Modeling the effect of process parameters on the photocatalytic degradation of organic pollutants using artificial neural networks

For modeling and optimizing the process parameters of manufacturing problems in the present days, numerical and Artificial Neural Networks (ANN) methods are widely using. In manufacturing environments, main focus is given to the finding of Optimum machining parameters. Therefore the present research is aimed at finding the optimal process parameters for End milling process. The End milling process is a widely used machining process because it is used for the rough and finish machining of many features such as slots, pockets, peripheries and faces of components. The present work involves the estimation of optimal values of the process variables like, speed, feed and depth of cut, whereas the metal removal rate (MRR) and tool wear resistance were taken as the output .Experimental design is planned using DOE. Optimum machining parameters for End milling process were found out using ANN and compared to the experimental results. The obtained results provβed the ability of ANN method for End milling process modeling and optimization.

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Prediction of Fracture Force of Weld Line Based on Artificial Neural Networks

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.314-316.547 ◽

2011 ◽

Vol 314-316 ◽

pp. 547-553

Author(s):

Peng Fei Zhu ◽

Xiao Fang Sun ◽

Ying Jun Lu ◽

Hai Tian Pan

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Process Parameters ◽

Weld Line ◽

Percentage Error ◽

Maximum Relative Error ◽

Melt Temperature ◽

Data Set ◽

Fracture Force ◽

Artificial Neural

A feed-forward three-layer neural network was proposed to predict the fracture force of injection-molded parts’ weld line. Firstly, the most significant process parameters which affect the fracture force of weld line were analyzed. Secondly, melt temperature, injection pressure, holding pressure and holding time were chosen as import variables and the fracture force of weld line was chosen as output variable to construct artificial neural networks. Furthermore, the performance of ANN was evaluated and tested by its application to verification tests with process parameters randomly selected which all of them were not used in the network training. Results showed that the ANN predictions yield mean absolute percentage error (MAPE) in the range of 0.86％,and maximum relative error (MRE) in the range of 1.84％ for the test data set, and which can comparatively accurately reflect the influence relation of the injection process parameters on part’s quality index under the circumstance of data deficiencies.

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Prediction of Powder Injection Molding Process Parameters Using Artificial Neural Networks

Jurnal Teknologi ◽

10.11113/jt.v59.2590 ◽

2012 ◽

Vol 59 (2) ◽

Author(s):

Javad Rajabi ◽

Norhamidi Muhamad ◽

Maryam Rajabi ◽

Jamal Rajabi

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Injection Molding ◽

Process Parameters ◽

Powder Injection Molding ◽

Powder Injection ◽

Injection Molding Process ◽

Ann Model ◽

Back Propagation Algorithm ◽

Artificial Neural

The parameters of Powder Injection Molding (PIM) process were modeled by artificial neural networks (ANNs). The feed-forward multilayer perceptron was utilized and trained by back-propagation algorithm. Particle size, particle morphology, debinding time, and sintering temperature were taken into account and regarded as inputs of the ANN model. The outputs included relative density, wax loss, shrinkage, and hardness. The results obtained using the ANN model were in good agreement with the experimental data. In fact, they displayed an average R-value of 0.95 versus the experimental values. The optimum architecture of ANN was 7-4-1, in which the network was trained with Levenberg–Marquardt training algorithm. Thus, the ANN model can be used to evaluate, calculate, and forecast PIM process parameters.

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Photocatalytic Degradation of Oxytetracycline in Aqueous Solutions with TiO2in Suspension and Prediction by Artificial Neural Networks

International Journal of Chemical Kinetics ◽

10.1002/kin.21005 ◽

2016 ◽

Vol 48 (8) ◽

pp. 464-473 ◽

Cited By ~ 5

Author(s):

Messaoud Bennemla ◽

Malika Chabani ◽

Abdeltif Amrane

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Aqueous Solutions ◽

Photocatalytic Degradation ◽

Artificial Neural

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Relative density prognosis for directed energy deposition with the help of artificial neural networks

Materials Testing ◽

10.1515/mt-2020-0004 ◽

2021 ◽

Vol 63 (1) ◽

pp. 41-47

Author(s):

Angelina Marko ◽

Andreas Schafner ◽

Julius Raute ◽

Michael Rethmeier

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Relative Density ◽

Process Parameters ◽

Energy Deposition ◽

Data Sets ◽

Artificial Neural ◽

Directed Energy

Abstract Additive manufacturing, and therefore directed energy deposition, is gaining more and more interest from industrial users. However, quality assurance for the components produced is still a challenge. Machine learning, especially using artificial neuronal networks, is a potential method for ensuring a high-quality standard. Based on process parameters and monitoring data, part quality can be predicted. A further advantage is the ability to constantly learn and adopt to slight process changes. First tests using artificial neural networks focus on the prediction of track geometry. The results show that even a small data set is enough to provide high accuracy in the predictions. In this work, an artificial neural network for the predictive analysis of relative density in laser powder cladding has been developed. A central composite experimental design is used to generate 19 data sets. Input variables are laser power, feed rate and powder mass flow. Cubes are built up where density is considered as a target value. Several neural networks are trained and evaluated with these data sets. Different topologies and initial weights are considered. The best network reaches a confidence level of around 90 % for the prediction of relative density based on the process parameters. Finally, the optimization of the generalization performance is investigated. To this purpose, methods of variation in error limit as well as cross-validation are applied. In this way, density is predictable by an artificial neural network with an accuracy of about 95 %.

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