Optimization of Metal Inert-Gas Welding Process for 5052 Aluminum Alloy by Artificial Neural Network

2021 ◽  
Vol 62 (5) ◽  
pp. 568-579
Author(s):  
Jiong Pu ◽  
Yanhong Wei ◽  
Shangzhi Xiang ◽  
Wenmin Ou ◽  
Renpei Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Welding Process ◽  
Inert Gas ◽  
Metal Inert Gas Welding ◽  
Artificial Neural

scholarly journals Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1494
Author(s):  
Ran Li ◽  
Manshu Dong ◽  
Hongming Gao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Welding Speed ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Training Dataset ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Bead Width ◽  
Artificial Neural

Bead size and shape are important considerations for industry design and quality detection. It is hard to deduce an appropriate mathematical model for predicting the bead geometry in a continually changing welding process due to the complex interrelationship between different welding parameters and the actual bead. In this paper, an artificial neural network model for predicting the bead geometry with changing welding speed was developed. The experiment was performed by a welding robot in gas metal arc welding process. The welding speed was stochastically changed during the welding process. By transient response tests, it was indicated that the changing welding speed had a spatial influence on bead geometry, which ranged from 10 mm backward to 22 mm forward with certain welding parameters. For this study, the input parameters of model were the spatial welding speed sequence, and the output parameters were bead width and reinforcement. The bead geometry was recognized by polynomial fitting of the profile coordinates, as measured by a structured laser light sensor. The results showed that the model with the structure of 33-6-2 had achieved high accuracy in both the training dataset and test dataset, which were 99% and 96%, respectively.

Predicting Fatigue Life of Pre-Corroded LC4 Aluminum Alloy by Artificial Neural Network

2010 ◽  
Vol 118-120 ◽  
pp. 221-225 ◽  
Author(s):  
Cheng Long Xu ◽  
Sheng Li Lv ◽  
Zhen Guo Wang ◽  
Wei Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Fatigue Life ◽  
Network Architecture ◽  
Fatigue Tests ◽  
Data Sets ◽  
Absolute Relative Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this work was to predict the fatigue life of pre-corroded LC4 aluminum alloy by applying artificial neural network (ANN). Specimens were exposed to the same corrosive environment for 24h, 48h, and 72h. Fatigue tests were conducted under different stress levels. The existing experimental data sets were used for training and testing the construction of proposed network. A suitable network architecture (2-15-1) was proposed with good performance in this study. For evaluating the method efficiency, the experimental results have been compared to values predicted by ANN. The maximum absolute relative error for predicted values does not exceed 5%. Therefore it can be concluded that using neural networks to predict the fatigue life of LC4 is feasible and reliable.

Artificial Neural Network for Predicting the Flow Behaviors of Hot Compressed 2124-T851 Aluminum Alloy

2010 ◽  
Vol 146-147 ◽  
pp. 720-723
Author(s):  
Yong Cheng Lin ◽  
Xiao Min Chen ◽  
Yu Chi Xia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Flow Stress ◽  
Strain Rate ◽  
Learning Algorithm ◽  
Ann Model ◽  
Wide Range ◽  
Feed Forward Network ◽  
Artificial Neural

The compressive deformation experiments of 2124-T851 aluminum alloy were carried out over a wide range of temperature and strain rate. An artificial neural network (ANN) model is developed for the analysis and simulation of the correlation between the flow behaviors of hot compressed 2124-T851 aluminum alloy and working conditions. The input parameters of the model consist of strain rate, forming temperature and deformation degree whereas flow stress is the output. A three layer feed-forward network with 15 neurons in a single hidden layer and back propagation (BP) learning algorithm has been employed. Good performance of the ANN model is achieved. The predicted results are consistent with what is expected from fundamental theory of hot compression deformation, which indicates that the excellent capability of the developed ANN model to predict the flow stress level, the strain hardening and flow softening stages is well evidenced.

scholarly journals Flow Stress of 6061 Aluminum Alloy at Typical Temperatures during Friction Stir Welding based on Hot Compression Tests

Metals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 804
Author(s):  
Sansan Ding ◽  
Qingyu Shi ◽  
Gaoqiang Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Flow Stress ◽  
Hot Compression ◽  
Compression Tests ◽  
6061 Aluminum Alloy ◽  
Friction Stir ◽  
Artificial Neural

The purpose of this paper is to report quantitative data and models for the flow stress for the computer simulation of friction stir welding (FSW). In this paper, the flow stresses of the commercial 6061 aluminum alloy at the typical temperatures in FSW are investigated quantitatively by using hot compression tests. The typical temperatures during FSW are determined by reviewing the literature data. The measured data of flow stress, strain rate and temperature during hot compression tests are fitted to a Sellars–Tegart equation. An artificial neural network is trained to implement an accurate model for predicting the flow stress as a function of temperature and strain rate. Two models, i.e., the Sellars–Tegart equation and artificial neural network, for predicting the flow stress are compared. It is found that the root-mean-squared error (RMSE) between the measured and the predicted values are found to be 3.43 MPa for the model based on the Sellars–Tegart equation and 1.68 MPa for the model based on an artificial neural network. It is indicated that the artificial neural network has better flexibility than the Sellars–Tegart equation in predicting the flow stress at typical temperatures during FSW.

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