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This work studies the influence of gamma prime and eta phases, resulting from precipitation hardening, on the corrosion behaviour of A286 superalloy by using electrochemical potentiokinetic reactivation (EPR) and double loop electrochemical potentiokinetic reactivation (DLEPR). The general trend is that Ir, Qr, Ir/Ia and Qr/Qa increase with aging temperature and, for a given aging temperature, with aging time, which is consistent with the fact that gamma prime precipitate may cause discontinuities in the passive layer. Ir, Qr, Ir/Ia and Qr/Qa reach a peak, which alters the general trend, for both aging temperatures (670 and 720 °C), at the aging time from which there is evidence of the presence of eta phase at grain boundary (GB). Gamma prime and eta precipitates can cause the passive layer that forms on them to become more unstable and, consequently, cause the current density through the passive layer and the susceptibility to localized corrosion attack to increase. Keywords: A286 superalloy, Precipitation hardening, EPR, DLEPR, SEM

This work aims to assess the effect of welding on the stress corrosion cracking (SCC) behaviour of prior cold worked AISI 316L stainless steel from ultimate tensile strength (UTS) and time to failure (TF) obtained in slow strain rate tests (SSRTs), which are conducted in corrosive environment and in non-corrosive environment (air). The UTS of cold worked and welded specimens does not show significant variations with prior cold work (CW). However, the TF of cold worked and welded specimens depends on several phenomena that occur in the heat affected zone (HAZ), such as sensitization, recrystallization, recrystallized grain growth or thermal transformation of strain-induced martensite. Additionally, a metallographic study of SSRT tested specimens is performed with the aim of assessing the fracture mode, which proves to be ductile. This work shows that the combined effect of prior CW and welding does not give rise to SCC because the degree of sensitization (DOS) induced in the HAZ is not sufficient to direct the crack growth.

The quality level of a resistance spot welding (RSW) joint of 304 austenitic stainless steel (ASS) is estimated from its tensile shear load bearing capacity (TSLBC). The quality levels are set by ultrasonic nondestructive testing. The objective of the present work is to develop a tool capable of reliably predicting the TSLBC (and consequently the quality level) of RSW joints from three welding parameters: (1) welding time (WT); (2) welding current (WC); (3) electrode force (EF). Firstly, a linear regression model is attempted but the residuals analysis reveals nonlinear behaviour. An artificial neural network (ANN) is proposed because the ANNs are capable of mapping nonlinear systems. The inputs are 3-component vectors, a component for each of the aforementioned welding parameters. The training of the ANN uses supervised learning mechanism. Therefore each input must come with its respective desired output (target). This target is the TSLBC of the RSW joint obtained with the respective input. The number of neurons in the hidden layers is selected considering the overfitting phenomenon: the number of neurons in the hidden layers that minimizes the validation mean square error (MSE) is 4. With the selected ANN, 3–4–4–1, the aim of the present study is achieved because this ANN produces good results in prediction from inputs nonused in the training.