Modeling the corrosion behavior of Ni-Cr-Mo-V high strength steel in the simulated deep sea environments using design of experiment and artificial neural network

2019 ◽  
Vol 35 (1) ◽  
pp. 168-175 ◽  
Author(s):  
Qiangfei Hu ◽  
Yuchen Liu ◽  
Tao Zhang ◽  
Shujiang Geng ◽  
Fuhui Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Corrosion Behavior ◽  
Design Of Experiment ◽  
Deep Sea ◽  
High Strength Steel ◽  
High Strength ◽  
Artificial Neural

scholarly journals Sustainable Synthesis Processes for Carbon Dots through Response Surface Methodology and Artificial Neural Network.

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 704 ◽  
Author(s):  
Yahaya Pudza ◽  
Zainal Abidin ◽  
Abdul Rashid ◽  
Md Yasin ◽  
Noor ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Design Of Experiment ◽  
Smart Materials ◽  
Carbon Dots ◽  
Synthesis Process ◽  
Artificial Neural ◽  
Hidden Layer

Nowadays, to ensure sustainability of smart materials, it is imperative to eliminate or reduce carbon footprint related to nano material production. The concept of design of experiment to provide an optimal synthesis process, with a desired yield, is indispensable. It is the researcher’s goal to get optimum value for experiments that requires multiple runs and multiple inputs. Herein, is a reliable approach of utilizing design of experiment (DOE) for response surface methodology (RSM). Thus, to optimize a facile and effective synthesis process for fluorescent carbon dots (CDs) derived from tapioca that is in line with green chemistry principles for sustainable synthesis. The predictions for fluorescent CDs synthesis from RSM were in excellent agreement with the artificial neural network (ANN) model prediction by the Levenberg–Marquardt back propagation (LMBP) algorithm. Considering R2, root mean square error (RMSE) and mean absolute error (MAE) have all revealed a positive hidden layer size. The best hidden layer of neurons were discovered at point 4-8, to confirm the validity of carbon dots, characterization of surface morphology and particles sizes of CDs were conducted with favorable confirmations of the unique characteristics and attributes of synthesized CDs by hydrothermal route.

Seismic Behavior of Concrete Filled Circular Steel Tubular Columns Based on Artificial Neural Network

2012 ◽  
Vol 502 ◽  
pp. 189-192
Author(s):  
Hua Wei ◽  
Yu Du ◽  
Hai Jun Wang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
High Strength ◽  
Steel Tube ◽  
Structure Design ◽  
Seismic Properties ◽  
Artificial Neural ◽  
Propagation Network

Artificial neural network (ANN) is self-adaptability, fault toleration and fuzziness. It is suitable to solve the seismic properties of high strength reinforced concrete columns with concrete filled steel tube core (HRCCFT). A three-layer back-propagation network model is build up to study the seismic properties of HRCCFT. The model is trained according to 30 sets of experimental data. The network convergence is fast. The model is verified by 8 groups of experimental data, the results show the predicted values of displacement ductility are in good agreement with test values. The precision of model is better than that of formula from other reference. This method is good enough to be used as an auxiliary method for structure design.

scholarly journals Application of Artificial Neural Network to the Prediction of Tensile Properties in High-Strength Low-Carbon Bainitic Steels

Metals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1314
Author(s):  
Sang-In Lee ◽  
Seung-Hyeok Shin ◽  
Byoung-Chul Hwang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Yield Strength ◽  
Tensile Properties ◽  
Uniform Elongation ◽  
High Strength ◽  
Low Carbon ◽  
Ann Model ◽  
Bainitic Steels ◽  
Artificial Neural

An artificial neural network (ANN) model was designed to predict the tensile properties in high-strength, low-carbon bainitic steels with a focus on the fraction of constituents such as PF (polygonal ferrite), AF (acicular ferrite), GB (granular bainite), and BF (bainitic ferrite). The input parameters of the model were the fraction of constituents, while the output parameters of the model were composed of the yield strength, yield-to-tensile ratio, and uniform elongation. The ANN model to predict the tensile properties exhibited a higher accuracy than the multi linear regression (MLR) model. According to the average index of the relative importance for the input parameters, the yield strength, yield-to-tensile ratio, and uniform elongation could be effectively improved by increasing the fraction of AF, bainitic microstructures (AF, GB, and BF), and PF, respectively, in terms of the work hardening and dislocation slip behavior depending on their microstructural characteristics such as grain size and dislocation density. The ANN model is expected to provide a clearer understanding of the complex relationships between constituent fraction and tensile properties in high-strength, low-carbon bainitic steels.

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