The Study of EDG GH3536 Surface Roughness Base on the Artificial Neural Network Modeling

2013 ◽  
Vol 690-693 ◽  
pp. 3175-3179
Author(s):  
Ji Gao ◽  
Di Wang ◽  
Yao Sun
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Neural Network Modeling ◽  
Pulse Interval ◽  
Peak Voltage ◽  
Process Indicators ◽  
Neural Network Algorithm ◽  
Artificial Neural

The process parameters of electrical discharge grinding,such as workpiece polarity, pulse width, pulse interval, peak current, peak voltage, all have influence on GH3536’s surface roughness.General method is difficult to determine the relationship between the process parameters and the process indicators. This article established a artificial neural network model of EDG GH3536 surface roughness which can forecast. Neural network algorithm use BP algorithm, the network structure is the 2-4-1.

The Study of EDM TC11 Surface Discharge Mark Diameter Based on the Artificial Neural Network Modeling

2014 ◽  
Vol 490-491 ◽  
pp. 586-589
Author(s):  
Ji Gao ◽  
Di Wang ◽  
Yao Sun ◽  
Shuai Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Electrical Discharge Machining ◽  
Surface Discharge ◽  
Neural Network Modeling ◽  
Pulse Interval ◽  
Peak Voltage ◽  
Process Indicators ◽  
Artificial Neural

The process parameters of electrical discharge machining, such as : workpiece polarity, pulse width, pulse interval, peak current, peak voltage, all have influence on TC11’s surface roughness.But general methods are difficult to determine the relationship between the process parameters and the process indicators. This article established a artificial neural network model of EDM TC11 surface discharge mark diameter which can forecast. Neural network algorithm used BP algorithm, the network structure was the 2-4-1.

scholarly journals Application of Neural Network Algorithm in Propylene Distillation

2021 ◽  
pp. 53-63
Author(s):  
Jinwei Lu ◽  
Ningrui Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Functional Relationship ◽  
Learning Ability ◽  
Neural Network Modeling ◽  
Distillation Tower ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Two Phases ◽  
Propylene Yield

Artificial neural network modeling does not need to consider the mechanism. It can map the implicit relationship between input and output and predict the performance of the system well. At the same time, it has the advantages of self-learning ability and high fault tolerance. The gas-liquid two phases in the rectification tower conduct interphase heat and mass transfer through countercurrent contact. The functional relationship between the product concentration at the top and bottom of the tower and the process parameters is extremely complex. The functional relationship can be accurately controlled by artificial neural network algorithms. The key components of the propylene distillation tower are the propane concentration at the top of the tower and the propylene concentration at the bottom of the tower. Accurate measurement of them plays a key role in increasing propylene yield in ethylene production enterprises. This article mainly introduces the development process of neural network model and its application progress in propylene distillation tower.

scholarly journals Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3108
Author(s):  
Mirko Ficko ◽  
Derzija Begic-Hajdarevic ◽  
Maida Cohodar Husic ◽  
Lucijano Berus ◽  
Ahmet Cekic ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Mean Squared Error ◽  
Water Jet ◽  
Depth Of Cut ◽  
Abrasive Water Jet ◽  
Ann Model ◽  
Artificial Neural

The study’s primary purpose was to explore the abrasive water jet (AWJ) cut machinability of stainless steel X5CrNi18-10 (1.4301). The study analyzed the effects of such process parameters as the traverse speed (TS), the depth of cut (DC), and the abrasive mass flow rate (AR) on the surface roughness (Ra) concerning the thickness of the workpiece. Three different thicknesses were cut under different conditions; the Ra was measured at the top, in the middle, and the bottom of the cut. Experimental results were used in the developed feed-forward artificial neural network (ANN) to predict the Ra. The ANN’s model was validated using k-fold cross-validation. A lowest test root mean squared error (RMSE) of 0.2084 was achieved. The results of the predicted Ra by the ANN model and the results of the experimental data were compared. Additionally, as TS and DC were recognized, analysis of variance at a 95% confidence level was used to determine the most significant factors. Consequently, the ANN input parameters were modified, resulting in improved prediction; results show that the proposed model could be a useful tool for optimizing AWJ cut process parameters for predicting Ra. Its main advantage is the reduced time needed for experimentation.

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