Today, sintering considers one of the significant processes that can be used in powder technology to produce a new solid product from powders using thermal energy. Many parameters can be successfully controlled by this process such as temperature, Particle size, process time, structure geometry, powder density, and powder composition. Study and analysis of the behavior of powder during the sintering process was carried out using finite element methods. The simulation provides two styles of discrete method and Qusi-static method. This research contributes to two types of processes in order to simulate the copper powder during the sintering process and to determine the variation by using contact and shrinkage ratios of powder behaviors. Finally, a comparison between the two styles of discrete element method explains how the selected parameters were impacted on the sintering process.

In this presented work, an Artificial Neural Network (ANN) connected with the backpropagation method was employed to predict the strength of joining materials that were carried out by using an ultrasonic spot welding process. The models created in this study were investigated, and their process parameters were analyzed. These parameters were classified and set as input variables like applying pressure, time of duration weld and trigger of vibrating amplitude. In contrast, the weld strength of joining dissimilar materials (Al-Cu) is set as output parameters. The identification from the process parameters is obtained using several experiments and finite element analyses based on prediction. The results of actual and numerical are accurate and reliable; however, their complexity has a significant effect due to being sensitivity to the condition variation of welding processes. Therefore, an efficient technique like an artificial neural network coupled with the backpropagation method is required to use the experiments as input data in the simulation of the ultrasonic welding process, finding the adequacy of the modeling process in the prediction of weld strength and to confirm the performance of using mathematical methods. The results of the selecting non-linear models show a noticeable potency when using ANN with a backpropagation method in providing high accuracy compared with other results obtained by conventional models.