Effect of Heat Treatment States of Feedstock on the Microstructure and Mechanical Properties of AA2219 Layers Deposited by Additive Friction Stir Deposition.

This study is the first to research the microstructure and mechanical properties of the workpiece after additive friction stir deposition (AFSD) of the feedstock at different heat treatment stages. AA2219 aluminum alloys with three different heat treatment stages were selected as the feedstock, and alloys with dense structure were successfully prepared by the additive friction stir deposition AFSD process. Experimental results show that AFSD exhibits an excellent ability to refine grains and improve the uniform distribution of precipitates in the second phase, thereby improving the plasticity of AA2219 alloy after the AFSD process. Because of the continuous dynamic recrystallization (CDRX) in the AA2219 alloy during AFSD, the grain size after the AFSD process is independent of the initial feedstock grain size for three samples. The equilibrium phase (θ) size is genetically related to the initial size of the second-phase particles in the feedstock. Due to grain refinement and dislocation strengthening, the yield strength of AA2219-casting increased significantly from 79.8 MPa to 124.1 MPa after AFSD. The yield strength of the AA2219-T4 decreases slightly from 151.8 MPa to 140.4 MPa after AFSD. The precipitation of the second phase leads to a decrease in solid solution strengthening and dislocation strengthening. However, grain refinement strengthening partially offsets this reduction. The yield strength of AA2219-T87 decreased from 398.5 MPa to 147.2 MPa after AFSD. As such, grain refinement strengthening and solid solution strengthening by the AFSD process are much smaller than the yield strength lost by precipitation strengthening and dislocation strengthening.

Enhanced Mechanical Properties of Cast Cu-10 wt%Fe Alloy via Single-Pass Friction Stir Processing.

In this study, Cu-10 wt% Fe alloy in as-cast state was modified using friction stir processing (FSP). The microstructure evolution of Cu-10 wt% Fe alloys in different states was characterized in detail using scanning electron microscopy (SEM), electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM). The results show that due to dynamic recrystallization, the FSPed Cu-10 wt% Fe alloy obtained a uniformly equiaxed ultrafine microstructure with low density of dislocation, high proportion of high-angle grain boundaries (HAGBs), and high degree of recrystallization. Fine equiaxed grains with an average size of 0.6 µm were produced after FSP. Many fine-precipitate Fe-phases with an average size of 20 nm were uniformly distributed in the Cu matrix. The FSPed samples possessed excellent mechanical properties, such as high Vickers hardness (163.5 HV), ultimate tensile strength (538.5 MPa), and good elongation (16%). This single-pass FSP method does not require subsequent aging treatment and provides a simple and efficient way to improve the properties of Cu-Fe alloys.

Influence of Preheating Temperature on the Microstructure and Mechanical Properties of 6061/TA1 Composite Plates Fabricated by AFSD.

In this study, composite plates of 6061/TA1 were successfully manufactured using additive friction stir deposition (AFSD). The impact of preheating temperatures (room temperature, 100 °C, 200 °C) on the interfacial microstructure and interface mechanical properties at various deposition zones was studied. The results showed that as the preheating temperature increased or when the deposit zone shifted from the boundary to the center, the diffusion width of Al and Ti increased, accompanied by an increase in bonding shear strength. Moreover, in the boundary zone of the sample preheated at room temperature (P-RT), only mechanical bonding was observed, resulting in the lowest bonding shear strength. Conversely, the other samples exhibited a combination of mechanical and metallurgical bonding. Under the preheating temperature of 200 °C, interfacial intermetallic compounds were observed near the center zone, which exhibited the highest bonding shear strength.

Comparative Genomic Analysis of a Thermophilic Protease-Producing Strain Geobacillus stearothermophilus H6.

The genus Geobacillus comprises thermophilic gram-positive bacteria which are widely distributed, and their ability to withstand high temperatures makes them suitable for various applications in biotechnology and industrial production. Geobacillus stearothermophilus H6 is an extremely thermophilic Geobacillus strain isolated from hyperthermophilic compost at 80 °C. Through whole-genome sequencing and genome annotation analysis of the strain, the gene functions of G. stearothermophilus H6 were predicted and the thermophilic enzyme in the strain was mined. The G. stearothermophilus H6 draft genome consisted of 3,054,993 bp, with a genome GC content of 51.66%, and it was predicted to contain 3750 coding genes. The analysis showed that strain H6 contained a variety of enzyme-coding genes, including protease, glycoside hydrolase, xylanase, amylase and lipase genes. A skimmed milk plate experiment showed that G. stearothermophilus H6 could produce extracellular protease that functioned at 60 °C, and the genome predictions included 18 secreted proteases with signal peptides. By analyzing the sequence of the strain genome, a protease gene gs-sp1 was successfully screened. The gene sequence was analyzed and heterologously expressed, and the protease was successfully expressed in Escherichia coli. These results could provide a theoretical basis for the development and application of industrial strains.

In situ synthesis of hydroxyapatite nanorods on graphene oxide nanosheets and their reinforcement in biopolymer scaffold.

Introduction: It is urgently needed to develop composite bone scaffold with excellent mechanical properties and bioactivity in bone tissue engineering. Combining graphene oxide (GO) and hydroxyapatite (HAP) for the reinforcement of biopolymer bone scaffold has emerged as a promising strategy. However, the dispersion of GO and HAP remains to be a big challenge. Objectives: In this present work, the mechanical properties of GO and the bioactivity of and HAP were combined respectively via in situ synthesis for reinforcing biopolymer bone scaffold. Methods: GO nanosheets were employed to in situ synthesize GO-HAP nanocomposite via hydrothermal reaction, in which their abundant oxygen-containing groups served as anchor sites for the chelation of Ca2+ and then Ca2+ absorbed HPO42- via electrovalent bonding to form homogeneously dispersed HAP nanorods. Thereby, the GO-HAP nanocomposite was blended with biopolymer poly-L-lactic acid (PLLA) for fabricating biopolymer scaffold by selective laser sintering (SLS). Results: GO nanosheets were uniformly decorated with HAP nanorods, which were about 60 nm in length and 5 nm in diameter. The compressive strength and modulus of PLLA/12%GO-HAP were significantly increased by 53.71% and 98.80% compared to the pure PLLA scaffold, respectively, explained on the base of pull out, crack bridging, deflection and pinning mechanisms. Meanwhile, the mineralization experiments indicated the PLLA/GO-HAP scaffold displayed good bioactivity by inducing the formation of apatite layer. Besides, cell culturing experiments demonstrated the favorable cytocompatibility of scaffold by promoting cell adhesion and proliferation. Conclusions: The present findings show the potential of PLLA/GO-HAP composite scaffold via in situ synthesis in bone tissue engineering.

Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal.

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.