Hamid Gharaei, Mahdi Salehi, Mehran Nahvi, Behzad Sadeghian,
Volume 2, Issue 2 (11-2016)
Abstract
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.
M. Niazi, A. Afsari, Seyed A. Behgozin, M. R. Nazemosadat,
Volume 9, Issue 1 (5-2023)
Abstract
Optimization of Stir Friction Welding parameters such as linear and rotational speed of the tool can be effective to a large extent in improving welding properties. In this research, welding of two sheets of Aluminum of Al-7075 and Al-6061 were validated based on theoretical relations and numerical simulation. The simulation of the contact characteristics of the workpieces with the tool was done using the contact algorithms available in the Ansys software. From the FEM, rotational and linear speed and diameter of the tool were selected as design variables, and multi object optimization was carried out with genetic algorithm and RSM to reach the lowest tool temperature and residual stress.The parametric analysis of FSW of the threaded and non-threaded tool pins showed that the generated heat has proportional and inverse relation with rotation and linear speed of tool respectively. Tool with a diameter of 20 mm showed minimum residual stress in the workpiece. By increasing welding speed, the temperature curves become more compact and the effect of thread on heat generation was more evident in all cases at lower heat input.
Mostafa Talebipour, Reza Shoja Razavi, Reza Mozafarinia, Masoud Barekat, Ali Khorram,
Volume 9, Issue 2 (8-2026)
Abstract
Selective laser melting (SLM) has been considered as a method for manufacturing bulk and complex industrial parts. Considering that structural defects are generally caused by process parameters, optimal evaluation of parameter selection to minimize localized defects has been of interest. Therefore, a model was presented to predict the optimal single-pass geometric characteristics based on the main process parameters, namely laser power and scanning speed, to prevent defects in single-pass Inconel 738LC on Inconel 738 casting substrate. An optimal process map was obtained based on the use of linear regression method together with genetic optimization algorithm with optimal combination parameters (PαVβ). Finally, based on the geometric characteristics of the single-pass, an optimal region was identified on the process map and for validation, the microstructure of the single-pass was investigated in three regions: the interface, the middle part and the top part of each pass. The results showed that at a power of 325 W and a laser scanning speed of 800 mm/s, there was a decrease in the G/R ratio and the microstructure progressed towards columnar dendrites and equiaxed dendrites.
