A PROCESS PARAMETERS DETERMINATION MODEL BY INTEGRATING ARTIFICIAL NEURAL NETWORK AND ANT COLONY OPTIMIZATION

2005 ◽  
Vol 22 (4) ◽  
pp. 346-354 ◽  
Author(s):  
Chwen-Tzeng Su ◽  
Jui-Tsung Wong ◽  
Shang-Chun Tsou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ant Colony Optimization ◽  
Process Parameters ◽  
Ant Colony ◽  
Artificial Neural

scholarly journals Optimization of Cerbera manghas Biodiesel Production Using Artificial Neural Networks Integrated with Ant Colony Optimization

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3811 ◽  
Author(s):  
Arridina Silitonga ◽  
Teuku Mahlia ◽  
Abd Shamsuddin ◽  
Hwai Ong ◽  
Jassinnee Milano ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ant Colony Optimization ◽  
Process Parameters ◽  
Network Models ◽  
Biodiesel Production ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
Cerbera Manghas

Optimizing the process parameters of biodiesel production is the key to maximizing biodiesel yields. In this study, artificial neural network models integrated with ant colony optimization were developed to optimize the parameters of the two-step Cerbera manghas biodiesel production process: (1) esterification and (2) transesterification. The parameters of esterification and transesterification processes were optimized to minimize the acid value and maximize the C. manghas biodiesel yield, respectively. There was excellent agreement between the average experimental values and those predicted by the artificial neural network models, indicating their reliability. These models will be useful to predict the optimum process parameters, reducing the trial and error of conventional experimentation. The kinetic study was conducted to understand the mechanism of the transesterification process and, lastly, the model could measure the physicochemical properties of the C. manghas biodiesel.

scholarly journals Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

2021 ◽  
Author(s):  
Sherwan Mohammed Najm ◽  
Imre Paniti
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Single Point ◽  
Tool Material ◽  
Deformation Mode ◽  
Geometric Accuracy ◽  
Forming Process ◽  
Tool Shape ◽  
Artificial Neural

AbstractIncremental Sheet Forming (ISF) has attracted attention due to its flexibility as far as its forming process and complexity in the deformation mode are concerned. Single Point Incremental Forming (SPIF) is one of the major types of ISF, which also constitutes the simplest type of ISF. If sufficient quality and accuracy without defects are desired, for the production of an ISF component, optimal parameters of the ISF process should be selected. In order to do that, an initial prediction of formability and geometric accuracy helps researchers select proper parameters when forming components using SPIF. In this process, selected parameters are tool materials and shapes. As evidenced by earlier studies, multiple forming tests with different process parameters have been conducted to experimentally explore such parameters when using SPIF. With regard to the range of these parameters, in the scope of this study, the influence of tool material, tool shape, tool-end corner radius, and tool surface roughness (Ra/Rz) were investigated experimentally on SPIF components: the studied factors include the formability and geometric accuracy of formed parts. In order to produce a well-established study, an appropriate modeling tool was needed. To this end, with the help of adopting the data collected from 108 components formed with the help of SPIF, Artificial Neural Network (ANN) was used to explore and determine proper materials and the geometry of forming tools: thus, ANN was applied to predict the formability and geometric accuracy as output. Process parameters were used as input data for the created ANN relying on actual values obtained from experimental components. In addition, an analytical equation was generated for each output based on the extracted weight and bias of the best network prediction. Compared to the experimental approach, analytical equations enable the researcher to estimate parameter values within a relatively short time and in a practicable way. Also, an estimate of Relative Importance (RI) of SPIF parameters (generated with the help of the partitioning weight method) concerning the expected output is also presented in the study. One of the key findings is that tool characteristics play an essential role in all predictions and fundamentally impact the final products.

The Artificial Neural Network and Regression Analysis Approach to Prediction of the Fiber Diameter of Spunbonding Fabrics

2010 ◽  
Vol 33 ◽  
pp. 74-78
Author(s):  
B. Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Network Model ◽  
Process Parameters ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Fiber Diameter ◽  
Statistical Regression ◽  
Artificial Neural

In this work, the artificial neural network model and statistical regression model are established and utilized for predicting the fiber diameter of spunbonding nonwovens from the process parameters. The artificial neural network model has good approximation capability and fast convergence rate, which is used in this research. The results show the artificial neural network model can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the statistical regression model, which reveals that the artificial neural network model is based on the inherent principles, and it can yield reasonably good prediction results and provide insight into the relationship between process parameters and fiber diameter.

scholarly journals Modelling of Rotary EDM Process Parameters of Inconel 718 Using Artificial Neural Networks

Mechanika ◽  
2020 ◽  
Vol 26 (6) ◽  
pp. 540-544
Author(s):  
Jayaraj JEEVAMALAR ◽  
Sundaresan RAMABALAN ◽  
Chinnamuthu SENTHILKUMAR
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Inconel 718 ◽  
Machining Process ◽  
Artificial Neural ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Matlab Programming ◽  
The Relationship

Modelling is used for correlating the relationship between the input process parameters and the output responses during the machining process. To characterize real-world systems of considerable complexity, an Artificial Neural Network (ANN) model is regularly used to replace the mathematical approximation of the relationship. This paper explains the methodological procedure and the outcome of the ANN modeling process for Electrical Discharge Drilling of Inconel 718 superalloy and hollow tubular copper as tool electrode. The most important process parameters in this work are peak current, pulse on time and pulse off time with machining performances of material removal rate and surface roughness. The experiments were performed by L20 Orthogonal Array. In such conditions, an Artificial Neural Network model is developed using MATLAB programming on the Feed Forward Back Propagation technique was used to predict the responses. The experimental data were separated into three parts to train, test the network and validate the model. The developed model has been confirmed experimentally for training and testing in considering the number of iterations and mean square error convergence criteria. The developed model results are to approximate the responses fairly exactly. The model has the mean correlation coefficient of 0.96558. Results revealed that the proposed model can be used for the prediction of the complex EDM drilling process.

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