Welding in the World最新文献

The geometry of objects by means of wire arc additive manufacturing technology (WAAM) is a function of the quality of the deposited layers. The process parameters variation and heat flow affect the geometric precision of the parts, when compared to the actual dimensions. Therefore, in situ geometry monitoring which is integrated in such a way to enable a backward control model is essential in the WAAM process. In this article, an attempt is made to study the effect of four input variables, namely voltage (U), welding current (I), travel speed and wire feed rate on the output function in the form of two geometrical characteristics of a single weld bead. These output functions which are determinant of the weld quality are width of weld bead (BW) and height of weld bead (BH). A machine learning approach is utilised to predict the bead dimensions based on the input parameters and to predict the parameters by assigning suitable scores. For predicting the bead dimensions, two models, namely linear regression and random forest, shall be utilised, whereas for the purpose of classification based on weld parameters, k-nearest neighbours model shall be employed. Through this work, a wide dataset of parameters in the form of input variable and output in the form bead dimensions are generated for 316LSi filler material which shall be used as a training data for a machine learning algorithm. Subsequently, the predicted parameters shall be cross-checked with actual parameters.

Additive manufacturing is becoming increasingly important for industrial production. In this context, directed energy deposition processes are in demand to achieve high deposition rates. In addition to the well-known laser-based processes, the electron beam has also reached industrial market maturity. The wire electron beam additive manufacturing offers advantages in the processing of copper materials, for example. In the literature, the higher energy efficiency and the resulting improvement in the carbon footprint of the electron beam are highlighted. However, there is a lack of practical studies with measurement data to quantify the potential of the technology. In this work, a comparative life cycle assessment between wire electron beam additive manufacturing (DED-EB) and laser powder additive manufacturing (DED-LB) is carried out. This involves determining the resources for manufacturing, producing a test component using both processes, and measuring the entire energy consumption. The environmental impact is then estimated using the factors global warming potential (GWP100), photochemical ozone creation potential (POCP), acidification potential (AP), and eutrophication potential (EP). It can be seen that wire electron beam additive manufacturing is characterized by a significantly lower energy requirement. In addition, the use of wire ensures greater resource efficiency, which leads to overall better life cycle assessment results.

The effects of Cr addition on the microstructure, mechanical properties, and corrosion behavior of two weld metals containing Ti or Mo within the Ni-Cu alloys used in high-speed train bogies were investigated. The results show that Cr can increase the acicular ferrite (AF) by about 15%, reduce the primary ferrite (PF) and the ferrite with second phase aligned (FSP), and slightly increase the M-A constituents in the weld containing Ti. Cr addition scarcely alters the AF, leads to a decline in PF and an increase in FSP, and causes a substantial increase in M-A constituents from 0.4 to 2.5% in the as-welded zone containing Mo. Meanwhile, it was found that Cr addition negatively affects weld toughness in the weld containing Mo due to the increase in the proportion and size of M-A constituents and the coarsening of inclusions. Regarding the corrosion resistance, Cr addition can promote the absorption of Cr on the surface of inclusions. This is the main reason for the reduction of the initial corrosion rate of the weld containing Mo, while this effect is attenuated in the welds containing Ti. In addition, Cr addition can densify the inner and outer rust layers, thereby reducing the corrosion rate of the welding rust layer.

This study investigates the impact of travel speed on the weld quality of AA7075-T651 aluminum alloy using the cold wire pulsed gas metal arc welding (CW-PGMAW) process. By maintaining a constant heat input of 0.4 kJ/mm while varying travel speed between 90 and 100 cm/min, the study examines the process’s influence on microstructure, porosity, and liquation cracking. Results demonstrate that CW-PGMAW effectively refines microstructure and reduces defect formation compared to conventional GMAW. While mechanical properties showed improvement, further optimization is necessary to achieve base metal equivalent properties. The findings contribute to the understanding of CW-PGMAW for challenging aluminum alloys and provide a foundation for future process enhancements.

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The hydrogen steel gas-shielded welding wire was utilized in the WAAM technique, and the microstructure, crystal structure, and properties of the parts generated by layer-wise deposition were analyzed and evaluated. The study revealed that the components exhibit good quality, devoid of significant defects, and demonstrate robust internal metallurgical bonding. The metallographic structure mainly consists of pearlite and ferrite. The distribution of microhardness in the parts is fairly consistent, with mean microhardness values of 196.6 HV_0.1 (transverse) and 196.7 HV_0.1 (longitudinal). The parts exhibit exceptional mechanical properties, with a transverse yield strength of 406 MPa, an elongation rate of 14.2%, and a longitudinal yield strength of 380 MPa, an elongation rate of 18.9%. At − 30 °C, the average transverse Charpy impact value is 95.7 J, and the average longitudinal is 117 J.

Laser welding offers distinct advantages over traditional methods: less heat impact, no filler metal needed, and strong weld penetration. It is efficient and cost-effective, perfect for joining materials like the stainless steel 301LN, and ideal for industries addressing climate change. This study delves into the impact of various operating parameters on weld quality, specifically focusing on microstructure and microhardness. Using the Taguchi method, it is designed an experimental setup to systematically analyze these factors. The microstructure analysis shows a unique grain structure in the weld bead and a small heat-affected zone (HAZ), indicating precise welding control. Weld penetration measurements correlated with specific operating parameters using microstructure imaging. The microhardness analysis further underlined the control over HAZ thickness, crucial for ensuring the integrity of the welded joint. Through analysis of variance (ANOVA), it is identified significant factors affecting physical properties, help to construct a mathematical model to quantify parameter influences accurately. Findings suggest that minimizing the focal spot diameter is key to optimizing weld penetration, albeit in a delicate balance with welding speed and laser power settings. Adjusting these parameters can also influence the chemical composition match between the weld bead and base material, crucial for structural integrity. For achieving the desired hardness close to the base material, specific parameter ranges are recommended: a beam oscillation amplitude of 1.45 mm, a beam oscillation amplitude between 2.82 and 2.97 kW, and a focal spot diameter above 0.34 mm. Findings offer practical insights for improving weld quality and efficiency in industrial applications.

The study aimed to assess the performance of several unsupervised machine learning (ML) techniques in online anomaly (The term “anomaly” is used here to indicate a departure from expected process behavior which may indicate a quality issue which requires further investigation. The term “defect detection” has often been used previously but the specific imperfection is often indirectly inferred.) detection during surface tension transfer (STT)-based wire arc additive manufacturing. Recent advancements in quality monitoring for wire arc manufacturing were reviewed, followed by a comparison of unsupervised ML techniques using welding current and welding voltage data collected during a defect-free deposition process. Both time domain and frequency domain feature extraction techniques were applied and compared. Three analysis methodologies were adopted: ML algorithms such as isolation forest, local outlier factor, and one-class support vector machine. The results highlight that incorporating frequency analysis, such as fast Fourier transform (FFT) and discrete wavelet transform (DWT), for feature extraction based on general frequency response and defined bandwidth frequency response, significantly improves performance, reflected in a 14% increase in F2 score, compared with time-domain features extraction. Additionally, a deep learning approach employing a convolutional autoencoder (CAE) demonstrated superior performance by processing time-frequency domain data stored as spectrograms obtained through short-time Fourier transform (STFT) analysis. The CAE method outperformed frequency domain analysis and traditional ML approaches, achieving an additional 5% improvement in F2-score. Notably, the F2-score (The F2 score is the weighted harmonic mean of the precision and recall (given a threshold value). Unlike the F1 score, which gives equal weight to precision and recall, the F2 score gives more weight to recall than to precision.) increased significantly from 0.78 in time domain analysis to 0.895 in time-frequency analysis. The study emphasizes the potential of utilizing low-cost sensors to develop anomaly detection modules with enhanced accuracy. These findings underscore the importance of incorporating advanced data processing techniques in wire arc additive manufacturing for improved quality control and process optimization.

In this paper, the welding of T2 copper and Al1060 was realized by vacuum diffusion welding process. The microstructure evolution, mechanical properties, and corrosion resistance of Cu/Al diffusion bonding layer were explored. The results show that intermetallic compounds Al₂Cu, AlCu, and Al₄Cu₉ generate at the joint under the welding condition of holding for 60 min at 530 °C. When the holding time reaches 90 min, a new phase of Al₂Cu₃ generates, and the diffusion bonding layer evolves into a four-layer structure. The thickness of diffusion layer increases with the extension of holding time and is affected by the body diffusion. The shear strength of the joint increases first and then decreases with the extension of holding time. The maximum shear strength of 20.91 MPa can be obtained under the holding time condition of 60 min, and fracture mainly occurs between Al₂Cu and AlCu phases. Nanoindentation hardness and elastic modulus of intermetallic compound phase are much higher than those of copper and aluminum matrix. Specifically, Al₄Cu₉ phase exhibits the largest nanoindentation hardness and elastic modulus of 11.062 GPa and 132.8 GPa. The corrosion resistance of diffusion bonding layers is significantly different from that of the base material. Compared with copper, the corrosion potential of each diffusion layer and aluminum matrix is relatively lower. The corrosion rates of diffusion layers and base materials are in descending order of Al > Al₂Cu > AlCu > Al₄Cu₉ > Cu.

In the present research, the possibility of using friction stir deposition (FSD) for the additive manufacturing (AM) of aluminum parts has been evaluated and checked. For this purpose, consumable tool technique was used for depositing bulk samples in the shape of linear and cylindrical parts. The current friction stir additive manufacturing (FSAM) process was carried out through the deposition of AA6061-T6 consumable rods on a substrate of the same material. For each of the linear and cylindrical types, six samples were deposited in three layers using different production parameters. FSD tool speeds including rotational, linear, and vertical were the production parameters. To evaluate the additive manufactured parts, appearance, microstructure, hardness, wear properties, and corrosion resistance were inquired. The apparent appearance characteristics for both linear and cylindrical samples were continuous layers with sufficient thickness without cracks and cavities. In terms of microstructure characteristics, the hot plastic deformation during FSAM caused enormous grain refinement (~ 560%) through the dynamic recrystallization and decreasing the precipitate size (~ 31%) by the dissolution of precipitates in the matrix, compared with the AA6061-T6 consumable rods. These microstructural changes and production parameters were correlated with the amount of frictional heat generated during the process. In order to find this correlation, the change in amount of heat input by changing the production parameters and its effect on the microstructural characteristics were discussed. For both linear/cylindrical samples, by increasing the consumable tool rotational speed and decreasing its linear/vertical speed (increase in deposition time), the heat input increased, which led to more dissolution of precipitates (decreasing their size) and grain growth (reduction of grain boundaries as the preferred precipitation sites). Decreasing the precipitate size and precipitate content reduced the three-body wear mechanism and lowered the corrosion prone areas, which improved the wear and corrosion properties, respectively. Although the dissolution of precipitates reduced the hardness of samples compared to the hardness of consumable rods (AA6061-T6), the enormous grain refinement caused by FSAM compensated this deficiency. Finally, the properties of additive manufactured parts are as follows: relatively good hardness (~ 60 HV), excellent wear rate (about 3 µgr/N.m), low friction coefficient (0.6–0.8), and excellent corrosion rate (less than 5 mpy).