scholarly journals PSO Research on Cutting Parameters in AWJM Process for Aluminum 6061 Alloy

2019 ◽  
Vol 8 (2S3) ◽  
pp. 64-71
Keyword(s):  
Particle Swarm Optimization ◽  
Soft Computing ◽  
Process Parameters ◽  
Particle Swarm ◽  
Machining Process ◽  
Orifice Diameter ◽  
Machining Performance ◽  
Swarm Optimization ◽  
Computing Technique ◽  
Soft Computing Technique

In recent years there is a rapid growth in the improvement of complexity, difficult and harder to machine metals and alloys. AWJM is one of the hybrid, nontraditional machining process in machining several hard-to-cut materials these days. Machining parameters play the lead role in determining the machine economics and quality of machining. In this study Particle Swarm Optimization soft computing technique is executed to estimate the optimal process parameters which leads to a least value of machining performance and compared with the machining performance value of experimental data. The approaches suggested in this study involve three components, viz., experimental observation, multi regression modeling and single objective Particle Swarm Optimization. The consequence of Pressure, Abrasive flow rate, Orifice diameter, Focusing nozzle diameter and Stand off distance AWJM process parameters on MRR and SR of Aluminium 6061 alloy which is machined by AWJM was experimentally performed and analyzed. According to Response Surface Methodology design, different experiments were conducted with the combination of input parameters on this alloy. The outcome of this study revealed that the PSO soft computing technique obtains the optimal solution of AWJM process parameters for Aluminium 6061 alloy.

Utilization of Soft Computing Techniques in Sputtering Processes: A Review

2013 ◽  
Vol 832 ◽  
pp. 260-265
Author(s):  
Norlina M. Sabri ◽  
Mazidah Puteh ◽  
Mohamad Rusop Mahmood
Keyword(s):  
Soft Computing ◽  
Process Parameters ◽  
Optimization Techniques ◽  
Future Research ◽  
Film Properties ◽  
Reliable Technique ◽  
Computing Technique ◽  
Optimization Of Process Parameters ◽  
Soft Computing Techniques ◽  
Soft Computing Technique

This paper presents an overview of research works on the utilizing of soft computing in the optimization of process parameters and in the prediction of thin film properties in sputtering processes. The papers from this review were obtained from relevant databases and from various scientific journals. The papers collected were published from 2008 to 2012. The focus of the review is to provide an outlook on the utilization of soft computing techniques in sputtering processes. Based on the review, the soft computing techniques which have been applied so far are ANN, GA and Fuzzy Logic. The first finding of this review is that soft computing technique is a promising and more reliable approach to optimize and predict process parameters compared to the traditional methods. The second finding is that the utilizing of soft computing techniques in sputtering processes are still limited and still in exploratory phase as they have not yet been extensively and stably applied. The techniques applied are also limited to ANN, GA and Fuzzy, whereas the exploration into other techniques is also necessary to be conducted in order to seek the most reliable technique and so as to expand the application of soft computing approach. Future research could focus on the exploration of other soft computing techniques for optimization in order to find the best optimization techniques based on the specific processes.

scholarly journals Performance comparisons of particle swarm optimization, echo state neural network and genetic algorithm for vegetation segmentation

2017 ◽  
Vol 7 (1.1) ◽  
pp. 184
Author(s):  
Rincy Merlin Mathew ◽  
S. Purushothaman ◽  
P. Rajeswari
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Soft Computing ◽  
Particle Swarm ◽  
Band 3 ◽  
Landsat 8 ◽  
Swarm Optimization ◽  
Landsat 7 ◽  
Performance Comparisons

This article presents the implementation of vegetation segmentation by using soft computing methods: particle swarm optimization (PSO), echostate neural network(ESNN) and genetic algorithm (GA). Multispectral image with the required band from Landsat 8 (5, 4, 3) and Landsat 7 (4, 3, 2) are used. In this paper, images from ERDAS format acquired by Landsat 7 ‘Paris.lan’ (band 4, band 3, Band 2) and image acquired from Landsat 8 (band5, band 4, band 3) are used. The soft computing algorithms are used to segment the plane-1(Near infra-red spectra) and plane 2(RED spectra). The monochrome of the two segmented images is compared to present performance comparisons of the implemented algorithms.

Cutting Parameters Optimization by Using Particle Swarm Optimization (PSO)

2007 ◽  
Vol 10-12 ◽  
pp. 879-883 ◽  
Author(s):  
Jian Guang Li ◽  
Ying Xue Yao ◽  
Dong Gao ◽  
Chang Qing Liu ◽  
Zhe Jun Yuan
Keyword(s):  
Particle Swarm Optimization ◽  
Essential Role ◽  
Particle Swarm ◽  
Cutting Parameters ◽  
Fundamental Principle ◽  
Parameters Optimization ◽  
Machining Process ◽  
Swarm Optimization ◽  
Cutting Parameters Optimization ◽  
Cutting Experiments

Cutting parameters play an essential role in the economics of machining. In this paper, particle swarm optimization (PSO), a novel optimization algorithm for cutting parameters optimization (CPO), was discussed comprehensively. First, the fundamental principle of PSO was introduced; then, the algorithm for PSO application in cutting parameters optimization was developed; thirdly, cutting experiments without and with optimized cutting parameters were conducted to demonstrate the effectiveness of optimization, respectively. The results show that the machining process was improved obviously.

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