Selection of Bending Parameters for Minimal Spring-Back Using an ANFIS Model and Simulated Annealing Algorithm

2011 ◽  
Vol 133 (3) ◽  
Author(s):  
H. Baseri ◽  
B. Rahmani ◽  
M. Bakhshi-Jooybari
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Fuzzy Inference ◽  
Sheet Thickness ◽  
Spring Back ◽  
Anfis Model ◽  
Inference System ◽  
Bending Process ◽  
Annealing Algorithm ◽  
Tip Radius

In this research, a simulated annealing algorithm was used to minimize the spring-back in V-die bending process. First, an adaptive neuro-fuzzy inference system (ANFIS) model was developed using the data generated based on experimental observations. The output parameter of the ANFIS model is spring-back and the input parameters are sheet thickness, sheet orientation, and punch tip radius. The performance of the ANFIS model in training and testing sets is compared with those observations. The results indicated that the ANFIS model can be applied successfully for prediction of spring-back. Then, the ANFIS model was used as a function in simulated annealing algorithm to minimize the spring-back. The results showed that the proposed model has an acceptable performance to optimize the bending process.

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 Training of ANFIS with simulated annealing algorithm on flexural buckling load prediction of aluminium alloy columns

2020 ◽  
pp. 15-23
Author(s):  
Bulent Haznedar ◽  
Rabia Bayraktar ◽  
Melih Yayla ◽  
Mustafa Diyar Demirkol
Keyword(s):  
Simulated Annealing ◽  
Aluminium Alloy ◽  
Soft Computing ◽  
Simulated Annealing Algorithm ◽  
Optimization Algorithms ◽  
Load Prediction ◽  
Inference System ◽  
Flexural Buckling ◽  
Bee Colony ◽  
Annealing Algorithm

In this study, we propose a simulated annealing algorithm (SA) to train an adaptive neurofuzzy inference system (ANFIS). We performed different types of optimization algorithms such as genetic algorithm (GA), SA and artificial bee colony algorithm on two different problem types. Then, we measured the performance of these algorithms. First, we applied optimization algorithms on eight numerical benchmark functions which are sphere, axis parallel hyper-ellipsoid, Rosenbrock, Rastrigin, Schwefel, Griewank, sum of different powers and Ackley functions. After that, the training of ANFIS is carried out by mentioned optimization algorithms to predict the strength of heat-treated fine-drawn aluminium composite columns defeated by flexural bending. In summary, the accuracy of the proposed soft computing model was compared with the accuracy of the results of existing methods in the literature. It is seen that the training of ANFIS with the SA has more accuracy.   Keywords: Soft computing, ANFIS, simulated annealing, flexural buckling, aluminium alloy columns.

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.

A New Solution for Maxterm Problem in Trigonometric Functions by Simulated Annealing Algorithm

2013 ◽  
Vol 4 (2) ◽  
pp. 20-28
Author(s):  
Farhad Soleimanian Gharehchopogh ◽  
Hadi Najafi ◽  
Kourosh Farahkhah
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Trigonometric Functions ◽  
Matlab Software ◽  
Annealing Algorithm ◽  
Do So

The present paper is an attempt to get total minimum of trigonometric Functions by Simulated Annealing. To do so the researchers ran Simulated Annealing. Sample trigonometric functions and showed the results through Matlab software. According the Simulated Annealing Solves the problem of getting stuck in a local Maxterm and one can always get the best result through the Algorithm.

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