scholarly journals Research Concerning the Springback Prediction in the Bending Operations

2013 ◽  
Vol 8-9 ◽  
pp. 490-499 ◽  
Author(s):  
Florica Mioara Serban ◽  
Nicolae Bâlc ◽  
Gheorghe Achimas ◽  
Cristea Ciprian
Keyword(s):  
Finite Element ◽  
Fuzzy Inference ◽  
Training Data ◽  
The Finite Element Method ◽  
Cylindrical Bending ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Bending Process ◽  
Time Required ◽  
Springback Prediction

Nowadays firms are required to obtain high quality products in order to increase their competitiveness. The time required to obtain a new product is also essential to fight the concurrence. For manufacturers of bent parts, accurate prediction of the springback is very important. Therefore, this paper investigates the applicability of artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) to predict the springback in the free cylindrical bending process of metallic sheets. The finite element method (FEM) was used to simulate the springback in the free cylindrical bending process and the results were used as training data for ANN and ANFIS. The finite element results were validated by comparison with experimental data. Statistic criteria were used to evaluate the performance of the developed ANN and ANFIS models. It was found that the predictions are in good agreement with the FEM data.

Dynamic Behaviour and Crack Detection of a Multi Cracked Rotating Shaft using Adaptive Neuro-Fuzzy-Inference System

2016 ◽  
Vol 6 (4) ◽  
pp. 1-10 ◽  
Author(s):  
Rajeev Ranjan
Keyword(s):  
Finite Element ◽  
Fuzzy Inference System ◽  
Crack Detection ◽  
Fuzzy Inference ◽  
Crack Depth ◽  
Multiple Cracks ◽  
Rotating Shaft ◽  
Element Analysis ◽  
Inference System ◽  
Neuro Fuzzy

The presence of crack changes the physical characteristics of a structure which in turn alter its dynamic response characteristics. So it is important to understand dynamics of cracked structures. Crack depth and location are the main parameters influencing the vibration characteristics of the rotating shaft. In the present study, a technique based on the measurement of change of natural frequencies has been employed to detect the multiple cracks in rotating shaft. The model of shaft was generated using Finite Element Method. In Finite Element Analysis, the natural frequency of the shaft was calculated by modal analysis using the software ANSYS. The Numerical data were obtained from FEA, then used to train through Adaptive Neuro-Fuzzy-Inference System. Then simulations were carried out to test the performance and accuracy of the trained networks. The simulation results show that the proposed ANFIS estimate the locations and depth of cracks precisely.

An Adaptive-Network-Based Fuzzy Inference System for Predicting Springback of U-Bending

2013 ◽  
Vol 284-287 ◽  
pp. 25-30 ◽  
Author(s):  
Bor Tsuen Lin ◽  
Kun Min Huang ◽  
Chun Chih Kuo
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Training Data ◽  
Adaptive Network ◽  
Anfis Model ◽  
Inference System ◽  
Bending Process ◽  
Punch Radius ◽  
Triangular Membership Function ◽  
Springback Angle

Springback will occur when the external force is removed after bending process in sheet metal forming. This paper proposed an adaptive-network-based fuzzy inference system (ANFIS) model for prediction the springback angle of the SPCC material after U-bending. Three parameters were selected as the main factors of affecting the springback after bending, including the die clearance, the punch radius, and the die radius. The training data were obtained from results of U-bending experiment. The training data with four different membership functions – triangular, trapezoidal, bell, and Gaussian functions –were employed in the ANFIS to construct a predictive model for the springback of the U-bending. After the comparison of the predicted value with the checking data, we found that the triangular membership function has the best accuracy, which make it the best function to predict the springback angle of sheet metals after U-bending.

scholarly journals Adaptive Neuro-Fuzzy Inference System Controller for Vibration Control of Reduced-Order Finite Element Model of Rotor-Bearing-Support System

2015 ◽  
Vol 55 ◽  
pp. 1-11
Author(s):  
Abdur Rosyid ◽  
Mohanad Alata ◽  
Mohamed El Madany
Keyword(s):  
Support System ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Controller Design ◽  
Element Model ◽  
Training Data ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Rotor Bearing ◽  
Lqr Controller

This paper evaluates the use of adaptive neuro-fuzzy inference system (ANFIS) controller to suppress the vibration in a rotor-bearing-support system, and compare the performance to LQR controller. ANFIS combines the smooth interpolation of fuzzy inference system (FIS) and the learning capability of adaptive neural network. The ANFIS controller design starts with initialization which includes loading the training data and generating the initial FIS. In this case, the gain values obtained from the LQR controller design previously conducted were used as training data for the ANFIS controller. After the training data is provided, the ANFIS controller learns through a certain optimization algorithm to adjust the parameters. In the current work, hybrid algorithm was used due to its faster convergence. To evaluate the performance, the ANFIS output was compared to the training data. From the evaluation, it can be concluded that ANFIS controller can replace LQR controller with no need to solve the LQR’s Riccati equation. However, in the initialization process, it needs training data obtained from LQR control design. Furthermore, ANFIS controller can replace more than one LQR controllers with different weighting matrices Q and/or R. In a more general tone, ANFIS controller can serve as an effective controller, given any arbitrary speed-gain pairs as its training data. Finally, ANFIS controller can serve as a better controller than LQR as long as tuning can be conducted adequately for that purpose.

Dynamic Behaviour and Crack Detection of a Multi Cracked Rotating Shaft using Adaptive Neuro-Fuzzy-Inference System

Fuzzy Systems ◽  
2017 ◽  
pp. 1540-1551
Author(s):  
Rajeev Ranjan
Keyword(s):  
Finite Element ◽  
Fuzzy Inference System ◽  
Crack Detection ◽  
Fuzzy Inference ◽  
Crack Depth ◽  
Rotating Shaft ◽  
Inference System ◽  
Response Characteristics ◽  
Neuro Fuzzy ◽  
Cracked Structures

The presence of crack changes the physical characteristics of a structure which in turn alter its dynamic response characteristics. So it is important to understand dynamics of cracked structures. Crack depth and location are the main parameters influencing the vibration characteristics of the rotating shaft. In the present study, a technique based on the measurement of change of natural frequencies has been employed to detect the multiple cracks in rotating shaft. The model of shaft was generated using Finite Element Method. In Finite Element Analysis, the natural frequency of the shaft was calculated by modal analysis using the software ANSYS. The Numerical data were obtained from FEA, then used to train through Adaptive Neuro-Fuzzy-Inference System. Then simulations were carried out to test the performance and accuracy of the trained networks. The simulation results show that the proposed ANFIS estimate the locations and depth of cracks precisely.

scholarly journals Damage Detection of Irregular Plates and Regular Dams by Wavelet Transform Combined Adoptive Neuro Fuzzy Inference System

2018 ◽  
Vol 4 (2) ◽  
pp. 305 ◽  
Author(s):  
D. Hamidian ◽  
J. Salajegheh ◽  
E. Salajegheh
Keyword(s):  
Finite Element ◽  
Wavelet Transform ◽  
Damage Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Damage Zone ◽  
Detection Methods ◽  
Mode Shapes ◽  
Inference System ◽  
Neuro Fuzzy

This paper presents a technique for irregular plate and regular dam damage detection based on combination of wavelet with adoptive neuro fuzzy inference system (ANFIS). Many damage detection methods need response of structures (such as the displacements, stresses or mode shapes) before and after damage, but this method only requires response of structures after damage, otherwise many damage detection methods study regular plate but this method also studies irregular plate. First, the structure (irregular plate or regular dam) is modelled by using ANSYS software, the model is analysed and structure’s responses with damage are obtained by finite element approach. Second, the responses at the finite element points with regular distances are obtained by using ANFIS. The damage zone is represented as the elements with reduced elasticity modules. Then these responses of structures are analysed with 2D wavelet transform. It is shown that matrix detail coefficients of 2D wavelet transform can specified the damage zone of plates and regular dams by perturbation in the damaged area.

Comparative Temperature Field Analysis of MgO Single Crystal Furnace Using Finite Element and Fuzzy Methods

2011 ◽  
Vol 675-677 ◽  
pp. 1019-1022
Author(s):  
Tie Li ◽  
Zhen Wang ◽  
Ning Hui Wang
Keyword(s):  
Finite Element ◽  
Temperature Field ◽  
Single Crystal ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Element Model ◽  
Field Analysis ◽  
Inference System ◽  
Field Distributions ◽  
Neuro Fuzzy

This paper presents a method for calculating the temperature field distributions for MgO single crystal furnace. Finite element method (FEM) had been used to carry out the temperature field distributions of MgO single crystal furnace in terms of its energy consumption. Then fuzzy model of the furnace was developed using adaptive neuro-fuzzy inference system (ANFIS), for carrying out its temperature field analysis. Performance was evaluated by comparing finite element model with fuzzy model and good correlation was achieved between them.

Nonlinear System Identification of Smart Buildings

2017 ◽  
pp. 328-347
Author(s):  
Soroush Mohammadzadeh ◽  
Yeesock Kim
Keyword(s):  
System Identification ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Nonlinear Behavior ◽  
Computation Time ◽  
Principal Component ◽  
Training Data ◽  
Inference System ◽  
Smart Buildings ◽  
Neuro Fuzzy

In this book chapter, a system identification method for modeling nonlinear behavior of smart buildings is discussed that has a significantly low computation time. To reduce the size of the training data used for the adaptive neuro-fuzzy inference system (ANFIS), principal component analysis (PCA) is used, i.e., PCA-based adaptive neuro-fuzzy inference system: PANFIS. The PANFIS model is evaluated on a seismically excited three-story building equipped with a magnetorheological (MR) damper. The PANFIS model is trained using an artificial earthquake that contains a variety of characteristics of earthquakes. The trained PANFIS model is tested using four different earthquakes. It was demonstrated that the proposed PANFIS model is effective in modeling nonlinear behavior of a smart building with significant reduction in computational loads.

scholarly journals Diagnosing Heart Disease using Wavelet Transformation and Adaptive Neuro Fuzzy Inference System (ANFIS) Based on Electrocardiagram (ECG)

2019 ◽  
Vol 19 (1) ◽  
pp. 71-82
Author(s):  
Indah Puspita ◽  
Agus Maman Abadi
Keyword(s):  
Heart Disease ◽  
Wavelet Transformation ◽  
Fuzzy Inference ◽  
Training Data ◽  
Anfis Model ◽  
Bundle Branch Block ◽  
Inference System ◽  
Specificity And Sensitivity ◽  
Neuro Fuzzy ◽  
Testing Data

Heart disease is the leading cause of death in the world. Heart disease is called the silent killer, because it often occcurs suddenly. Therefore, periodic cardiac examination is very necessary to reduce cases of death from heart disease.Heart disease can be known through electrocardiogram (ECG) examination. This study aims to explain the process of diagnosing heart disease through ECG using wavelet transformation and Adaptive Neuro Fuzzy Inference System (ANFIS).The process of diagnosing heart disease begins with cutting ECG signal consisting of 9-11 waves into one ECG wave, then decomposition and extraction are performed using wavelet transformation to obtain 6 parameters. The parameters will be used as input in ANFIS model. Data obtained from ECG extraction are divided into 70% training data and 30% testing data The output from the ANFIS model is a diagnosis of heart diseases, such as left bundle branch block (LBBB),  right bundle branch block (RBBB), and normal. ANFIS learning is divided into 6 stages, namely clustering data with Fuzzy C-Means method, computing the degree of membership of each data, determining fixed neurons, looking for normalized firing strength, calculating the consequent parameter values, and determining network output.The results of the study obtained the best ANFIS model with 10 clusters. The level of accuracy, specificity, and sensitivity for training data is 100%, 100%, and 100%, respectively and for the testing data, the level of accuracy, specificity, and sensitivity is 100%, 100%, and 100%, respectively.

AN ADAPTIVE-NETWORK-BASED FUZZY INFERENCE SYSTEM FOR PREDICTING SPRINGBACK OF U-BENDING

2013 ◽  
Vol 37 (3) ◽  
pp. 335-344 ◽  
Author(s):  
Bor-Tsuen Lin ◽  
Kun-Min Huang
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Training Data ◽  
Adaptive Network ◽  
Anfis Model ◽  
Inference System ◽  
Bending Process ◽  
Punch Radius ◽  
Triangular Membership Function ◽  
Springback Angle

Springback will occur when the external force is removed after bending process in sheet metal forming. This paper proposed an adaptive-network-based fuzzy inference system (ANFIS) model for prediction the springback angle of the SPCC material after U-bending. Three parameters were selected as the main factors of affecting the springback after bending, including the die clearance, the punch radius, and the die radius. The training data were obtained from results of U-bending experiment. The training data with four different membership functions – triangular, trapezoidal, bell, and Gaussian functions – were employed in the ANFIS to construct a predictive model for the springback of the U-bending. After the comparison of the predicted value with the checking data, the results show that the triangular membership function has the best accuracy, which make it the best function to predict the springback angle of sheet metals after U-bending.

Nonlinear System Identification of Smart Buildings

Fuzzy Systems ◽  
2017 ◽  
pp. 1183-1202
Author(s):  
Soroush Mohammadzadeh ◽  
Yeesock Kim
Keyword(s):  
System Identification ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Nonlinear Behavior ◽  
Computation Time ◽  
Principal Component ◽  
Training Data ◽  
Inference System ◽  
Smart Buildings ◽  
Neuro Fuzzy

In this book chapter, a system identification method for modeling nonlinear behavior of smart buildings is discussed that has a significantly low computation time. To reduce the size of the training data used for the adaptive neuro-fuzzy inference system (ANFIS), principal component analysis (PCA) is used, i.e., PCA-based adaptive neuro-fuzzy inference system: PANFIS. The PANFIS model is evaluated on a seismically excited three-story building equipped with a magnetorheological (MR) damper. The PANFIS model is trained using an artificial earthquake that contains a variety of characteristics of earthquakes. The trained PANFIS model is tested using four different earthquakes. It was demonstrated that the proposed PANFIS model is effective in modeling nonlinear behavior of a smart building with significant reduction in computational loads.

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