When predicting welding deformation of the laser-manufactured vehicles and aerospaces, analytical solutions or empirical formulas are not usually accessible in complex problems. Based on the inherent deformation method, a machine learning (ML) approach for predicting welding deformation of welded structures is proposed based on an artificial neural network (ANN). This method is a promising substitute for analytical, empirical, and finite element (FE) solutions due to its accuracy, easy-to-use, efficiency, and universality. First, the outputs of the ANN are determined via dimensionless analysis and comparison of numerical results, which are dimensionally independent. Then, based on the inherent deformation method, the training and validation sets of the ANN are generated through an elastic finite element analysis. At last, the structure of the ANN is determined by analyzing the ANN prediction accuracy with different hidden layers, numbers of neurons, and activation functions. The results show that the ML solutions are in good agreement with the FE results, verifying the effectiveness and generalization ability of the proposed method.

Heat source parameters are critical input variables in welding thermal analysis, directly and significantly affecting the accuracy of the temperature field distribution, welding distortion, and residual stress prediction. This is particularly important in safety-critical welded structures, where high-precision heat source parameter identification is essential for ensuring the thermal simulation accuracy and mechanical performance reliability. Traditional parameter identification methods based on finite element simulations or experiments have limitations in adapting to complex working conditions and variable environments. To address this, this paper proposes the Heat Source Parameter Identification Network (HSPINet) model based on a residual convolutional neural network (ResNet) architecture with an attention mechanism capable of extracting key features from the weld morphology of T-joint structures, while accounting for the influence of process parameters and joint dimensions to achieve efficient and accurate identification of heat source parameters. This study not only enhances the intelligence level of heat source parameter identification but also provides a practical, intelligent tool for welding simulation and thermal field evaluation in complex industrial applications, demonstrating significant theoretical value and broad applicability in laser processing and manufacturing scenarios.

This study investigates the mechanical behavior and failure mechanisms of SFHCPs under low-velocity impact and compression after impact (CAI) conditions. Symmetric foam-filled hat-stiffened composite panels (SFHCPs) are widely used in critical load-bearing structures such as vessels and aircraft due to their high strength-to-weight ratio and integrated stiffener design. However, due to the material’s high sensitivity to impact, it is necessary to conduct a systematic evaluation of its application reliability. By integrating experimental testing and numerical simulation, the buckling modes characterized by symmetry and envelope number were adopted as key performance indicators. The integration of an optical buckling measurement method with iterative finite element model (FEM) updates significantly enhances model accuracy and computational efficiency. Experimental results indicate that for specimens impacted at the mid-section of the stiffener the residual compressive strength drops sharply from 106 kN to 40.6 kN (a reduction of 61.7%), with the buckling mode changing from a symmetric four-wave pattern in the undamaged state to localized buckling in the impact region, leading to brittle failure. The integration of FPP data improved the accuracy of the FEM, highlighting the critical influence of the symmetry of the buckling mode in optimizing impact-resistant composite structures.

Alloy gradient-grained structures (represented by copper as a typical single-phase face-centered cubic (FCC) metal), known for their superior mechanical properties such as enhanced strength, ductility, and fatigue resistance, have become increasingly important in aerospace and automotive industries. These alloys are often fabricated using advanced processing techniques such as laser welding, electron beam melting, and controlled cooling, which induce spatial gradients in grain size and optimize material properties by overcoming the traditional strength–ductility trade-off. In this study, a deep learning-based inversion framework combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed to efficiently predict key constitutive parameters, such as the initial critical resolved shear stress and hardening modulus, in alloy gradient-grained structures. The model integrates spatial features extracted from strain-field sequences and grain morphology images with temporal features from loading sequences, providing a comprehensive solution for path-dependent mechanical behavior modeling. Trained on high-fidelity Crystal Plasticity Finite Element Method (CPFEM) simulation data, the proposed framework demonstrates high prediction accuracy for the constitutive parameters. The model achieves an error margin of less than 5%. This work highlights the potential of deep learning techniques for the efficient and physically consistent identification of constitutive parameters in alloy gradient-grained structures, offering valuable insights for alloy design and optimization.

This study investigates capillary waves excited by droplet impact on a deep pool of Ga–In–Sn liquid metal, which spontaneously forms a nanometric oxide skin. High-speed specular reflection imaging is employed to capture the concentric wave rings, from which the wavelength and phase speed are extracted. The results demonstrate that the waves obey the classical dispersion relation ω2 = σk3/ρ despite the presence of the oxide layer. The effective surface tension retrieved from the dispersion law averages 0.313 N/m, which is close to the reported value for gallium oxide (∼0.35 N/m) but substantially lower than that of the pristine metal (0.614 N/m). This indicates that the oxide skin renormalizes the surface tension without altering the functional form of the dispersion. These findings establish droplet-impact capillary waves as a simple, contact-free probe for determining the effective surface tension of reactive liquid surfaces.