Materials Science Forum Vol. 1032

Abstract: A crack is found on the surface of stainless steel casting support. The crack was analyzed by macro and micro observation of fracture surface, Energy Dispersive Spectrometer (EDS) analysis, chemical analysis, microstructure examination and hardness test. Moreover, the stress distribution was simulated by finite element analysis. The results show that the crack model of the support is stress corrosion. The stress at the crack location is the largest. Three main factors of stress corrosion were the crack of paint layer, marine atmospheric environment and the pretightening force for the support. Because the service environment cannot be changed, therefore, surface paint protective effect and reduction of preload can be improved to avoid stress corrosion cracking again on the support.

Abstract: Two states of aluminum alloy material 7B04 T651 and 7B04 T74 using C-ring specimen were selected to carry out stress corrosion simulation test with different stress levels, corrosion concentrations and time, and the fracture morphology of the crack was observed and analyzed by optical microscope and scanning electron microscope (SEM). The results showed that 7B04-T74 alloy was insensitive to stress corrosion and was not prone to stress corrosion cracking under constant tensile stress lower than 432MPa; The stress corrosion cracking time of 7B04 T651 alloy under three different concentrations has no significant difference, and the stress corrosion cracking occurs within 7 days under the stress of 180MPa-432MPa. The time of stress corrosion cracking increased with the decrease of stress. Stress corrosion cracking (SCC) was very sensitive to Cl element, and it was also easy to produce SCC when the concentration of corrosive medium was low, the threshold value of corrosion cracking was about 108 MPa. SEM and EDS analysis showed that the fracture surface was intergranular, mud-like corrosion products, and secondary cracks. At the same time, the matrix grain boundaries were weakened by Cl element.

Abstract: In this experiment, Al-Cu-Mg-Ag alloy was used as material and solution temperature was used as variable to investigate its effect on the corrosion properties of the alloy and Hardness test, metallographic observation, electrochemical test, intergranular corrosion and exfoliation corrosion test were carried out on three groups of samples. The results show that the intergranular corrosion resistance of the alloy decreases with the increase of solution treatment temperature, and the sample treated at 505 °C has the best performance. This is mainly because grain boundary structure plays a role in increasing PFZ and expanding corrosion channels. The exfoliation corrosion resistance of Al-Cu-Mg-Ag alloy increases first and then decreases, and the sample treated at 515 °C has the best performance. This is due to the dual effects of grain boundary structure and grain morphology. On the one hand, the solution treatment temperature increases, which widens the precipitation-free zone and reduces the electrochemical corrosion resistance of the alloy. On the other hand, the increase of recrystallized grains decreases the cohesion of corrosion products and enhances the electrochemical corrosion resistance of the alloy.

Abstract: Controlling the corrosion rate of metal materials is one of the key issues in circulating cooling water treatment. In recent years, the treatment of circulating cooling water by microorganisms has become a research hotspot. Compared with the traditional chemical treatment, microbial treatment is an environmentally friendly technology. In this paper, the effects of ammonia nitrogen concentration, microbial dosage and aeration intensity on copper corrosion rate were studied. In order to analyze the experimental data more comprehensively, a full factor experimental design was used to investigate the effects of ammonia nitrogen concentration, microbial dosage and aeration intensity on copper corrosion. The corrosion rate of copper was less than the national standard (< 0.005 mm / a), in which ammonia nitrogen concentration and aeration intensity were significant factors (P < 0.05), and the interaction between ammonia nitrogen concentration and aeration intensity was also significant (P < 0.05), After optimization, the regression rate of the model increased from 85.02% to 92.41%.