinol alloy leads to extensive wear on the tool and deprived surface quality. Wire electro discharge machining (WEDM) is widely accepted for machining this alloy involving various input factors, namely, P, (pulse-on-duration), Q, (pulse-off-duration), C, (maximum-current), and V, (voltage). The factor’s effect on MRR (metal removal rate) and SR (surface roughness) responses and multi-response optimization of the WEDM process by employing PSO (particle swarm optimization) method are studied. The relationship model between factors and response characteristics were generated by ANOVA and optimized by response surface methodology has shown more significant factors (A and C). Though the effect of WEDM process factors on SR and MRR are contradictory when studied individually. MRPSO method was employed to get the best optimum condition for minimizing SR and maximizing MRR. MRPSO results improved the responses for vast combination of optimal setting of factors to meet the product requirements.

Performance Analysis on Eco-friendly Machining of Ti6Al4V using Powder Mixed with Different Dielectrics in WEDM

2020 ◽  
Vol 17 (3) ◽  
pp. 8128-8139
Author(s):  
S. Chakraborty ◽  
S. Mitra ◽  
D. Bose
Keyword(s):  
Metal Removal ◽  
Removal Rate ◽  
Machining Process ◽  
Process Efficiency ◽  
Machining Parameters ◽  
Dielectric Fluids ◽  
High Emission ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Wedm Process

The recent scenario of modern manufacturing is tremendously improved in the sense of precision machining and abstaining from environmental pollution and hazard issues. In the present work, Ti6Al4V is machined through wire EDM (WEDM) process with powder mixed dielectric and analyzed the influence of input parameters and inherent hazard issues. WEDM has different parameters such as peak current, pulse on time, pulse off time, gap voltage, wire speed, wire tension and so on, as well as dielectrics with powder mixed. These are playing an essential role in WEDM performances to improve the process efficiency by developing the surface texture, microhardness, and metal removal rate. Even though the parameter’s influencing, the study of environmental effect in the WEDM process is very essential during the machining process due to the high emission of toxic vapour by the high discharge energy. In the present study, three different dielectric fluids were used, including deionised water, kerosene, and surfactant added deionised water and analysed the data by taking one factor at a time (OFAT) approach. From this study, it is established that dielectric types and powder significantly improve performances with proper set of machining parameters and find out the risk factor associated with the PMWEDM process.

scholarly journals A Modified NM-PSO Method for Parameter Estimation Problems of Models

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
An Liu ◽  
Erwie Zahara ◽  
Ming-Ta Yang
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Wide Range ◽  
Estimation Problems

Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional particle swarm optimization (PSO) method, and the conventional NM-PSO method.

scholarly journals Grid and Force Based Sensor Deployment Methods in Wireless Sensor Network using Particle Swarm Optimization

2018 ◽  
Vol 10 (3) ◽  
pp. 1287
Author(s):  
Aparna Pradeep Laturkar ◽  
Sridharan Bhavani ◽  
DeepaliParag Adhyapak
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Sensor Deployment ◽  
Packet Size ◽  
Swarm Optimization ◽  
Wide Range ◽  
Battery Capacity

Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling & data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. PSO is a multidimensional optimization method inspired from the social behavior of birds called flocking. Basic version of PSO has the drawback of sometimes getting trapped in local optima as particles learn from each other and past solutions. This issue is solved by discrete version of PSO known as Modified Discrete Binary PSO (MDBPSO) as it uses probabilistic approach. This paper discusses performance analysis of random; grid based MDBPSO (Modified Discrete Binary Particle Swarm Optimization), Force Based VFCPSO and Combination of Grid & Force Based sensor deployment algorithms based on interval and packet size. From the results of Combination of Grid & Force Based sensor deployment algorithm, it can be concluded that its performance is best for all parameters as compared to rest of the three methods when interval and packet size is varied.

scholarly journals Random, PSO and MDBPSO based Sensor Deployment in Wireless Sensor Network

2018 ◽  
Vol 10 (3) ◽  
pp. 1278 ◽  
Author(s):  
Aparna Pradeep Laturkar ◽  
Sridharan Bhavani ◽  
DeepaliParag Adhyapak
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Sensor Deployment ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Wide Range ◽  
Battery Capacity

Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling and data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. This paper discusses sensor deployment using Random; Particle Swarm Optimization (PSO) and grid based MDBPSO (Modified Discrete Binary Particle Swarm Optimization) methods. This paper analyzes the performance of Random, PSO based and MDBPSO based sensor deployment methods by varying different grid sizes and the region of interest (ROI). PSO and MDBPSO based sensor deployment methods are analyzed based on number of iterations. From the simulation results; it can be concluded that MDBPSO performs better than other two methods.

scholarly journals An Enhanced Partial Search to Particle Swarm Optimization for Unconstrained Optimization

Mathematics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 357 ◽  
Author(s):  
Shu-Kai S. Fan ◽  
Chih-Hung Jen
Keyword(s):  
Particle Swarm Optimization ◽  
Unconstrained Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Searching Strategy ◽  
Wide Range

Particle swarm optimization (PSO) is a population-based optimization technique that has been applied extensively to a wide range of engineering problems. This paper proposes a variation of the original PSO algorithm for unconstrained optimization, dubbed the enhanced partial search particle swarm optimizer (EPS-PSO), using the idea of cooperative multiple swarms in an attempt to improve the convergence and efficiency of the original PSO algorithm. The cooperative searching strategy is particularly devised to prevent the particles from being trapped into the local optimal solutions and tries to locate the global optimal solution efficiently. The effectiveness of the proposed algorithm is verified through the simulation study where the EPS-PSO algorithm is compared to a variety of exiting “cooperative” PSO algorithms in terms of noted benchmark functions.

scholarly journals Synthesis of a Sparse 2D-Scanning Array using Particle Swarm Optimization for Side-Lobe Reduction

2021 ◽  
Vol 20 ◽  
pp. 112-116
Author(s):  
Sivaranjan Goswami ◽  
Kandarpa Kumar Sarma ◽  
Kumaresh Sarmah
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Side Lobe ◽  
Ann Model ◽  
Phased Array Antenna ◽  
Binary Matrix ◽  
Rectangular Array ◽  
Swarm Optimization ◽  
Wide Range ◽  
Artificial Neural Network Ann

Synthesis of sparse arrays is a promising area of research for a wide range of applications including radar and millimeter-wave wireless communication. The design goal of array thinning problems is to reduce the number of elements of an array without significantly affecting its performance. This work presents a technique for synthesizing a sparse phased-array antenna from a 16×16 uniform rectangular array (URA). The proposed approach reduces the number of elements by 50% without any significant increase in the peak sidelobe level (PSLL) for all possible scan angles in the azimuthal and elevation plans within a finite range of scan angles. The synthesis includes an artificial neural network (ANN) model for estimation of the excitation weights of the URA for a given scan-angle. The weights of the sparse array are computed by the Hadamard product of the weight matrix of the URA with a binary matrix that is obtained using particle swarm optimization (PSO) to minimize the PSLL.

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