A review of dynamic recrystallization phenomena in metallic materials

ASME boiler and pressure vessel codeの許容応力の改訂

Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel

https://hal.archives-ouvertes.fr/hal-00664802/document

Integrated modeling of friction stir welding of 6xxx series Al alloys: Process, microstructure and properties

https://estudogeral.sib.uc.pt/bitstream/10316/4183/1/file884093a59c4f48d68d7a9adae70ff2ff.pdf

Material parameters identification: Gradient-based, genetic and hybrid optimization algorithms

Effects of conventional heat treatment on wear resistance of AISI H13 tool steel

Using genetic algorithm and artificial neural network analyses to design an Al–Si casting alloy of minimum porosity

Prediction of mechanical properties of DP steels using neural network model

Effect of parameters such as current density, welding time, electrodes force and holding time after welding on tensile-shear strength of dissimilar joint were analyzed. Taguchi method was used to design the experiments and Minitab software was used to analyze the effect of parameters and obtain an optimum condition. The microstructure of the optimum condition was analyzed by Scanning Electron Microscope (SEM). The strength of the join was analyzed by tensile-shear test. Fracture mode was assessed by optical microscope. Results showed that current density, holding time after welding, welding time and electrodes force were the most effective parameters hierarchically. Optimum current density of 8 Kamp, holding time after welding of 40 cycle, 16 cycle welding time and 5 kN electrode force were defined as the optimum parameters to join the sheets. Weld nugget (WN) had a martensitic structure containing a relatively high strength and hardness. Its fracture mode was pullout failure.

Optimization of spot welding process parameters in dissimilar joint of dual phase steel DP600 and AISI 304 stainless steel to achieve the highest level of shear-tensile strength

In this study, effects of tungsten on wear resistance of high chromium white cast iron with and without tungsten in erosion–corrosion condition have been investigated. At the same time, the comparison between wear resistance of this grade of cast iron and low alloy steels with various contents of Cr which are used in industrial condition (in Sarcheshme Company, the greatest copper production company in the Middle East and with more than 4000 years historical cupper production background) was studied, while, copper concentrates have used for erosion particles. Results show that, because of higher hardness of matrix due to the tungsten, the wear resistance of high chromium cast iron increases. In addition to that, combine cutting and deformation wear mechanism and spalling mechanism were attributed in high chromium cast iron and low alloy steels, respectively. Subsequently, pitting mechanism in corrosion aspect was recognized because of inhomogeneity in chemical composition and sulfide inclusions content. Finally, the combine effects of erosion and corrosion (synergetic effect) were recognized in the high chromium white iron in industrial condition for the damaged samples.

S.H. Mousavi Anijdan

Papers

Effects of conventional heat treatment on wear resistance of AISI H13 tool steel

Using genetic algorithm and artificial neural network analyses to design an Al–Si casting alloy of minimum porosity

Prediction of mechanical properties of DP steels using neural network model

Optimization of spot welding process parameters in dissimilar joint of dual phase steel DP600 and AISI 304 stainless steel to achieve the highest level of shear-tensile strength

Effects of tungsten on erosion–corrosion behavior of high chromium white cast iron