Effect of Dynamic Preheating on the Thermal Behavior and Mechanical Properties of Laser-Welded Joints.

The high cooling rate and temperature gradient caused by the rapid heating and cooling characteristics of laser welding (LW) leads to excessive thermal stress and even cracks in welded joints. In order to solve these problems, a dynamic preheating method that uses hybrid laser arc welding to add an auxiliary heat source (arc) to LW was proposed. The finite element model was deployed to investigate the effect of dynamic preheating on the thermal behavior of LW. The accuracy of the heat transfer model was verified experimentally. Hardness and tensile testing of the welded joint were conducted. The results show that using the appropriate current leads to a significantly reduced cooling rate and temperature gradient, which are conducive to improving the hardness and mechanical properties of welded joints. The yield strength of welded joints with a 20 A current for dynamic preheating is increased from 477.0 to 564.3 MPa compared with that of LW. Therefore, the use of dynamic preheating to reduce the temperature gradient is helpful in reducing thermal stress and improving the tensile properties of the joint. These results can provide new ideas for welding processes.

Effect of Preheating Temperature on Geometry and Mechanical Properties of Laser Cladding-Based Stellite 6/WC Coating.

The effect of 60Si2Mn substrate preheating on the forming quality and mechanical properties of cobalt-based tungsten carbide composite coating was investigated. Substrate preheating was divided into four classes (room temperature, 150 °C, 250 °C, and 350 °C). The morphology, microstructure, and distribution of elements of the coating were analyzed using a two-color laser handheld 3D scanner, a scanning electron microscope (SEM), and an energy dispersive X-ray spectrometer (EDX), respectively. The hardness and wear properties of the cladding layer were characterized through a microhardness tester and a friction wear experiment. The research results show that the substrate preheating temperature is directly proportional to the height of the composite coating. The solidification characteristics of the Stellite 6/WC cladding layer structure are not obviously changed at substrate preheating temperatures of room temperature, 150 °C, and 250 °C. The solidified structure is even more complex at a substrate preheating temperature of 350 °C. At this moment, the microstructure of the cladding layer is mainly various blocky, petaloid, and flower-like precipitates. The hardness and wear properties of the cladding layer are optimal at a substrate preheating temperature of 350 °C in terms of mechanical properties.

Noise Reduction of Welding Crack AE Signal Based on EMD and Wavelet Packet.

The acoustic emission (AE) signal collected by a sensor in the welding process has an overlapping frequency band and weak characteristics under a complex noise background. It is difficult for the wavelet noise reduction method, with single basis function, to effectively match the different characteristic information of the welding crack AE signal. Taking into account the adaptive decomposition characteristics of Empirical Mode Decomposition (EMD), a novel wavelet packet noise reduction method for welding AE signal was proposed. The welding AE signal was adaptively decomposed into several Intrinsic Mode Functions (IMFs) by the EMD. The effective IMFs were selected by the frequency distribution characteristics of the welding crack AE signal. A wavelet packet, with a specific basis function, was subsequently performed on the effective IMFs, which were reconstructed to be the welding crack AE signal. The simulated and experimental results indicated that the proposed method can effectively achieve noise reduction of the welding crack AE signal, which provided a mean for structure crack detection in the welding process.

Time-frequency characteristics of acoustic emission signal for monitoring of welding structural state using Stockwell transform.

The stockwell transform (S transform) is used to conduct analysis and processing of the Acoustic Emission (AE) signal in the welding process. The multi dimension instantaneous frequency information of AE signal in the joint time frequency distribution is obtained to analyze and recognize the welding structure state. The changing paces of window functions under different parameters are analyzed, and the parameters of S transform are determined to realize the optimal adaptive multi resolution and time frequency clustering. The S transform is performed to analyze the time frequency characteristics of the collected AE signal in weldment tensile testing experiment. According to the analysis results of the weldment tensile AE signal, the time frequency distribution and the first-order moment of the AE signal are observed to identify and detect the elastic, plastic deformation, and crack in the welding restrictable experiment. The results indicate that the feature information of the AE signal obtained by S transform can present the welding structural state, which provides an effective method for online detection of welding crack.