Modeling of Weld Lap-Shear Strength for Laser Transmission Welding of Thermoplastic Using Artificial Neural Network

2012 ◽  
Vol 445 ◽  
pp. 454-459
Author(s):  
M.R. Nakhaei ◽  
N.B. Mostafa Arab ◽  
F. Kordestani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shear Strength ◽  
Laser Transmission Welding ◽  
Lap Shear Strength ◽  
Lap Shear ◽  
Artificial Neural

Modeling of Weld Lap-Shear Strength for Laser Transmission Welding of Thermoplastic Using Artificial Neural Network

2012 ◽  
Vol 445 ◽  
pp. 454-459 ◽  
Author(s):  
M.R. Nakhaei ◽  
N.B. Mostafa Arab ◽  
F. Kordestani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shear Strength ◽  
Laser Welding ◽  
Process Parameters ◽  
Welding Speed ◽  
Laser Power ◽  
Laser Transmission Welding ◽  
Lap Shear Strength ◽  
Lap Shear

Laser welding of plastic materials has a wide range of applications in the packaging, medical, electronics and automobile industries provided it can predict high quality welds compared with other joining methods. Laser welding process parameters can affect the quality of welds. In this paper, Artificial Neural Network (ANN) is used to model the effects of laser power, welding speed, clamp pressure and stand-off distance on weld lap-shear strength in laser transmission welding (LTW) of acrylic (polymathy methacrylate). A set of experimental data on diode laser weld lap-shear strengths was used to train and test the ANN from which the neurons relations were gradually extracted to develop a model. The developed ANN model can be used for the analysis and prediction of the complex relationships between the above mentioned process parameters and weld lap-shear strength. The results indicated that increase in laser power and clamp pressure increases the weld lap-shear strength whereas welding speed and stand off distance had a decreasing affect on shear strength at high value.

scholarly journals Application of Residual Shear Strength Predicted by Artificial Neural Network Model for Evaluating Liquefaction-Induced Lateral Spreading

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yanxin Yang ◽  
Bai Yang ◽  
Chunhui Su ◽  
Jianlin Ma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shear Strength ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Lateral Spreading ◽  
Case Histories ◽  
Residual Shear Strength ◽  
Artificial Neural

The residual shear strength of liquefied soil is critical to estimating the displacement of lateral spreading. In the paper, an Artificial Neural Network model was trained to predict the residual shear strength ratio based on the case histories of lateral spreading. High-quality case histories were analyzed with Newmark sliding block method. The Artificial Neural Network model was used to predict the residual shear strength of liquefied soil, and the post-liquefaction yield acceleration corresponding with the residual shear strength was obtained by conducting limit equilibrium analysis. Comparing the predicted residual shear strength ratios to the recorded values for different case histories, the correlation coefficient, R, was 0.92 and the mean squared error (MSE) was 0.001 for the predictions by the Artificial Neural Network model. Comparison between the predicted and reported lateral spreading for each high-quality case history was made. The results showed that the probability of the lateral spreading calculated with the Newmark sliding block method using the residual shear strength was 98% if a lateral spreading ratio of 2.0 was expected and a truncated distribution was used. An exponential relationship was proposed to correlate the residual shear strength ratio to the equivalent clean sand corrected SPT blow count of the liquefied soil.

Influence of Process Parameters on Weldment Gap, Depth of Fusion and Lap-Shear Strength of PA66 with Laser Transmission Welding

2013 ◽  
Vol 50 (12) ◽  
pp. 121402
Author(s):  
严长 Yan Zhang ◽  
李品 Li Pin ◽  
刘会霞 Liu Huixia ◽  
蔡野 Cai Ye ◽  
陈浩 Chen Hao ◽  
...  
Keyword(s):  
Shear Strength ◽  
Process Parameters ◽  
Influence Of Process Parameters ◽  
Laser Transmission Welding ◽  
Lap Shear Strength ◽  
Lap Shear

scholarly journals Predicting Shear Strength in FRP-Reinforced Concrete Beams Using Bat Algorithm-Based Artificial Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Mohammad Nikoo ◽  
Babak Aminnejad ◽  
Alireza Lork
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shear Strength ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
Bat Algorithm ◽  
Pso Algorithm ◽  
Reinforced Concrete Beams ◽  
Feed Forward Network ◽  
Artificial Neural

In this article, 140 samples with different characteristics were collected from the literature. The Feed Forward network is used in this research. The parameters f’c (MPa), ρf (%), Ef (GPa), a/d, bw (mm), d (mm), and VMA are selected as inputs to determine the shear strength in FRP-reinforced concrete beams. The structure of the artificial neural network (ANN) is also optimized using the bat algorithm. ANN is also compared to the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. Finally, Nehdi et al.’s model, ACI-440, and BISE-99 equations were used to evaluate the models’ accuracy. The results confirm that the bat algorithm-optimized ANN is more capable, flexible, and provides superior precision than the other three models in determining the shear strength of the FRP-reinforced concrete beams.

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