Tuning of neural networks using particle swarm optimization to model MIG welding process

Malviya, Rakesh Kumar; Pratihar, Dilip Kumar

doi:10.1016/j.swevo.2011.07.001

Cited by 79 publications

(30 citation statements)

References 13 publications

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“…Particle Swarm Optimization (PSO) is a stochastic population-based evolutionary computation technique which is inspired by the social behavior of bird flocking or fish schooling or swarm of insects [14,18]. The system is initialized with a population of random solution and then it searches the optima through updating generation [19].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A Comparative Modeling and Multi- Objective Optimization in Wire EDM Process on H21 Tool Steel Using Intelligent Hybrid Approach

Choudhuri¹,

Sen²,

Ghosh³

et al. 2016

IJET

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H21 steel is one of the hot work tool steel, which exhibits superior red hardness, high mechanical strength and difficult-to-machine. Wire electrical discharge machining (WEDM) always demands high-speed and high-precision machining to fulfill productivity and accuracy of machining hard materials. Cutting speed determines the productivity of machining and the width of kerf determines the tolerance of finished product. Two methodologies viz. response surface method (RSM) and artificial neural network (ANN) are compared for their modeling, sensitivity analysis and optimization abilities. The predictability of ANN model is better than RSM which indicating the advantage of ANN in mapping the nonlinear behavior of the system. Finally, the ANN fitness function is integrated with particle swarm optimization (PSO) algorithm to optimize the process parameters of the WEDM process.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

A Comparative Modeling and Multi- Objective Optimization in Wire EDM Process on H21 Tool Steel Using Intelligent Hybrid Approach

Choudhuri¹,

Sen²,

Ghosh³

et al. 2016

IJET

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“…The neural network classifier is trained using particle swarm optimization algorithm. In [7,21,22] the corresponding authors demonstrate hybrid PSO based neural network training algorithm. A population based search algorithm is used to search out optimized synaptic weights for a multilayer perceptron.…”

Section: Neuro Swarm Optimization Techniquementioning

confidence: 99%

Performance analysis of neuro swarm optimization algorithm applied on detecting proportion of components in manhole gas mixture

Ojha

Dutta

2012

AIR

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The article presents performance analysis of the neuro swarm optimization algorithm applied for the detection of proportion of the component gases found in manhole gas mixture. The hybrid neuro swarm optimization technique is used for implementing an intelligent sensory system for the detection of component gases present in manhole gas mixture. The manhole gas mixture typically contains toxic gases such as Hydrogen Sulfide, Ammonia, Methane, Carbon Dioxide, Nitrogen Oxide, and Carbon Monoxide. A semiconductor based gas sensor array used for sensing the gas components consists of many sensor elements, where each sensor element is responsible for sensing particular gas component. Presence of multiple gas sensors for detecting multiple gases results in cross-sensitivity. The central theme of this article is the performance analysis of the algorithm which offers solution to multiple gas detection issue. The article also presents study on the computational cost incurred by the algorithm.

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“…For example, to automate a manufacturing process, it is necessary to know the input-output relationship in both directions, and using a radial basis network it is possible to predict the results of a manufacturing process efficiently [3]. The RBF networks are important in prediction by his character of universal approximators [4] and for its good performance in the non-linearity common in processes [5]. In this paper, we propose a comparative study between a multiple regression model and a Radial Basis Function Neural Network in terms of the statistical metrics …”

Section: Introductionmentioning

confidence: 99%

Radial Basis Function Network versus Regression Model in Manufacturing Processes Prediction

Jesus¹

2018

OABB

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Tuning of neural networks using particle swarm optimization to model MIG welding process

Cited by 79 publications

References 13 publications

A Comparative Modeling and Multi- Objective Optimization in Wire EDM Process on H21 Tool Steel Using Intelligent Hybrid Approach

A Comparative Modeling and Multi- Objective Optimization in Wire EDM Process on H21 Tool Steel Using Intelligent Hybrid Approach

Performance analysis of neuro swarm optimization algorithm applied on detecting proportion of components in manhole gas mixture

Radial Basis Function Network versus Regression Model in Manufacturing Processes Prediction

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