In the process of welding zinc-coated steel, zinc vapor causes serious porosity defects. The porosity defect is an important indicator of the quality of welds and degrades the durability and productivity of the weld. Therefore, this study proposes a deep neural network (DNN)-based non-destructive testing method that can detect and predict porosity defects in real-time, based on welding voltage signal, without requiring additional device in gas metal arc welding (GMAW) process. To this end, a galvannealed hot-rolled high-strength steel sheet applied to automotive parts was used to measure process signals in real-time. Then, feature variables were extracted through preprocessing, and correlation between the feature variables and weld porosity was analyzed. The proposed DNN based framework outperformed the artificial neural network (ANN) model by 15% or more. Finally, an experiment was conducted by using the developed porosity detection and prediction system to evaluate its field application.

Ratcheting failure of materials and structures subjected to low cycle fatigue in the presence of significant mean stress is of great interest to researchers. In this experimental and numerical study, the response of 316L stainless steel samples was observed in symmetric strain control uniaxial test followed by post-stabilized monotonic test, uniaxial and biaxial ratcheting tests, in order to determine the Chaboche model parameters and to evaluate ratcheting prediction using finite element analysis. The critical elastic limit was initially obtained from incremental uniaxial cyclic tests. The Chaboche parameters were subsequently extracted from experimental hysteresis and post-stabilized monotonic stress plastic-strain curves using two optimization technics, namely, the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The two optimization methods were compared for efficiency, in terms of time and accuracy. The PSO method presented higher efficient results and was subsequently used to derive the parameters from hysteresis and post-stabilized monotonic curves. Different values (by definition) of elastic limit were also used. The Finite Element commercial software ANSYS was utilized with the Chaboche model to predict the uniaxial and biaxial ratcheting behavior of 316L stainless steel pipe. The comparison between experimental and the numerical simulation demonstrates that adopting post-stabilized monotonic curve rather than hysteresis curve and with accurate elastic limit obtained from incremental loading test improves ratcheting prediction significantly.