scholarly journals Determining optimal combination of roller compacted concrete pavement mixture containing recycled asphalt pavement and crumb rubber using hybrid artificial neural network–genetic algorithm method considering energy absorbency approach

2017 ◽  
Vol 44 (11) ◽  
pp. 945-955 ◽  
Author(s):  
Mansour Fakhri ◽  
Ershad Amoosoltani ◽  
Mona Farhani ◽  
Amin Ahmadi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Fitness Function ◽  
Crumb Rubber ◽  
Concrete Pavement ◽  
Genetic Operators ◽  
Efficiency Criterion ◽  
Roller Compacted Concrete

The present study investigates the effectiveness of evolutionary algorithms such as genetic algorithm (GA) evolved neural network in estimating roller compacted concrete pavement (RCCP) characteristics including flexural and compressive strength of RCC and also energy absorbency of mixes with different compositions. A real coded GA was implemented as training algorithm of feed forward neural network to simulate the models. The genetic operators were carefully selected to optimize the neural network, avoiding premature convergence and permutation problems. To evaluate the performance of the genetic algorithm neural network model, Nash-Sutcliffe efficiency criterion was employed and also utilized as fitness function for genetic algorithm which is a different approach for fitting in this area. The results showed that the GA-based neural network model gives a superior modeling. The well-trained neural network can be used as a useful tool for modeling RCC specifications.

An explicit methodology for manufacturing cost–tolerance modeling and optimization using the neural network integrated with the genetic algorithm

2020 ◽  
Vol 34 (3) ◽  
pp. 430-443
Author(s):  
A. Saravanan ◽  
J. Jerald ◽  
A. Delphin Carolina Rani
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Network Models ◽  
Manufacturing Cost ◽  
Genetic Operators ◽  
Neural Network Models ◽  
Data Set ◽  
The Neural Network

AbstractThe objective of the paper is to develop a new method to model the manufacturing cost–tolerance and to optimize the tolerance values along with its manufacturing cost. A cost–tolerance relation has a complex nonlinear correlation among them. The property of a neural network makes it possible to model the complex correlation, and the genetic algorithm (GA) is integrated with the best neural network model to optimize the tolerance values. The proposed method used three types of neural network models (multilayer perceptron, backpropagation network, and radial basis function). These network models were developed separately for prismatic and rotational parts. For the construction of network models, part size and tolerance values were used as input neurons. The reference manufacturing cost was assigned as the output neuron. The qualitative production data set was gathered in a workshop and partitioned into three files for training, testing, and validation, respectively. The architecture of the network model was identified based on the best regression coefficient and the root-mean-square-error value. The best network model was integrated into the GA, and the role of genetic operators was also studied. Finally, two case studies from the literature were demonstrated in order to validate the proposed method. A new methodology based on the neural network model enables the design and process planning engineers to propose an intelligent decision irrespective of their experience.

Soft computing techniques to model and optimize magnetic field-assisted finishing process and characterization of the finished surface

2017 ◽  
Vol 232 (17) ◽  
pp. 3156-3168
Author(s):  
Anwesa Barman ◽  
Manas Das
Keyword(s):  
Neural Network ◽  
Magnetic Field ◽  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Network Model ◽  
Process Parameters ◽  
Neural Network Model ◽  
Output Process ◽  
Input And Output ◽  
Finishing Process

In magnetic field-assisted finishing process, magnetorheological polishing fluid is used for precision polishing of freeform surfaces in the nanometer range. An efficient model is derived to accurately relate the input and output process parameters for better prediction of finishing performance. In this study, the relationship between the input and output process parameters of magnetic field-assisted finishing process is established using back-propagation neural network technique. Also, a close comparison between the regression analysis and neural network model has been carried out. The simulation results from neural network model better matches with the experimental data. Hence, this particular neural network model can be utilized to predict the response variables. A further optimization study using genetic algorithm and simulated annealing techniques is carried out to optimize the input process parameters for achieving maximum finishing performance. It is found that the results obtained from the genetic algorithm is more accurate and matches with the experimental results than the simulated annealing. Further, a characterization study of the finished workpiece surface is carried out which shows that MFAF process can achieve surface finish in the nanometer range having a minimum surface roughness value of 70 nm.

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