The effects of welding energy on the mechanical and microstructural characteristics of ultrasonic-welded pure copper plates were investigated. Complex dynamic recrystallization and grain growth occurred inside the weld zone during ultrasonic welding. At a low welding energy, a thin band of straight weld interfaces was observed and had an ultra-fine grain structure. With an increase in welding energy, the weld interface progressively changed from flat to sinusoidal, and eventually turned into a convoluted wavy pattern, bearing similarities to shear instabilities, as observed in fluid dynamics. The lap shear load of the joints initially increased and then remained stable as the welding energy increased. The tensile characteristics of the joints significantly depended on the development of plastic deformation at the interface. The influence of the microstructure on the hardness was also discussed.

The effects of vibration amplitude on the interface reaction and mechanical strength of the Cu/Al joints were systematically investigated in ultrasonic spot welding (USW) experiments. The appropriate vibration amplitude (22.5 μm) was beneficial for obtaining a sound joint. The formation of the continuous intermetallic compounds (IMC) layer accelerated with a higher vibration amplitude. The lap shear tensile strength of the Cu/Al joints decreased when the thickness of the intermetallic layer was greater than 1 μm at various amplitudes. With the increase in welding time, a crack occurred in the copper side owing to the occurrence of the eutectic reaction, $\mathit{\alpha -A}l+\theta \to L$ , at the periphery of the nugget. The remarkable decline of ultrasonic power curves occurred at various amplitude levels upon the formation of a crack in the copper side.

Arc sound signals are considered appropriate for detecting penetration states in cold metal transfer (CMT) welding because of their noninvasive nature and immunity to interference from splatter and arc light. Nevertheless, the stability of arc sound signals is suboptimal, the conventional feature extraction methods are inefficient, and the significance of arc sound attributes for determining penetration statuses is often overlooked. In this study, a compact convolutional neural network (CNN) model is proposed for the adaptive extraction of features from arc sound signals. The model uses the Mel spectrum diagram of an arc sound signal obtained through a short-time Fourier transform (STFT) and a Mel filter bank conversion step as its input. To improve the recognition capabilities of the model, a novel CNN-selective kernel (SK) model for weld penetration recognition is introduced, which integrates the dynamic selection kernel network (SKNet) into the CNN architecture. The experimental results indicate that the CNN-SK model outperforms the traditional models, achieving an accuracy of 98.83% on the validation dataset. This model holds promise for assessing weld penetration in CMT welding applications. The project is available at https://github.com/ZWL58/data/tree/master.

Optical inspection of highly reflective cylindrical components—such as stainless-steel vessels featuring both planar and curvilinear surfaces—presents significant challenges due to complex geometric distortions in single-pass imaging. This study proposes a line-scan imaging framework that integrates synchronized kinematic control with geometry-aware distortion correction. The system addresses shape deformations through three coordinated modules: (1) parametric synchronization between rotational motion and image acquisition ensures full-surface coverage; (2) scanline-specific 1D projective transformations correct perspective distortions on toroidal sidewalls; and (3) adaptive polar coordinate remapping restores radial symmetry on circular bases. Experimental results demonstrate subpixel-level geometric correction accuracy, validating the proposed framework’s effectiveness in eliminating geometric aberrations with low computational complexity and without reliance on data-driven training, while maintaining compatibility with defect detection and quantitative surface analysis of specular cylindrical specimens.