Prediction of angular distortion due GMAW process of thin-sheets Hardox 450® steel by numerical model and artificial neural network

2021 ◽  
Vol 68 ◽  
pp. 1202-1213
Author(s):  
Cristian Rubio-Ramirez ◽  
Daniela F. Giarollo ◽  
José E. Mazzaferro ◽  
Cíntia Petry Mazzaferro
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Numerical Model ◽  
Angular Distortion ◽  
Thin Sheets ◽  
Artificial Neural

scholarly journals Spatio-temporal prediction of suspended sediment concentration in the coastal zone using an artificial neural network and a numerical model

2012 ◽  
Vol 14 (3) ◽  
pp. 574-584 ◽  
Author(s):  
B. Bhattacharya ◽  
T. van Kessel ◽  
D. P. Solomatine
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Numerical Model ◽  
Series Data ◽  
Ann Model ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Temporal Prediction ◽  
Artificial Neural

A problem of predicting suspended particulate matter (SPM) concentration on the basis of wind and wave measurements and estimates of bed shear stress done by a numerical model is considered. Data at a location at 10 km offshore from Noordwijk in the Dutch coastal area is used. The time series data have been filtered with a low pass filter to remove short-term fluctuations due to noise and tides and the resulting time series have been used to build an artificial neural network (ANN) model. The accuracy of the ANN model during both storm and calm periods was found to be high. The possibilities to apply the trained ANN model at other locations, where the model is assisted by the correctors based on the ratio of long-term average SPM values for the considered location to that for Noordwijk (for which the model was trained), have been investigated. These experiments demonstrated that the ANN model's accuracy at the other locations was acceptable, which shows the potential of the considered approach.

Performance Prediction of Pulse Tube Refrigerator Using Artificial Neural Network

2014 ◽  
Vol 984-985 ◽  
pp. 1147-1149
Author(s):  
Pankaj Kumar ◽  
Sachindra Ku Rout ◽  
Ajay Ku Gupta ◽  
Rajit Ku Sahoo ◽  
Sunil Ku Sarangi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Numerical Model ◽  
Process Model ◽  
Fluid Dynamic ◽  
Coefficient Of Performance ◽  
Pulse Tube ◽  
Time Saving ◽  
Pulse Tube Refrigerator ◽  
Artificial Neural

The present study proposes a numerical model to analyze the effect of four dimensional parameters on performance characteristics such as Coefficient of performance (COP), of the Inertance-Type Pulse Tube Refrigerator (ITPTR). The numerical model is validated by comparing with previously published results. The detail analysis of cool down behaviour, heat transfer at the cold end and the pressure variation inside the whole system has been carried out by using the most powerful computational fluid dynamic software package ANSYS FLUENT 13. The operating frequency for all the studied cases is (34 Hz). In fact, to get an optimum parameter experimentally is a very tedious for iterance pulse tube refrigerator job, so that the CFD approach gives a better solution. Finally, an artificial neural network (ANN) based process model is proposed to establish relation between input parameters and the responses. The model provides an inexpensive and time saving substitute to study the performance of ITPTR. The model can be used for selecting ideal process states to improve ITPTR performance.

An Application of Neural Network Solutions to Modeling of Diode Laser Assisted Forming Process of AA6082 Thin Sheets

2007 ◽  
Vol 344 ◽  
pp. 325-332 ◽  
Author(s):  
S. Guarino ◽  
Nadia Ucciardello ◽  
Vincenzo Tagliaferri
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Diode Laser ◽  
Experimental Tests ◽  
Laser Forming ◽  
Thin Sheets ◽  
Forming Process ◽  
Neural Network Approach ◽  
Artificial Neural

In this paper a neural network approach is used to model the diode laser assisted forming process. In particular thin sheets of Aluminum alloy AA 6082 were bended in the elastic range and then treated with a diode laser with the aim to reduce the spring back phenomenon. Experimental tests were performed to study the influence of the process parameters such as laser power, laser speed and starting elastic deformation on the evolution of forming process. In particular the heating effects on the elastic properties of the material was studied. A statistical approach is used to define the experimental plan and discuss the experimental results. Interesting trend of the effects of the diode laser on the forming process were found. Subsequently in order to predict the residual inflexion, during the laser forming, a multilayer feedforward artificial neural network has been implemented. A sensitivity analysis on the artificial neural network model is used to show the significance of all the input data employed. As a result of sensitivity analysis, a check between experimental and calculated trends for each investigated variables was performed, which revealed an appreciable fit between data displayed.

Monitoring and Fault Identification in Aeronautical Structures Using an ARTMAP-Fuzzy-Wavelet Artificial Neural Network

2014 ◽  
Vol 1025-1026 ◽  
pp. 1107-1112 ◽  
Author(s):  
Fernando Parra dos Anjos Lima ◽  
Simone Silva Frutuoso de Souza ◽  
Fábio Roberto Chavarette ◽  
Mara Lúcia Martins Lopes ◽  
Antonio Eduardo Turra ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Numerical Model ◽  
Fault Identification ◽  
Structural Failure ◽  
Aircraft Structures ◽  
Artificial Neural ◽  
Aluminum Beam

This paper presents a methodology to perform the monitoring and identification of flaws in aircraft structures using an ARTMAP-Fuzzy-Wavelet artificial neural network. This technique is used in the detection and characterization of structural failure. The main application of this method is to assist in the inspection of aircraft structures in order to identify and characterize failures as well as decision-making, in order to avoid accidents or air crashes. In order to evaluate this method, the modeling and simulation of signals from a numerical model of an aluminum beam was performed. The results obtained by the method are satisfactory compared to literature.

Sign in / Sign up

Export Citation Format

Share Document