Additive friction stir deposition (AFSD) provides strong flexibility and better performance in component design, which is controlled by the process parameters. It is an essential and difficult task to tune those parameters. The recent exploration of machine learning (ML) exhibits great potential to obtain a suitable balance between productivity and set parameters. In this study, ML techniques, including support vector machine (SVM), random forest (RF) and artificial neural network (ANN), are applied to predict the mechanical properties of the AFSD‐based AA6061 deposition. Expect for the stable parameters (temperature, force and torque) in situ monitored by the self‐developed process‐aware kit during the AFSD process and the other factors (rotation speed, traverse speed, feed rate and layer thickness) are also set as input variables. The output variables are microhardness and ultimate tensile strength (UTS). Prediction results show that the ANN model performs the best prediction accuracy with the highest R² (0.9998) and the lowest mean absolute error (MAE, 0.0050) and root mean square error (RMSE, 0.0063). Furthermore, analysis suggests that the feed rate (24.8%/24.1%) and layer thickness (25.6%/26.6%) indicate a higher contribution that affects the mechanical properties.

In this study, the effects of friction stir processing (FSP) parameters on the microstructure and hardness of cast Al-Si-Fe-Mg alloy were investigated. Orthogonal arrays were applied in the design of the experiments. The selected parameters for the experiments included rotation speed, transverse speed, penetration depth, and tilt angle. The microstructure and hardness of the FSPed Al-Si-Fe-Mg were studied using optical and scanning electron microscopy, and microhardness testing, respectively. The quadratic model was proposed to fit the experimental data of hardness. Signal-to-noise ratio (S/N) analysis showed the maximum hardness achieved when rotation speed, transverse speed, penetration depth, and tilt angle were chosen as 1600 rpm, 400 mm/min, 0.1 mm, and 1.5°, respectively. Taguchi’s analysis of variance (ANOVA) was used to determine the significant FSP parameters on hardness, which revealed that rotation speed was the most dominant processing parameter, followed by transverse speed, tilt angle, and penetration depth. Moreover, a quadratic polynomial model was developed to predict and optimize the combination of the parameters, enabling superior mechanical properties. Subsequently, the verification of the microstructure was conducted, demonstrating good agreement between the experimental observation of the microstructure and estimated outcomes.

The microbiologically influenced corrosion of 201 stainless steel by Shewanella algae was investigated via modulating the concentration of fumarate (electron acceptor) in the medium and constructing mutant strains induced by ΔOmcA. The ICP-MS and electrochemical tests showed that the presence of S. algae enhanced the degradation of the passive film; the lack of an electron acceptor further aggravated the effect and mainly affected the early stage of MIC. The electrochemical tests and atomic force microscopy characterization revealed that the ability of ΔOmcA to transfer electrons to the passive film was significantly reduced in the absence of the c-type cytochrome OmcA related to EET progress, leading to the lower corrosion rate of the steel.