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Ti-6Al-4V(TC4) titanium alloy is a kind of α+β type two-phase titanium alloy widely used. However, due to the microscopic defects in additive manufacturing titanium alloy, its mechanical properties are lower than the forging level, and post-treatment is usually required. Therefore, it is necessary to further study the additive manufacturing process and post-treatment of TC4 titanium alloy. In this paper, the microstructure and comprehensive properties of titanium alloy were analyzed by the changes of common process parameters such as energy input power and scanning strategy in the additive manufacturing process, and the influence of other process parameters such as protective gas type, substrate thickness, powder size and other factors in the additive manufacturing process was introduced. The influence of common heat treatment methods after additive manufacturing on its microstructure and mechanical properties was also comprehensively analyzed, and the influence of new post-heat treatment methods, such as vacuum heat treatment and cyclic heat treatment, as well as the influence of multiple post-treatment and comprehensive use of heat treatment were summarized. Generally speaking that the reasonable selection of additive manufacturing process parameters and the application of post-heat treatment method are the basis for obtaining titanium alloy components with excellent performance. The comprehensive use of various heat treatment methods or other post-treatment methods and heat treatment are the effective ways to further improve the performance of titanium alloy components in additive manufacturing. To establish a uniform selection standard for additive manufacturing process parameters and post-processing process is the key to the future development of additive manufacturing.

Additive manufacturing (AM) usually leads to the epitaxial growth of columnar grains in the deposition layer along the construction direction, typically resulting in the decrease in mechanical properties. In this study, A517Q alloy steel was fabricated by laser and wire additive manufacturing (LWAM) assisted with ultrasonic vibration (UV) treatment. The microstructure characteristics of A517Q alloy steel under different UV affected were investigated by OM, XRD, SEM, and EBSD. Compared with the sample without UV, the percentage of equiaxed grains in samples with dual UV increased by 90%. Due to the optimization of grain structure, the tensile strength of the sample with dual UV increased by 29% and the elongation had also been improved. The results showed that due to the cavitation and acoustic flow effects of ultrasonic, the temperature gradient (G) of the molten pool reduced and the solidification rate (R) increased, thus promoting the transformation from columnar grains to equiaxed grains. [ABSTRACT FROM AUTHOR]

In this study, the ER321 stainless steel is fabricated by wire and arc additive manufacturing (WAAM) assisted with ultrasonic impact treatment (UIT). The mechanical properties and microstructure characterizations of ER321 stainless steel with and without UIT are investigated. It is found that grain structure of ER321 stainless steel is reduced to equiaxed dendrites (with UIT) from coarse columnar dendrites (without UIT). The UIT effectively refines grain sizes that decreases by ∼150% and homogenizes the grain structure of the deposition layers. Simultaneously, the UIT also facilitates the recrystallization that leads to the reduction of dislocation and texture densities. Furthermore, the improvement of grain structure enhances yield strength (∼10.5%), ultimate tensile strength (∼3.7%), microhardness (∼12.5%) and elongation of ER321 stainless steel. The grain boundary strengthening is the main strengthening mechanism, which leads to the yield strength increment of ER321 stainless steel under UIT condition. A way to effectively control grain structure in additive manufacturing-fabricated metal products using an UIT technique is provided in this work. [ABSTRACT FROM AUTHOR]

• Building three optimized machine learning methods to predict melt pool shape. • Utilization of melt pool shapes derived from a large number of directed energy deposition experiments as data sets. • Prediction accuracy varies between models applying different algorithms, with accuracy of up to 96% or more. Directed Energy Deposition (DED), one type of additive manufacturing (AM) as novel modern manufacturing technology, is widely employed to fabricate materials layer by layer through digital models. It is well known that the shape of the melt pool is influenced by the manufacturing process parameters during the manufacturing process, which can affect the performance of the part. Therefore, three machine learning models, support vector regression (SVR), extreme gradient boosting (XGBoost), and back propagation neural network (BPNN), were selected and constructed in this study to predict melt pool shape and to maximize prediction performance by integrating various algorithms and parameters. Meanwhile, the geometry of the melt pool, such as the height, width, and depth of 210 single-channel deposition layers, were measured as training and testing datasets, which can be supplied the basic data for training the prediction models and testing the prediction performance of models. In addition to the training and test datasets, a new dataset with 36 samples was created to validate the performance predictions made by the three machine learning models. The results demonstrate that, based on the test dataset, the RBF kernel-based SVR model predicts the melt pool height with an accuracy of ∼ 93 %, while the XGboost model predicts the melt pool width and depth with accuracy rates of ∼ 97 % and ∼ 96.3 %, respectively. In the new dataset, the BPNN model based on Adam's method achieves a prediction accuracy of ∼ 93.7 % for melt pool height, but the XGboost model achieves ∼ 96.6 % and ∼ 97.8 % for melt pool width and depth, correspondingly. Thus, in addition to the training and testing datasets, the enhanced machine learning models exhibited remarkable prediction accuracy, excellent generalization, and robustness in the new dataset. As a result, the application of machine learning has greatly improved the possibility of controlling the DED process more intelligently and stably. [ABSTRACT FROM AUTHOR]

The ER321 stainless steel was fabricated by laser and wire additive manufacturing (LWAM) assisted with ultrasonic vibration (UV) under synchronous motion conditions. It was found that the grain structure of ER321 stainless steel varied from coarse columnar dendrites (without UV) to equiaxed dendrites (with UV). And, the UV effectively weakened the texture strength and homogenized the grain structure of the deposition layers. The improvement of grain structure enhanced the microhardness (~10.7%) and yield strength (~11.9%) of ER321 stainless steel. These results show that this innovative manufacturing approach can effectively improve the problem of coarse columnar dendrites in the additive manufactured complex large-scale components. [ABSTRACT FROM AUTHOR]