Neuro-fuzzy MIMO nonlinear control for ceramic roller kiln

Dinh, Nguyen Quoc; Afzulpurkar, Nitin

doi:10.1016/j.simpat.2007.08.005

Cited by 35 publications

(18 citation statements)

References 10 publications

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“…Four data sets were selected randomly as the testing data and the remaining twelve data sets were used for training. A developed CANFIS model used the gaussian membership function (MF) with two MFs per input, momentum (MOM) learning rule during training process and TSK fuzzy model proposed by Takagi, Sugeno and Kang [19] for fuzzy part in these hybrid systems. TSK fuzzy model has been selected instead of the Mamdani model [20] for its simplicity and its compactness.…”

Section: Co-active Neuro-fuzzy Inference System (Canfis)mentioning

confidence: 99%

Prediction and analysis of radial overcut in holes drilled by electrochemical machining process

Tajdari

Chavoshi

2013

Open Engineering

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Abstract:Radial overcut predictive models using multiple regression analysis, artificial neural network and co-active neurofuzzy inference system are developed to predict the radial overcut during electrochemical drilling with vacuum extraction of electrolyte. Four process parameters, electrolyte concentration, voltage, initial machining gap and tool feed rate, are selected to develop the models. The comparison between the results of the presented models shows that the artificial neural network and co-active neuro-fuzzy inference system models can predict the radial overcut with an average relative error of nearly 5%. Main effect and interaction plots are generated to study the effects of process parameters on the radial overcut. The analysis shows that the voltage, electrolyte concentration and tool feed rate have significant effect on radial overcut, respectively, while initial machining gap has a little effect. It is also found that the increase of the voltage and electrolyte concentration increases the radial overcut and the increase of the tool feed rate decreases the radial overcut. Keywords

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Section: Co-active Neuro-fuzzy Inference System (Canfis)mentioning

confidence: 99%

Prediction and analysis of radial overcut in holes drilled by electrochemical machining process

Tajdari

Chavoshi

2013

Open Engineering

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“…Among them a total of 300 data sets were selected for the purpose of training in ANFIS and the rest 50 data sets were selected for testing purposes after the training was completed in order to verify the accuracy of the predicted values [10].…”

Section: Anfis Modelingmentioning

confidence: 99%

Temperature Field Analysis and Process Control Strategies for MgO Single Crystal Production Using Adaptive Neuro-Fuzzy Inference System

Li¹

2011

TOMSJ

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Abstract:To grow high-purity and large sizes MgO single crystals with twin-electrode DC submerged arc furnace requires that the temperature distribution be well understood and the processing temperature be precisely controlled. For the complexity of the production of MgO single crystals and the difficulty to measure the temperature inside the furnace, the temperature distribution was studied by using finite element method (FEM), and the temperature control was realized by the process control strategies with adaptive neuro-fuzzy inference system (ANFIS). Experiments were carried out to verify the effectiveness of the method. The result of experiments indicated that using the adaptive neuro-fuzzy control system can improve the quality and the quantity of the MgO single crystal production.

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“…It means that fuzzy rules are constructed with shared membership values to express possible correlations between outputs in this diagram. Besides, we can also form a multiple ANFIS (MANFIS) by placing many ANFIS models side by side, in which, each ANFIS has an independent set of fuzzy rules [26].…”

Section: Coactive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

“…CANFIS has been mostly used in areas like earth and weather sciences [25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Tool flank wear prediction in CNC turning of 7075 AL alloy SiC composite

Chavoshi

2010

Prod. Eng. Res. Devel.

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Flank wear occurs on the relief face of the tool and the life of a tool used in a machining process depends upon the amount of flank wear; so predicting of flank wear is an important requirement for higher productivity and product quality. In the present work, the effects of feed, depth of cut and cutting speed on flank wear of tungsten carbide and polycrystalline diamond (PCD) inserts in CNC turning of 7075 AL alloy with 10 wt% SiC composite are studied; also artificial neural network (ANN) and co-active neuro fuzzy inference system (CANFIS) are used to predict the flank wear of tungsten carbide and PCD inserts. The feed, depth of cut and cutting speed are selected as the input variables and artificial neural network and co-active neuro fuzzy inference system model are designed with two output variables. The comparison between the results of the presented models shows that the artificial neural network with the average relative prediction error of 1.03% for flank wear values of tungsten carbide inserts and 1.7% for flank wear values of PCD inserts is more accurate and can be utilized effectively for the prediction of flank wear in CNC turning of 7075 AL alloy SiC composite. It is also found that the tungsten carbide insert flank wear can be predicted with less error than PCD flank wear insert using ANN. With Regard to the effect of the cutting parameters on the flank wear, it is found that the increase of the feed, depth of cut and cutting speed increases the flank wear. Also the feed and depth of cut are the most effective parameters on the flank wear and the cutting speed has lesser effect.

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Neuro-fuzzy MIMO nonlinear control for ceramic roller kiln

Cited by 35 publications

References 10 publications

Prediction and analysis of radial overcut in holes drilled by electrochemical machining process

Prediction and analysis of radial overcut in holes drilled by electrochemical machining process

Temperature Field Analysis and Process Control Strategies for MgO Single Crystal Production Using Adaptive Neuro-Fuzzy Inference System

Tool flank wear prediction in CNC turning of 7075 AL alloy SiC composite

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