Serious heat accumulation limits the further efficiency and application in additive manufacturing (AM). This study accordingly proposed a double-wire SS316L stainless steel arc AM with a two-direction auxiliary gas process to research the effect of three parameters, such as auxiliary gas nozzle angle, auxiliary gas flow rate and nozzle-to-substrate distance on depositions, then based on the Box–Behnken Design response surface, a regression equation between three parameters and the total score were established to optimized parameters by an evaluation system. The results showed that samples with nozzle angle of 30° had poor morphology but good properties, and increasing gas flow or decreasing distance would enhance the airflow strength and stiffness, then strongly stir the molten pool and resist the interference. Then a diverse combination of auxiliary process parameters had different influences on the morphology and properties, and an interactive effect on the comprehensive score. Ultimately the optimal auxiliary gas process parameters were 17.4°, 25 L/min and 10.44 mm, which not only bettered the morphology, but refined the grains and improved the properties due to the stirring and cooling effect of the auxiliary gas, which provides a feasible way for quality and efficiency improvements in arc additive manufacturing.

It is difficult to detect and identify natural defects in welded components. To solve this problem, according to the Faraday magneto-optical (MO) effect, a nondestructive testing system for MO imaging, excited by an alternating magnetic field, is established. For the acquired MO images of crack, pit, lack of penetration, gas pore, and no defect, Gaussian filtering, bilateral filtering, and median filtering are applied for image preprocessing. The effectiveness of these filtering methods is evaluated using metrics such as peak signal–noise ratio (PSNR) and mean squared error. Principal component analysis (PCA) is employed to extract column vector features from the downsampled defect MO images, which then serve as the input layer for the error backpropagation (BP) neural network model and the support vector machine (SVM) model. These two models can be used for the classification of partial defect MO images, but the recognition accuracy for cracks and gas pores is comparatively low. To further enhance the classification accuracy of natural weld defects, a convolutional neural network (CNN) classification model and a ResNet50 classification model for MO images of natural weld defects are established, and the model parameters are evaluated and optimized. The experimental results show that the overall classification accuracy of the ResNet50 model is 99%. Compared with the PCA-SVM model and CNN model, the overall classification accuracy was increased by 7.4% and 1.8%, and the classification accuracy of gas pore increased by 10% and 4%, respectively, indicating that the ResNet50 model can effectively and accurately classify natural weld defects.

The widespread adoption of arc additive manufacturing techniques across various industries has advanced the field of SS316L stainless steel manufacturing. It is crucial to acknowledge that different welding modes exert distinct influences on the forming and mechanical performance. This study analyzed the thermal input associated with four specific welding modes in LORCH MIG welding, clarifying the transition dynamics of molten droplets through waveform analysis and examining the resultant effects on microstructure and performance characteristics. The Pulse, Speed-Pulse-XT, and Twin-Pulse modes were found to induce spatter during the manufacturing process, consequently reducing molding efficiency in comparison to the SA-XT mode. Notably, the Twin-Pulse mode, characterized by double-pulse agitation, generated fish scale patterns along the lateral surfaces of the fabricated parts, promoting anisotropic grain growth. This microstructural refinement, compared to single-pulse samples with equivalent thermal input, resulted in enhanced mechanical properties. Nevertheless, the horizontal tensile strength of the three pulse modes was lower than the industrial standard for SA-XT mode and forging. In contrast, the SA-XT mode with an average hardness of 168.1 ± 6.9 HV and a tensile strength of 443.58 ± 5.7 MPa. Therefore, while three pulse modes offer certain microstructural advantages, the SA-XT mode demonstrates superior overall performance.

Overcharge is one of the main factors that lead to thermal runaway of lithium batteries. However, there is no research on the quantitative relationship between overcharged state and gas production, so as to effectively monitor the safe state of the battery and avoid thermal runaway. In this paper, X-ray computed tomography (CT) is proposed to explore the overcharge battery. The internal structure changes of bulge deformation and electrode separation is observed from tomographic images of two different cross-sectional directions. The relationship between gas production and overcharge state of charge (SOC) is quantitatively analyzed. As overcharge SOC increases, gas production increases exponentially. Gas distribution is analyzed by density distribution feature (DDF) vector. The gas production is mainly distributed in the middle of the overcharge batteries. It is envisaged that these techniques can be used to better understand the overcharge of battery nondestructively, visually and effectively, then will lead to avoid the occurrence of thermal runaway.