High-accuracy wire electrical discharge machining using artificial neural networks and optimization techniques

2018 ◽  
Vol 49 ◽  
pp. 24-38 ◽  
Author(s):  
A. Conde ◽  
A. Arriandiaga ◽  
J.A. Sanchez ◽  
E. Portillo ◽  
S. Plaza ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
High Accuracy ◽  
Optimization Techniques ◽  
Wire Electrical Discharge Machining ◽  
Artificial Neural

Soft Modeling of Wire Electrical Discharge Machining of WC-Co Composite

2011 ◽  
Vol 335-336 ◽  
pp. 535-540 ◽  
Author(s):  
Veluswamy Muthuraman ◽  
Raju Ramakrishnan
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Artificial Neural Networks ◽  
Material Removal Rate ◽  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Wire Electrical Discharge Machining ◽  
Ann Model

The prediction of optimal machining conditions for required surface roughness and material removal rate (MRR) plays a very significant role in process planning of wire electrical discharge machining (WEDM). Artificial neural networks (ANN) are widely applied to predict the performance characteristics of complex machining process like WEDM very accurately. This present work deals with the features of cutting operation by WEDM of tungsten carbide- cobalt composite(WC – Co) and an artificial neural networks(ANN) model in terms of machining parameters, developed to predict surface roughness(Ra) and material removal rate (MRR).The experiment was planned as per Taguchi’s L 27 orthogonal array. The predictive capacity of the models was validated. The test results indicate that the proposed models could adequately describe the performance indicators with the limits of the factors that are being investigated. Finally the accuracy of the developed ANN model was compared to the experimental values. It was observed that the proposed ANN model is good.

scholarly journals Optimization of a Steam Reforming Plant Modeled with Artificial Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1923
Author(s):  
Eduardo G. Pardo ◽  
Jaime Blanco-Linares ◽  
David Velázquez ◽  
Francisco Serradilla
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steam Reforming ◽  
Real Data ◽  
Memetic Algorithms ◽  
Black Box ◽  
Optimization Techniques ◽  
Ann Model ◽  
The Real ◽  
Artificial Neural

The objective of this research is to improve the hydrogen production and total profit of a real Steam Reforming plant. Given the impossibility of tuning the real factory to optimize its operation, we propose modelling the plant using Artificial Neural Networks (ANNs). Particularly, we combine a set of independent ANNs into a single model. Each ANN uses different sets of inputs depending on the physical processes simulated. The model is then optimized as a black-box system using metaheuristics (Genetic and Memetic Algorithms). We demonstrate that the proposed ANN model presents a high correlation between the real output and the predicted one. Additionally, the performance of the proposed optimization techniques has been validated by the engineers of the plant, who reported a significant increase in the benefit that was obtained after optimization. Furthermore, this approach has been favorably compared with the results that were provided by a general black-box solver. All methods were tested over real data that were provided by the factory.

The significance of relevance trees in the identification of artificial neural networks input vectors

2013 ◽  
Vol 24 (1) ◽  
pp. 27-34
Author(s):  
G. Manuel ◽  
J.H.C. Pretorius
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Methods ◽  
Demand Forecasting ◽  
Forecast Model ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Wide Range ◽  
Accurate Forecast ◽  
Artificial Neural

In the 1980s a renewed interest in artificial neural networks (ANN) has led to a wide range of applications which included demand forecasting. ANN demand forecasting algorithms were found to be preferable over parametric or also referred to as statistical based techniques. For an ANN demand forecasting algorithm, the demand may be stochastic or deterministic, linear or nonlinear. Comparative studies conducted on the two broad streams of demand forecasting methodologies, namely artificial intelligence methods and statistical methods has revealed that AI methods tend to hide the complexities of correlation analysis. In parametric methods, correlation is found by means of sometimes difficult and rigorous mathematics. Most statistical methods extract and correlate various demand elements which are usually broadly classed into weather and non-weather variables. Several models account for noise and random factors and suggest optimization techniques specific to certain model parameters. However, for an ANN algorithm, the identification of input and output vectors is critical. Predicting the future demand is conducted by observing previous demand values and how underlying factors influence the overall demand. Trend analyses are conducted on these influential variables and a medium and long term forecast model is derived. In order to perform an accurate forecast, the changes in the demand have to be defined in terms of how these input vectors correlate to the final demand. The elements of the input vectors have to be identifiable and quantifiable. This paper proposes a method known as relevance trees to identify critical elements of the input vector. The case study is of a rapid railway operator, namely the Gautrain.

scholarly journals Artificial Neural Networks Based Optimization Techniques: A Review

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2689
Author(s):  
Maher G. M. Abdolrasol ◽  
S. M. Suhail Hussain ◽  
Taha Selim Ustun ◽  
Mahidur R. Sarker ◽  
Mahammad A. Hannan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Optimization Techniques ◽  
Initial Population ◽  
Neural Net ◽  
Bee Colony ◽  
Whale Optimization ◽  
Artificial Neural

In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system.

scholarly journals Sign Language Recognition using Hybrid Neural Networks

2019 ◽  
Vol 9 (2) ◽  
pp. 1092-1098
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Sign Language ◽  
Back Propagation ◽  
Optimization Techniques ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Sign Languages ◽  
Artificial Neural

Language has a prime role in communication between persons, in learning, in distribution of concepts and in preserving public contacts. The hearing-impaired have to challenge communication obstacles in a mostly hearing-capable culture. There are hundreds Sign Languages that are used all around the world today .The Sign Languages are established depending on the country and area of the deaf public. The aim of sign language recognition is to offer an effectual and correct tool to transcribe hand gesture into text. It can play a vital role in the communiqué between deaf and hearing society. Sign language recognition (SLR), as one of the significant research fields of human–computer interaction (HCI), has produced more and more interest in HCI society. Since, artificial neural networks are best suited for automated pattern recognition problems; they are used as a classification tool for this research. Back propagation is the most important algorithm for training neural networks. But, it easily gets trapped in local minima leading to inaccurate solutions. Therefore, some global search and optimization techniques were required to hybridize with artificial neural networks. One such technique is Genetic algorithms that imitate the principle of natural evolution. So, in this article, a hybrid intelligent system is proposed for sign language recognition in which artificial neural networks are merged with genetic algorithms. Results show that proposed hybrid model outperformed the existing back propagation based system.

scholarly journals Identification of Tire Model Parameters with Artificial Neural Networks

2020 ◽  
Vol 10 (24) ◽  
pp. 9110 ◽  
Author(s):  
José Luis Olazagoitia ◽  
Jesus Angel Perez ◽  
Francisco Badea
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computing Time ◽  
Optimization Techniques ◽  
Training Dataset ◽  
Model Parameters ◽  
Mathematical Form ◽  
Tire Model ◽  
Data Set ◽  
Artificial Neural

Accurate modeling of tire characteristics is one of the most challenging tasks. Many mathematical models can be used to fit measured data. Identification of the parameters of these models usually relies on least squares optimization techniques. Different researchers have shown that the proper selection of an initial set of parameters is key to obtain a successful fitting. Besides, the mathematical process to identify the right parameters is, in some cases, quite time-consuming and not adequate for fast computing. This paper investigates the possibility of using Artificial Neural Networks (ANN) to reliably identify tire model parameters. In this case, the Pacejka’s “Magic Formula” has been chosen for the identification due to its complex mathematical form which, in principle, could result in a more difficult learning than other formulations. The proposed methodology is based on the creation of a sufficiently large training dataset, without errors, by randomly choosing the MF parameters within a range compatible with reality. The results obtained in this paper suggest that the use of ANN to directly identify parameters in tire models for real test data is possible without the need of complicated cost functions, iterative fitting or initial iteration point definition. The errors in the identification are normally very low for every parameter and the fitting problem time is reduced to a few milliseconds for any new given data set, which makes this methodology very appropriate to be used in applications where the computing time needs to be reduced to a minimum.

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