بهزاد صادقیان

Due to high mortality rate and prevalence of cancer, early detection is vital and important. Precancerous oral lesions have the potential to lead to SCC and therefore they should be carefully assessed. Brush cytology is a simple method, which obtains a sample from the epithelium. Computer analyses have an important role in interpretation of pathologic samples. An engineering team designed the software with neural networks, and the algorithm was trained with samples obtained from patients. In the second stage, brush cytology samples were collected from 20 patients with cancer and 20 healthy individuals. From each slide 50 digital images was captured with a camera under a microscope. The images were separately entered into the software program. The results were recorded as healthy and unhealthy. Statistical analyses were performed using Excel program. The software had 91 errors in a total of 2000 digital images. Comparison of the results provided by the software program and those of the scalpel biopsy of the patients with Fisher's exact test did not reveal any significant differences (p value = 0.004). Based on the results of this study, the designed software exhibited high specificity and sensitivity.

Friction stir welding is of the most applicable methods to join dissimilar metals. In this study, the thermal distribution during the joining of 304 stainless steel and 5083 aluminum alloy by friction stir welding method was simulated by the finite element method. Both, transient and stationary thermal solutions were used in the simulations and the two methods were compared correspondingly. To verify the model, two sheets of stainless steel and aluminum were prepared and the friction stir welding was applied. Additionally, by using thermocouples temperature, the history of points on the sheets was obtained during welding. Then, the simulation and the experimental results were compared to validate the model. Finally, an artificial neural network model was created and the effect of different input parameters on the maximum temperature under the tool was investigated.

Aluminum to stainless steel joints are broadly used in industries in order to reduce fuel consumption. While fusion welding is not a suitable method to join these metals. solid state welding, like friction welding (FW), is an effective way to this process. However, risk of intermetallic compounds (IMCs) formation is probable in these welds. In previews investigations formation of FeAl3, Fe2Al5 and Fe4Al13 is reported. In this study, effect of different parameters on generated heat and temperature distribution that lead to formation of these compounds in a FW of aluminum alloy to stainless steel is investigated using Finite Element Method (FEM). Additionally, a mathematical modeling of the parameters is performed using Artificial Neural Network (ANN) and the optimum level of the parameters has been found.

In this research, artificial neural network (ANN) and genetic algorithm (GA) were used in order to produce and develop the NiAl intermetallic coating with the best wear behavior and the most value of hardness. The effect of variations of current, voltage and gas flow on the hardness and wear resistance were optimized by ANN and GA. In the following, the optimum values of current, voltage and gas flow were obtained 90(A), 10(v) and 9 (Lit/min), respectively. Then, the wear behavior in the environment temperature and high temperature for optimized NiAl compound was compared with two other experimental samples.

Today, steel to aluminum joints are used to facilitate transportation and fuel consumption. These joints are applied from nuclear, aerospace and naval to automobile and kitchen industries. According to previous studies fusion welding processes are not suitable methods for these joints, solid-state welding, especially friction stir welding, is a proper way to use for these joints. However, using this method for these two metals needs adequate prediction of temperature distribution and material flow to obtain enhanced joints. In this study, a finite element method is used to predict the temperature distribution. In addition, a computational fluid dynamics solution is coupled with the thermal solution. Therefore, the flow rate, strain rate and dynamic viscosity is obtained. Also, the joint morphology is predicted using the Level Set method. It is shown the material flow depends on flow rate, strain rate and dynamic viscosity and is intensively function of rotational speed. Additionally, offset to the aluminum side improves the morphology of the stir zone.