Laser shock peening (LSP) is an advanced surface strengthening technique crucial for enhancing the performance of critical components operating in extreme service environments. By generating gradient residual compressive stress and refining the microstructure via the interaction between the laser and metallic materials, LSP achieves multiscale modulation of the surface properties of metal components and considerably improves the fatigue, wear, and corrosion resistance of the material under demanding conditions. Owing to its noncontact surface modification process and properties, LSP has garnered considerable interest across fields such as aerospace, rail transit, biomedicine, and the nuclear industry. The pulse duration of the laser used in LSP considerably influences its interaction with metallic materials. Ultrashort-pulsed LSP exhibits extreme nonlinear, nonequilibrium, and multiscale time/space properties during its interaction with metallic materials, distinguishing it from short-pulsed LSP. However, existing reviews have predominantly analyzed LSP based on various factors, equipment, single performance, and applications. The pulse duration, which inevitably influences the application of LSP, has not been investigated yet. This review analyzes about 180 short-pulsed LSP and approximately 100 ultrashort-pulsed LSP papers published between 1963 and 2025 and focuses on the laser pulse duration to elucidate the existing status and prospective application value of short- and ultrashort-pulsed LSP from the viewpoints of mechanisms, processes, and applications. The associated challenges and prospects are examined and summarized using strengthening mechanisms, high-fidelity prediction models, process coupling innovations, and intelligent and efficient strengthening equipment. This work offers valuable insights for advancing laser manufacturing processes towards meeting the rigorous demands of extreme applications.

Fatigue properties of materials by Additive Manufacturing (AM) depend on many factors such as AM processing parameter, microstructure, residual stress, surface roughness, porosities, post-treatments, etc. Their evaluation inevitably requires these factors combined as many as possible, thus resulting in low efficiency and high cost. In recent years, their assessment by leveraging the power of Machine Learning (ML) has gained increasing attentions. A comprehensive overview on the state-of-the-art progress of applying ML strategies to predict fatigue properties of AM materials, as well as their dependence on AM processing and post-processing parameters such as laser power, scanning speed, layer height, hatch distance, built direction, post-heat temperature, etc., were presented. A few attempts in employing Feedforward Neural Network (FNN), Convolutional Neural Network (CNN), Adaptive Network-Based Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and Random Forest (RF) to predict fatigue life and RF to predict fatigue crack growth rate are summarized. The ML models for predicting AM materials’ fatigue properties are found intrinsically similar to the commonly used ones, but are modified to involve AM features. Finally, an outlook for challenges (i.e., small dataset, multifarious features, overfitting, low interpretability, and unable extension from AM material data to structure life) and potential solutions for the ML prediction of AM materials’ fatigue properties is provided.

Titanium alloys have a wide application in aerospace industries as it has greater strength and low density, but it has poor tribological properties. To improve its friction and wear performance, in present work, a femtosecond laser is used to directly irradiate the Ti6Al4V titanium alloy surface in air conditioning, which results in localized ablation and the formation of periodic microstructures but also a strong pressure wave, propagating the material inside. Through the optimization of processing parameters, surface modification and periodic micropatterning with effective anti-friction properties were successfully induced on the surface. After a treatment of femtosecond laser-induced surface modification (FsLSM), the surface microhardness was improved by 16.6% and compressive residual stress reached −746 MPa. Besides, laser-induced periodic surface structures (LIPSS) with a titanium oxide outer coating were fabricated uniformly on the titanium alloy surface. Rotary ball-on-disk wear experiments revealed that the average coefficient of friction (COF) and wear mass loss of the specimen with FsLSM treatment were largely reduced by 68.9% and 90% as compared to that of untreated specimens, respectively. It was analyzed that the reason for the remarkable wear resistance was attributed to the comprehensive action of the generation of LIPSS, the titanium oxide outer coating, high amplitude compressive residual stress and gradient grain size distribution on the subsurface during the laser surface treatment. Since the findings here are broadly applicable to a wide spectrum of engineering metals and alloys, the present results offer unique pathways to enhancing the tribological performance of materials.