scholarly journals Efficient characterization of microwave applicator loaded with multilayer dielectric based on neural networks

2010 ◽  
Vol 23 (2) ◽  
pp. 217-226
Author(s):  
Zoran Stankovic ◽  
Bratislav Milovanovic ◽  
Nebojsa Doncov ◽  
Aleksandar Marincic
Keyword(s):  
Neural Network ◽  
Neural Model ◽  
Hardware Platform ◽  
Neural Network Approach ◽  
Frequency Model ◽  
Suggested Approach ◽  
Cavity Configuration ◽  
Microwave Applicator ◽  
Multilayer Dielectric

In this paper, new neural network approach for characterization of cylindrical metallic cavity loaded with multilayer dielectric is suggested. Such cavity configuration has a great importance in realization of microwave resonant applicators, widely used for thermal processing of multilayer materials. Proposed neural model is based on representation of multilayer cavity load via several planparallel homogeneous dielectric slabs and superposition of separate influence of each dielectric slab parameters on the cavity resonant frequency. Model is incorporated into MLP neural network enabling its efficient implementation on modest hardware platform. Suggested approach is verified on the example of circular cylindrical metallic cavity loaded with two-layer dielectric.

scholarly journals Characterization of Superplastic Deformation Behavior for a Novel Al-Mg-Fe-Ni-Zr-Sc Alloy: Arrhenius-Based Modeling and Artificial Neural Network Approach

2021 ◽  
Vol 11 (5) ◽  
pp. 2208
Author(s):  
Ahmed O. Mosleh ◽  
Anton D. Kotov ◽  
Anna A. Kishchik ◽  
Oleg V. Rofman ◽  
Anastasia V. Mikhaylovskaya
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Superplastic Forming ◽  
Equation Model ◽  
The Novel ◽  
Neural Network Approach ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The application of superplastic forming for complex components manufacturing is attractive for automotive and aircraft industries and has been of great interest in recent years. The current analytical modeling theories are far from perfect in this area, and the results deduced from it characterize the forming conditions insufficiently well; therefore, successful numerical modeling is essential. In this study, the superplastic behavior of the novel Al-Mg-Fe-Ni-Zr-Sc alloy with high-strain-rate superplasticity was modeled. An Arrhenius-type constitutive hyperbolic-sine equation model (ACE) and an artificial neural network (ANN) were developed. A comparative study between the constructed models was performed based on statistical errors. A cross validation approach was utilized to evaluate the predictability of the developed models. The results revealed that the ACE and ANN models demonstrated strong workability in predicting the investigated alloy’s flow stress, whereas the ACE approach exhibited better predictability than the ANN.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

Assessment of Self-Heating Susceptibility of Indian Coal Seams – A Neural Network Approach

2014 ◽  
Vol 59 (4) ◽  
pp. 1061-1076 ◽  
Author(s):  
D.C. Panigrahi ◽  
S.K. Ray
Keyword(s):  
Neural Network ◽  
Spontaneous Combustion ◽  
Potential Method ◽  
Oxidation Potential ◽  
Wet Oxidation ◽  
Experimental Conditions ◽  
Neural Network Approach ◽  
Indian Coal ◽  
Petrographic Analyses ◽  
Artificial Neural Network Ann

Abstract The paper addresses an electro-chemical method called wet oxidation potential technique for determining the susceptibility of coal to spontaneous combustion. Altogether 78 coal samples collected from thirteen different mining companies spreading over most of the Indian Coalfields have been used for this experimental investigation and 936 experiments have been carried out by varying different experimental conditions to standardize this method for wider application. Thus for a particular sample 12 experiments of wet oxidation potential method were carried out. The results of wet oxidation potential (WOP) method have been correlated with the intrinsic properties of coal by carrying out proximate, ultimate and petrographic analyses of the coal samples. Correlation studies have been carried out with Design Expert 7.0.0 software. Further, artificial neural network (ANN) analysis was performed to ensure best combination of experimental conditions to be used for obtaining optimum results in this method. All the above mentioned analysis clearly spelt out that the experimental conditions should be 0.2 N KMnO4 solution with 1 N KOH at 45°C to achieve optimum results for finding out the susceptibility of coal to spontaneous combustion. The results have been validated with Crossing Point Temperature (CPT) data which is widely used in Indian mining scenario.

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