ABSTRACT
The temperature distributions, microstructure, and mechanical properties of tungsten composite with aluminum alloy friction-welded joints are presented in this paper. The effects of welding parameters on flash diameter, shortening, joint efficiency, microhardness, and microstructure were studied. Empirical temperature models for heating and cooling phases are proposed in this study. The predicted maximum temperatures at the periphery and in the axis of aluminum specimens were close to 550 °C and 480 °C at the interface, respectively. Moreover, the peak temperature in the weld zone was studied analytically. A maximum tensile strength of 234 MPa was reached for the following welding parameters: friction time of 3.5 s and friction force of 12.5 kN. The efficiency of the welded samples decreased after reaching the maximum value, with an increase of friction time and force. Maximum hardness at the interface and the half-radius reached 100 HV and 80 HV in the aluminum alloy joints, respectively. Dynamic recrystallisation areas on the aluminum alloy side were observed. Transmission electron microscopy observations of the microstructure in the aluminum alloy revealed the presence of a high dislocation density compared to the parent material.
ABSTRACT
In this study, an efficient optimisation method by combining response surface methodology (RSM) and genetic algorithm (GA) is introduced to find the optimal topology of artificial neural networks (ANNs) for predicting colour changes in rehydrated apple cubes. A multi-layered feed-forward backpropagation ANN model of algorithms was developed to correlate one output (colour change) to four input variables (drying air temperature, drying air velocity, temperature of distilled water and rehydration time). A predictive model for ANN topology in terms of the best mean squared error (MSE) performance on validation samples was created using RSM. RSM model was integrated with an effective GA to find the optimum topology of ANN. The optimum ANN had minimum MSE when the number of hidden neurons, learning rate, momentum constant, number of epochs and number of training runs were 13, 0.33, 0.89, 3869 and 3, respectively. MSE of optimal ANN topology on validation samples was 0.0072095. It turned out that the optimal ANN topology can be considered as more precise for predicting colour change in the rehydrated apple cubes. Mean absolute error and regression coefficient (R) of the optimal ANN topology were determined as 0.0259 and 0.96475 for training, 0.0399 and 0.95243 for testing and 0.0264 and 0.95151 for validation data sets. The results of the testing model on new samples showed excellent agreement between the actual and predicted data with coefficient of determination R2 = 0.97.
