Sensitivity Analysis of the Artificial Neural Network Outputs in Friction Stir Lap Joining of Aluminum to Brass

Abstract

Al-Mg and CuZn34 alloys were lap joined using friction stir welding while the aluminum alloy sheet was placed on the CuZn34. In addition, the mechanical properties of each sample were characterized using shear tests. Scanning electron microscopy (SEM) and X-ray diffraction analysis were used to probe chemical compositions. An artificial neural network model was developed to simulate the correlation between the Friction Stir Lap Welding (FSLW) parameters and mechanical properties. Subsequently, a sensitivity analysis was performed to investigate the effect of each input parameter on the output in terms of magnitude and direction. Four methods, namely, the “PaD” method, the “Weights” method, the “Profile” method, and the “backward stepwise” method, which can give the relative contribution and/or the contribution profile of the input factors, were compared. The PaD method, giving the most complete results, was found to be the most useful, followed by the Profile method that gave the contribution profile of the input variables.

How Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept Study

“…Sensitivity was computed using the partial derivative (PaD) method. 27,28 This method starts by computing, analytically, the PaD of the prediction with respect to each input …”

The identification of relationships between selected honey parameters by extracting the contribution of independent variables in a neural network model

“…In the prior literature, one can find many reports where the results of variable contribution calculated using different methods are of the significant differentiation. Shojaeefard et al [47] have published the report where there was a similarity between results obtained using the PaD method and the profile method, as well as the classical stepwise method, but the results obtained using the connection weights method were significantly different. Gevrey et al [49] employed several methods of testing the variables contribution to study a brown trout reproduction phenomenon.…”

“…Combining (5) and (6): The relative contribution of each input variable on a specific output can be calculated as follows [47]:…”

Modeling and Optimization of β-Cyclodextrin Production by Bacillus licheniformis using Artificial Neural Network and Genetic Algorithm

“…In a study, checking the responsiveness of the suggested model to change in any model parameter(s) has (14). Evaluation of the suggested model quality (robustness of the model parameter) is carried out by finding out the extent of contribution of the selected functions in describing relationship between the input, the constructed (hidden), and the output variables (14).…”

“…Evaluation of the suggested model quality (robustness of the model parameter) is carried out by finding out the extent of contribution of the selected functions in describing relationship between the input, the constructed (hidden), and the output variables (14). In the present study and according to the results of sensitivity analysis (weight method / NeuralPower software), the quantitative extent of contribution of each input variables on the amount of β-CD produced, was presented in Figure 8.…”