GENETIC ALGORITHM-ASSISTED ARTIFICIAL NEURAL NETWORK FOR THE ESTIMATION OF DRILLING PARAMETERS OF MAGNESIUM AZ91 IN VERTICAL MILLING MACHINE

2020 ◽  
Vol 27 (10) ◽  
pp. 1950221
Author(s):  
M. VARATHARAJULU ◽  
G. JAYAPRAKASH ◽  
N. BASKAR ◽  
A. SARAVANAN
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
High Speed Steel ◽  
Milling Machine ◽  
Ann Model ◽  
Drilling Parameters ◽  
Vertical Milling ◽  
Vertical Milling Machine ◽  
Artificial Neural

The selection of appropriate drilling parameters is essential for improving productivity and part quality, therefore, this work mainly concentrates on the investigation of drilling time, burr height, burr thickness, roundness and surface roughness. The drilling experiments were carried out on Magnesium (Mg) AZ91 with High Speed Steel (HSS) tool using the Vertical Milling Machine (VMM). The parameters reckoned are spindle speed and feed rate. Artificial Neural Network (ANN) was concerned with the building of the model that will be used to forecast the responses following the consideration of Response Surface Methodology (RSM). Conventional method of modeling (RSM) yields poorer results which redirected the study with ANN. The Genetic Algorithm (GA)-based ANN has been reckoned for developing the model. With two nodes in the parameter layer and seven nodes in the response layer, six different networks were constructed using variety of nodes in the hidden layers which are 2–6–7, 2–7–7, 2–8–7, 2–6–6–7, 2–7–6–7 and 2–8–6–7. It is observed that the 2–8–7 network offers the best ANN model in predicting the various responses. The prediction results ensure the reliability of the ANN model to analyze the effect of drilling parameters over the various responses.

Modeling and Optimization of M-cresol Isopropylation for Obtaining N-thymol: Combining a Hybrid Artificial Neural Network with a Genetic Algorithm

2007 ◽  
Vol 5 (1) ◽  
Author(s):  
Yao Kouassi Benjamin ◽  
Emmanuel Assidjo Nogbou ◽  
Gossan Ado ◽  
Catherine Azzaro-Pantel ◽  
André Davin
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Molar Ratio ◽  
Ann Model ◽  
Sequential Method ◽  
Modeling And Optimization ◽  
Artificial Neural ◽  
Better Than

The application of a hybrid framework based on the combination, artificial neural network-genetic algorithm (ANN-GA), for n-thymol synthesis modeling and optimization has been developed. The effects of molar ratio propylene/cresol (X1), catalyst mass (X2) and temperature (X3) on n-thymol selectivity Y1 and m-cresol conversion Y2 were studied. A 3-8-2 ANN model was found to be very suitable for reaction modeling. The multiobjective optimization, led to optimal operating conditions (0.55 ? X1 ? 0.77; 1.773 g ? X2 ? 1.86 g; 289.74 °C ? X3 ? 291.33 °C) representing good solutions for obtaining high n-thymol selectivity and high m-cresol conversion. This optimal zone corresponded to n-thymol selectivity and m-cresol conversion ranging respectively in the interval [79.3; 79.5]% and [13.4 %; 23.7]%. These results were better than those obtained with a sequential method based on experimental design for which, optimum conditions led to n-thymol selectivity and m-cresol conversion values respectively equal to 67% and 11%. The hybrid method ANN-GA showed its ability to solve complex problems with a good fitting.

scholarly journals Intelligent Prediction Model of the Triaxial Compressive Strength of Rock Subjected to Freeze-Thaw Cycles Based on a Genetic Algorithm and Artificial Neural Network

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xin Xiong ◽  
Feng Gao ◽  
Keping Zhou ◽  
Yuxu Gao ◽  
Chun Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Rock Mass ◽  
Ann Model ◽  
Cold Regions ◽  
Freeze Thaw ◽  
Triaxial Compressive Strength ◽  
Artificial Neural

Rock compressive strength is an important mechanical parameter for the design, excavation, and stability analysis of rock mass engineering in cold regions. Accurate and rapid prediction of rock compressive strength has great engineering value in guiding the efficient construction of rock mass engineering in a cold regions. In this study, the prediction of triaxial compressive strength (TCS) for sandstone subjected to freeze-thaw cycles was proposed using a genetic algorithm (GA) and an artificial neural network (ANN). For this purpose, a database including four model inputs, namely, the longitudinal wave velocity, porosity, confining pressure, and number of freeze-thaw cycles, and one output, the TCS of the rock, was established. The structure, initial connection weights, and biases of the ANN were optimized progressively based on GA. After obtaining the optimal GA-ANN model, the performance of the GA-ANN model was compared with that of a simple ANN model. The results revealed that the proposed hybrid GA-ANN model had a higher accuracy in predicting the testing datasets than the simple ANN model: the root mean square error (RMSE), mean absolute error (MAE), and R squared ( R 2 ) were equal to 1.083, 0.893, and 0.993, respectively, for the hybrid GA-ANN model, while the corresponding values were 2.676, 2.153, and 0.952 for the simple ANN model.

Optimization of Culture Medium for Maximal Production of Spinosad Using an Artificial Neural Network - Genetic Algorithm Modeling

2015 ◽  
Vol 25 (4) ◽  
pp. 253-261 ◽  
Author(s):  
Zhou Lan ◽  
Chen Zhao ◽  
Weiqun Guo ◽  
Xiong Guan ◽  
Xiaolin Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Ann Model ◽  
Saccharopolyspora Spinosa ◽  
Medium Components ◽  
Key Factor ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Rsm Model

<b><i>Background:</i></b> Spinosyns, products of secondary metabolic pathway of <i>Saccharopolyspora spinosa</i>, show high insecticidal activity, but difficulty in enhancing the spinosad yield affects wide application. The fermentation process is a key factor in this case. <b><i>Methods:</i></b> The response surface methodology (RMS) and artificial neural network (ANN) modeling were applied to optimize medium components for spinosad production using <i>S. spinosa </i>strain CGMCC4.1365. Experiments were performed using a rotatable central composite design, and the data obtained were used to construct an ANN model and an RSM model. Using a genetic algorithm (GA), the input space of the ANN model was optimized to obtain optimal values of medium component concentrations. <b><i>Results:</i></b> The regression coefficients (R<sup>2</sup>) for the ANN and RSM models were 0.9866 and 0.9458, respectively, indicating that the fitness of the ANN model was higher. The maximal spinosad yield (401.26 mg/l) was obtained using ANN/GA-optimized concentrations. <b><i>Conclusion:</i></b> The hybrid ANN/GA approach provides a viable alternative to the conventional RSM approach for the modeling and optimization of fermentation processes.

scholarly journals Short-term water demand predictions coupling an Artificial Neural Network model and a Genetic Algorithm

2021 ◽  
Author(s):  
Majid Gholami Shirkoohi ◽  
Mouna Doghri ◽  
Sophie Duchesne
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Water Demand ◽  
Urban Water ◽  
Ann Model ◽  
Short Term ◽  
Urban Water Demand ◽  
Artificial Neural ◽  
Mean Square Errors

Abstract The application of artificial neural network (ANN) models for short-term (15 min) urban water demand predictions is evaluated. Optimization of the ANN model's hyperparameters with a Genetic Algorithm (GA) and use of a growing window approach for training the model are also evaluated. The results are compared to those of commonly used time series models, namely the Autoregressive Integrated Moving Average (ARIMA) model and a pattern-based model. The evaluations are based on data sets from two Canadian cities, providing 15 minute water consumption records over respectively 5 years and 23 months, with a respective mean water demand of 14,560 and 887 m3/d. The GA optimized ANN model performed better than the other models, with Nash-Sutcliffe Efficiencies of 0.91 and 0.83, and Relative Root Mean Square Errors of 6 and 16% for City 1 and City 2, respectively. The results of this study indicate that the optimization of the hyperparameters of an ANN model can lead to better 15 min urban water demand predictions, which are useful for many real time control applications, such as dynamic pressure control.

scholarly journals Optimizing the Rail Profile for High-Speed Railways Based on Artificial Neural Network and Genetic Algorithm Coupled Method

2020 ◽  
Vol 12 (2) ◽  
pp. 658
Author(s):  
Hanwen Jiang ◽  
Liang Gao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
High Speed ◽  
Sustainable Transportation ◽  
Ann Model ◽  
Coupled Method ◽  
Rail Wear ◽  
Wide Range ◽  
Artificial Neural

Though the high-speed railways are seen as a sustainable form of transportation, the fact that the rail wear in high-speed railways negatively affects the running safety and riding comfort, as well as the maintenance of railways, has drawn a wide range of concerns among researchers and scholars. In order to reduce the rail wear and achieve the goal of sustainable transportation, this paper proposes an ingenious optimization program of rail profiles based on the artificial neural network (ANN) and genetic algorithm (GA) coupled method. The candidate solutions of the nonlinear GA programming model are regarded as the inputs of the trained ANN model. Meanwhile, the outputs of the trained ANN model serve as the objective functions of the GA model. The computational results show that the optimized rail profile not only has superior performances in terms of the wheel/rail wear and contact conditions, but also maintains good dynamic performances. Therefore, this study can provide the theoretical and practical basis for the design and the preventive grinding of rails in the high-speed railways. Also, the ANN-GA coupled model can be extended and further employed on the optimization of other rail profiles.

scholarly journals Optimization of Position and Number of Hotspot Detectors Using Artificial Neural Network and Genetic Algorithm to Estimate Material Levels Inside a Silo

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4427
Author(s):  
Jeong Hoon Rhee ◽  
Sang Il Kim ◽  
Kang Min Lee ◽  
Moon Kyum Kim ◽  
Yun Mook Lim
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Economic Feasibility ◽  
Ann Model ◽  
Ground Truth Data ◽  
Efficient Operation ◽  
Internal Storage ◽  
Artificial Neural

To realize efficient operation of a silo, level management of internal storage is crucial. In this study, to address the existing measurement limitations, a silo hotspot detector, which is typically utilized for internal silo temperature monitoring, was employed. The internal temperature data measured using the hotspot detectors were used to train an artificial neural network (ANN) algorithm to predict the level of the internal storage of the silo. The prediction accuracy was evaluated by comparing the predicted data with ground truth data. We combined the ANN model with the genetic algorithm (GA) to improve the prediction accuracy and establish efficient sensor installation positions and number to proceed with optimization. Simulation results demonstrated that the best predictive performance (up to 97% accuracy) was achieved when the ANN structure was 9-19-19-1. Furthermore, the numbers of efficient sensors and sensors positions determined using the proposed ANN-GA technique were reduced from seven to five or four, thereby ensuring economic feasibility.

Optimal Tilt Angle for PV Modules Using the Neural-Genetic Algorithm Considering Mathematical Model of the Solar Orbit and Position

2012 ◽  
Vol 512-515 ◽  
pp. 250-253 ◽  
Author(s):  
Ying Pin Chang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Tilt Angle ◽  
Experimental System ◽  
Solar Irradiation ◽  
Continuous Variables ◽  
Ann Model ◽  
Pv Modules ◽  
Artificial Neural

This paper presents a method which combines an artificial neural network and a genetic algorithm (ANNGA) in determining the tilt angle for photovoltaic (PV) modules. First, a Taguchi experiment was used to perform an efficient experimental design and analyze the robustness of the tilt angles for fixed south-facing PV modules. Following, the results from the Taguchi experiment were used as the learning data for an artificial neural network (ANN) model that could predict the tilt angles at discrete levels. Finally, a genetic algorithm method was applied to obtain a robust tilt angle setting of the tilt angle of PV modules with continuous variables. The objective is to maximize the electrical energy of the modules. In this study, three Taiwanese areas were selected for analysis. The position of the sun at any time and location was predicted by the mathematical procedure of Julian dating; then, the solar irradiation was obtained at each site under a clear sky. To confirm the computer simulation results, experimental system are conducted for determining the optimum tilt angle of the modules. The results show that the seasonal optimum angle is 26.4 (deg.) for February-March-April; -9.47(deg.) for May-June-July, 21.32(deg.) for August-September-October and 53.13(deg.) from November-December-January in the Taiwan area.

scholarly journals Artificial Neural Network Hyperparameter Effectiveness Determination And Optimization Algorithm

2021 ◽  
Author(s):  
James VanderVeen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Model Performance ◽  
Activation Function ◽  
Ann Model ◽  
Reproductive Process ◽  
Hyperparameter Selection ◽  
Artificial Neural ◽  
Individual Variables

Machine learning models can contain many layers and branches. Each branch and layer, contain individual variables, know as hyperparameters, that require manual tuning. For instance, the genetic algorithm designed by Unit Amin [2] was designed to mimic the reproductive process of living organisms. The genetic algorithm and the Artificial Neural Network (ANN) training processes contain inherent randomness that reduces the replicability of results. Combined with the sheer magnitude of hyperparameter permutations, confidence that model has arrived at the best solution may be low. The algorithm designed for this thesis was designed to isolate portions of a complex ANN model and generate results showing the effect each hyperparameter has on the performance of the model as a whole. The results of this thesis show that the algorithm effectively generates data correlating model performance to hyperparameter selection. This is evident in section 3.1, and 3.2, where it is shown that using the sigmoid activation function with CNN layers, regardless of the number of filters, or hyperparameters used in the subsequent LSTM layers, produces superior RMSE scores. Section 3.2 also reveals that the model does not improve in performance as the number of CNN and LSTM layers are added to the model. Finally, the results in section 3.3 show that the rmsprop optimizer generates superior results regardless of the hyperparameters used in the rest of the model.

Optimization of the Culture Medium for Rhodospirillum rubrum S1 with an Artificial Neural Network Model and GA

2013 ◽  
Vol 756-759 ◽  
pp. 172-175
Author(s):  
Yan Pan ◽  
Wei Jian Wang ◽  
Hai Juan Tian ◽  
Shi Hao Li ◽  
Zhu Zhu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Artificial Neural Network Model ◽  
Culture Medium ◽  
Rhodospirillum Rubrum ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Central Composite

on the basis of the data from the previous box-behnken central composite design, an Artificial Neural Network (ANN) model was constructed for the prediction of outputs of carotenoids. GA (genetic algorithm) was used to search for the optimal culture medium for Rhodospirillum Rubrum S1:citric acid 3.678g/L, Beef extract 3.407 g/L, MgSO4 0.524g/L, FeSO40.023g/L. In the optimal culture medium, it was predicted that the outputs of the carotenoids were13.85 mg/ L.After three verification experiments, the outputs of the carotenoids were 13.72mg/L, the error between the expected value and the experimental value was 0.93%.

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