Galaxy evolution studies require the measurement of the physical properties of galaxies at different redshifts. In this work, we build supervised machine learning models to predict the redshift and physical properties (gas-phase metallicity, stellar mass, and star formation rate) of star-forming galaxies from the broad-band and medium-band photometry covering optical to near-infrared wavelengths, and present an evaluation of the model performance. Using 55 magnitudes and colors as input features, the optimized model can predict the galaxy redshift with an accuracy of σ(Δz/1+z) = 0.008 for a redshift range of z < 0.4. The gas-phase metallicity [12 + log(O/H)], stellar mass [log(Mstar)], and star formation rate [log(SFR)] can be predicted with the accuracies of σNMAD = 0.081, 0.068, and 0.19 dex, respectively. When magnitude errors are included, the scatter in the predicted values increases, and the range of predicted values decreases, leading to biased predictions. Near-infrared magnitudes and colors (H, K, and H −K), along with optical colors in the blue wavelengths (m425–m450), are found to play important roles in the parameter prediction. Additionally, the number of input features is critical for ensuring good performance of the machine learning model. These results align with the underlying scaling relations between physical parameters for star-forming galaxies, demonstrating the potential of using medium-band surveys to study galaxy scaling relations with large sample of galaxies.

In this study, experiments and simulations were performed for fillet joint friction stir welding according to tool shape and welding conditions. Conventional butt friction stir welding has good weldability because heat is generated by friction with the bottom of the tool shoulder. However, in the case of fillet friction stir welding, the frictional heat is not sufficiently generated at the bottom of the tool shoulder due to the shape of the tool and the shape of the joint. Therefore, it is important to sufficiently generate frictional heat by slowing the welding speed as compared to butt welding. In this study, experiments and simulations were carried out on an aluminum battery housing made by friction stir welding an extruded material with a fillet joint. The temperature of the structure was measured using a thermocouple during welding, and the heat source was calculated through correlation analysis. Thermal elasto-plastic analysis of the structure was carried out using the calculated heat source and geometric boundary conditions. It is confirmed that the experimental results and the simulation results are well matched. Based on the results of the study, the deformation of the structure can be calculated through simulation even if the tool shape and welding process conditions change.