The material of Ti–6Al–4V has been widely applied in various industries, such as automobile, aerospace, and medical due to its high specific strength, superior thermal stability and strong corrosion resistance. In the recent decades, selective laser melting (SLM) has become an attractive method to fabricate Ti–6Al–4V parts, thanks to its significant advantages in low material consumption, the high degree of freedom in design, low carbon footprint, etc. Predictability of SLM material fatigue properties is especially important for the safety-critical structures under dynamic load cases. The present research is aimed at evaluating the low cycle fatigue (LCF) performance of SLM Ti–6Al–4V under high loading states. LCF tests were performed for as-built and annealed SLM Ti–6Al–4V. Comparison between LCF properties of SLM Ti–6Al–4V and the wrought Ti–6Al–4V was also made. It was found that as-built SLM Ti–6Al–4V demonstrated a comparable LCF performance with the wrought material. The LCF life of as-built SLM Ti–6Al–4V was longer than that of wrought Ti–6Al–4V at lower strain amplitudes. However, the wrought Ti–6Al–4V had better LCF performance at higher strain amplitudes. The results revealed that the porosity in the as-built SLM material exerted much more impact on the degradation of the material at high strain amplitudes. Annealing deteriorated the LCF performance of SLM Ti–6Al–4V material due to the formation of coarser grains. The cyclic Ramberg–Osgood and the Basquin–Coffin–Manson models were fitted to depict the cyclic stress–strain and the strain–life curves for the SLM Ti–6Al–4V, based on which the LCF performance parameters were determined. In addition, the fatigue fracture surfaces were observed by using scanning electron microscopy (SEM), and the results indicated that fatigue cracks originated from the surface or subsurface defects.

A deep understanding of the laser-material interaction mechanism, characterized by laser absorption, is very important in simulating the laser metal powder bed fusion (PBF) process. This is because the laser absorption of material affects the temperature distribution, which influences the thermal stress development and the final quality of parts. In this paper, a three-dimensional finite element analysis model of heat transfer taking into account the effect of material state and phase changes on laser absorption is presented to gain insight into the absorption mechanism, and the evolution of instantaneous absorptance in the laser metal PBF process. The results showed that the instantaneous absorptance was significantly affected by the time of laser radiation, as well as process parameters, such as hatch space, scanning velocity, and laser power, which were consistent with the experiment-based findings. The applicability of this model to temperature simulation was demonstrated by a comparative study, wherein the peak temperature in fusion process was simulated in two scenarios, with and without considering the effect of material state and phase changes on laser absorption, and the simulated results in the two scenarios were then compared with experimental data respectively.

Renewable energy, especially wind power, is required to reduce greenhouse gas emissions and fossil fuel use. Variable wind patterns and weather make wind energy integration into modern grids difficult. Energy trading, resource planning, and grid stability demand accurate forecasting. This study proposes a hybrid deep learning framework that improves forecasting accuracy and interpretability by combining advanced deep learning (DL) architectures, explainable artificial intelligence (XAI), and metaheuristic optimization. The intricate temporal relationships in wind speed data were captured by training Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), LSTM-GRU hybrid, and Bidirectional LSTM-GRU following data preprocessing and normalization. To enhance transparency, Local Interpretable Model-Agnostic Explanations (LIMEs) were applied, revealing key time-step contributions across three urban datasets (Los Angeles, San Francisco, and San Diego). The framework further incorporates the Snake Optimizer Algorithm (SOA) to optimize hyperparameters such as LSTM units, dropout rate, learning rate, and batch size, ensuring improved training efficiency and reduced forecast error. The model predicted 2020–2040 wind speeds using rolling forecasting; the SOA-optimized LSTM model outperformed baseline and hybrid models, achieving low MSE, RMSE, and MAE and high R² scores. This proves its accuracy, stability, and adaptability across climates, supporting wind energy prediction and sustainable energy planning.