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Short-fiber reinforcement is a potent approach to improving the material properties of injection-molded parts. The main consideration in such reinforced materials is to preserve the fiber length, as this is the major influence on the properties of a given composite. The aim of this work was to investigate the different influencing parameters in injection molding processing on the properties of short carbon and glass fiber-reinforced polypropylene. We investigated parameters like melt temperature and back pressure, but also machine size and pre-heating regarding their influence on the tensile properties. We found that adjustments of melt temperature and back pressure only yield small improvements in the fiber length and the tensile properties, also depending on machine size, but a pre-heating step of the granules can significantly improve the properties. [ABSTRACT FROM AUTHOR]

Aiming at the new control method of filling unbalance in the flow channel by using convection mixing， the influence of micro-scale polymer melt flow factors on the control characteristics of melt temperature distribution in the flow channel was studied by numerical simulation. Taking the "H" flow channel as the research object， the convection mixing device was arranged in the flow channel. The effects of melt temperature， convective heat transfer coefficient and flow channel size on melt filling unbalance were studied by single factor experiment. The numerical simulation results show that， the increase in melt temperature is accompanied by the decrease of melt self-viscosity under adiabatic boundary conditions. The temperature difference between symmetric points decreases， which improves the filling imbalance. Under the convective heat transfer boundary， the increase of melt temperature increases the heat loss of melt through the wall， and the increase of temperature difference between symmetric points leads to the more obvious filling imbalance. The lower the convective heat transfer coefficient， the lower the thermal conductivity efficiency between melt and wall， and the higher the average melt temperature， which increases the temperature difference between symmetric points and intensifies the filling imbalance. With the decrease in the size of the flow path， the micro-scale effect is weakened， the temperature difference between symmetric points can be reduced， and the phenomenon of filling imbalance is better improved. The convective heat transfer coefficient has the greatest influence on the filling unbalance， followed by the flow channel size， and finally the melt temperature.

Copyright of Journal of Materials Engineering / Cailiao Gongcheng is the property of Journal of Materials Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Short-fiber reinforcement is a potent approach to improving the material properties of injection-molded parts. The main consideration in such reinforced materials is to preserve the fiber length, as this is the major influence on the properties of a given composite. The aim of this work was to investigate the different influencing parameters in injection molding processing on the properties of short carbon and glass fiber-reinforced polypropylene. We investigated parameters like melt temperature and back pressure, but also machine size and pre-heating regarding their influence on the tensile properties. We found that adjustments of melt temperature and back pressure only yield small improvements in the fiber length and the tensile properties, also depending on machine size, but a pre-heating step of the granules can significantly improve the properties.

This article devoted the mathematical model of the plasticization process for the injection molding processes in a cylindrical domain influenced from the main thermodynamic characteristics: particle velocity, pressure distribution, particle flow density, and melting temperature. Here is determined a thermodynamic relationship between the particle velocity vector and the scalar pressure function in which the particle velocity is defined by the three-dimensional Maxwell distribution satisfying the energy conservation law. The presented mathematical model has been described via the initial-boundary value problem for second-order parabolic equations in a cylindrical domain. Here the three-dimensional energy conservation law regulates the pressure instability effect according to the particle velocity vector in a turbulent flow. It is a new issue for defining the processing pressure distribution that is provided proofing the theorem of existence and uniqueness of their classical parameters. This result as Maxwell distribution of the particle velocity has great opportunity for studying the transient particle flows in multiscale modeling the thermodynamic diffusivity for the injection moldings processes. In the previous author's research was investigated a weak formulation of the Navier–Stokes problem. [ABSTRACT FROM AUTHOR]

In running production the measurement of process and material properties in-line, is a requirement to design and implement effective process control in injection moulding. This work presents a structured thorough analysis and review of the research concerned with in-line measurements of selected processes and material properties on the injection moulding machines. This review sets the current state of art within a range of process and material properties, and identifies areas for future research. The process and material property measurements reviewed are; Viscosity, Melt temperature, pvT and Melt density. It is identified that the largest research effort has been placed on the measurement of viscosity and melt temperature, whereas pvT and melt density measurement are less developed. Finally, gaps within the literature are identified and the proposal of a direction within the field is stated.

Aluminizing process of titanium in aluminum melts A7 and AK12 was investigated. The influence of aluminizing regime on the structure and chemical composition of the coating on the titanium alloy VT1-0 was established. [ABSTRACT FROM AUTHOR]

This work aims to focus on fine precipitation of Mg-Si compound in AS21 alloy system by the dispersion of SiC reinforcement through stir-casting in two steps. Dual step stir-casting at a melt temperature of 680 o C, 700 o C, and 720 o C with varied stirring rates (S) manufactured the AS21 alloy composite having 2, 4 and 6 wt.% of SiC. The Taguchi L9 experiments were implemented with three processing factors (wt.% of reinforcement, melt temperature and stirring speed) at three levels in order to acquire optimum conditions. Metallographic examinations depict the formation fine grain structure and precipitation of fine Mg-Si compound with the SiC reinforcement. The ANOVA analysis identified the SiC reinforcement as the most effective parameter influencing the mechanical properties of AS21 alloy composites. The tensile strength of 199.35 MPa was attained at the optimum two-step stir casting conditions: i.e. melt temperature of 720 o C, stirring speed of 600 rpm by adding 6 wt.% of SiC in AS21 alloy. The influence of SiC variation on Mg2Si compound by duel step stir casting were discussed in detail. [ABSTRACT FROM AUTHOR]

Highly reliable and accurate melt temperature measurements in the barrel are necessary for stable injection molding. Conventional sheath-type thermocouples are insufficiently responsive for measuring melt temperatures during molding. Herein, machine learning models were built to predict the melt temperature after plasticizing. To supply reliably labeled melt temperatures to the models, an optimized temperature sensor was developed. Based on measured high-quality temperature data, three machine learning models were built. The first model accepted process setting parameters as inputs and was built for comparisons with previous models. The second model accepted additional measured process parameters related to material energy flow during plasticizing. Finally, the third model included the specific heat and part weights reflecting the material energy, in addition to the features of the second model. Thus, the third model outperformed the others, and its loss decreased by more than 70%. Meanwhile, the coefficient of determination increased by about 0.5 more than those of the first model. To reduce the dataset size for new materials, a transfer learning model was built using the third model, which showed a high prediction performance and reliability with a smaller dataset. Additionally, the reliability of the input features to the machine learning models were evaluated by shapley additive explanations (SHAP) analysis. [ABSTRACT FROM AUTHOR]

In running production the measurement of process and material properties in-line, is a requirement to design and implement effective process control in injection moulding. This work presents a structured thorough analysis and review of the research concerned with in-line measurements of selected processes and material properties on the injection moulding machines. This review sets the current state of art within a range of process and material properties, and identifies areas for future research. The process and material property measurements reviewed are; Viscosity, Melt temperature, pvT and Melt density. It is identified that the largest research effort has been placed on the measurement of viscosity and melt temperature, whereas pvT and melt density measurement are less developed. Finally, gaps within the literature are identified and the proposal of a direction within the field is stated. [ABSTRACT FROM AUTHOR]

The possibility of obtaining real-time information about the thermodynamic state of the molten metal pool created during additive layer-by-layer deposition of powder material on a substrate surface by laser directed energy deposition (LDED) technique is considered. Current information about the thermodynamic temperature of the molten metal material is important for obtaining predictable properties of parts manufactured using laser-based additive technologies. As a result of the studies carried out, the existence of a linear dependence of the intensity of light radiation from the melt pool in the spectral range (280–380 nm) on the power density of laser radiation has been found. This linear dependence allows to obtain further information about the quality of the LDED process. The obtained scientific and technical solutions for the melt pool temperature control can be used to improve the precision control in laser additive manufacturing of metal parts with stringent quality requirements. Along with this, the results obtained in the work will be used to improve the information base of the decision support system being developed by the team of authors for process engineers of laser robotic equipment. [ABSTRACT FROM AUTHOR]

The effects of melt temperature on the residual stress of thermoplastic polyurethane(TPU)sheets during the injection molding process were studied by birefringence. The results indicate that the residual stress in the near gate area is higher than that in the far gate area and decreases with the increase of the melt temperature. The residual stress of cross section in the two directions is characterized. The injection molded TPU sheets have obvious skin-core structure and zero stress layer. The thickness of core layer increases with the melt temperature rising. The results of residual stress analysis show that the residual stress of core layer is dominated by the flow residual stress, while it is the result of superposition of flow and thermal residual stress in the surface layer. Besides, the warpage deformation is directly related to the distribution of residual stress. The change of the dimensions of the TPU sheets is directly caused by the release of flow residual stress.

During a severe accident in light water reactors, the molten reactor core (corium) falls into a water pool in the form of a jet. Complex interactions may occur between the melt and coolant known as molten fuel–coolant interactions (FCI), including energetic coolant evaporation and metallic melt (e.g., Zr and Fe) oxidation. This may further lead to steam and hydrogen explosions, which are both substantial safety risks for nuclear power plants. The heat of reaction and hydrogen production during oxidation can influence the progress and severity of the accidents. For example, the reaction heat may prolong the liquid state of corium, potentially leading to high-intensity explosions, whereas the generated hydrogen can create a combustible atmosphere, increasing the risk of hydrogen explosion. Therefore, this study evaluates the hydrogen production and oxidation degree of molten metallic droplets falling into a water pool to improve the FCI models for the risk evaluation of severe accident safety. The MISTEE-OX facility at KTH, which has been primarily built to study steam explosions is modified to investigate oxidation during FCI and provide experimental data on the oxidation behaviour of metallic droplets (Zr/Fe) quenched in a subcooled water pool. The dynamics of the falling droplets and generated bubbles are recorded using a high-speed camera, and the total volume of the bubbles is measured using a graduated cylinder. This study presents preliminary experimental results of the oxidation between Zr/Fe droplets and water, as well as recent improvements in measurement methods and facility upgrades. Our research findings are useful to enhance the knowledge of the oxidation process in FCI phenomena and validate the related mechanistic models in FCI codes. [ABSTRACT FROM AUTHOR]

Featured Application: In the presented research, machine learning methods were applied to the prediction of melt temperature and carbon concentration in the melt in the basic oxygen furnace (BOF). The study's significance is that machine learning methods have not yet been applied to such an extent, even though the metallurgy industry requires it. The presented significant results will help choose the most powerful modeling method and improve the steelmaking process' variable prediction. Prediction of temperature and carbon concentration can improve the process control and reduce after-blows in melting. Estimation of endpoint in BOF is currently the most researched field of interest to ensure the quality of produced steel. The basic oxygen steelmaking process (BOS) faces the issue of the absence of information about the melt temperature and the carbon concentration in the melt. Although deterministic models for predicting steelmaking process variables are being developed in metallurgical research, machine-learning models can model the nonlinearities of process variables and provide a good estimate of the target process variables. In this paper, five machine learning methods were applied to predict the temperature and carbon concentration in the melt at the endpoint of BOS. Multivariate adaptive regression splines (MARS), support-vector regression (SVR), neural network (NN), k-nearest neighbors (k-NN), and random forest (RF) methods were compared. Machine modeling was based on static and dynamic observations from many melts. In predicting from dynamic melting data, a method of pairing static and dynamic data to create a training set was proposed. In addition, this approach has been found to predict the dynamic behavior of temperature and carbon during melting. The results showed that the piecewise-cubic MARS model achieved the best prediction performance for temperature in testing on static and dynamic data. On the other hand, carbon predictions by machine models trained on joined static and dynamic data were more powerful. In the case of predictions from dynamic data, the best results were obtained by the k-NN-based model, i.e., carbon, and the piecewise-linear MARS model in the case of temperature. In contrast, the neural network recorded the lowest prediction performance in more tests. [ABSTRACT FROM AUTHOR]

The global plastics production has shown a rapid growth from 1.5 to 367 million metric tonnes from year 1950 to 2020, where it has been predicted that this growth rate might increase further within the next few decades. In future, there will a be huge demand for polymeric materials due to their superior properties such as high specific strength, ease of shaping/forming into intricate shapes, high resistance to environmental factors, etc. Here, polymer extrusion serves as one of the fundamental approaches of processing plastic/polymeric materials. Meanwhile, in the majority of polymer processes, materials are usually being processed inside closed and pressurized chambers and hence real-time process monitoring is vital for achieving high-quality products. In this study, a comprehensive review is made to explore the state-of-the-art of the major process monitoring techniques (melt temperature, melt pressure, mass throughput, screw torque melt viscosity) in polymer extrusion. Moreover, the previous works reported on process monitoring are discussed. Eventually, a critical discussion is made on the current challenges/limitations and future trends in terms of achieving better quality melt in polymer extrusion for making good quality products with a lower unit cost. The importance of the selection of appropriate monitoring techniques is also emphasized.

Copyright of Journal of Materials Engineering / Cailiao Gongcheng is the property of Journal of Materials Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

One intensively developing field in solar engineering is to increase the efficiency of solar photothermal heating installations. The main element of photothermal installations with solar heating is a vacuumed heat receiver with a selectively absorbing coating. Efficient selectively absorptive coatings make it possible to create vacuum pipes for the new generation of solar collectors able to operate year-round. The aim of our study is to create metal-ceramic materials and coatings with high selectively absorbing properties. The results on create metal-ceramic composite materials of TiO2–NiO by partially reducing a metal of one oxide with the help of a solar furnace is given. Phase formation of cermet composite materials depending on technological modes is examined. It is shown that the phase composition of the synthesized cermet composite material depends mainly on melt temperature and the rate of its cooling. An optimal technological mode for synthesizing cermet is as follows: to obtain the overheated melt with temperature higher than 2000°C with further ultrafast quenching with a rate of ~105–106 °C/s. The dependence between spectral and optical properties of selectively absorbing coatings and the production technology and properties of the synthesized composite materials is given. The highest value of integral absorption αs = 96.96%, emissivity ε = 3.48, and high selectivity coefficient αs/ε = 27.86 are typical for cermet coating produced by melting in air with melt overheating under a cooling rate of ~105–106 °C/s. The experiments on coating heating in vacuum show that these coatings are the most heat resistant relative to coatings generated by other technological modes. Spectral and optical properties change insufficiently after heating under Т = 495°C and they are αs = 91.69%, ε = 3.7, and αs/ε = 24.78. [ABSTRACT FROM AUTHOR]

Copyright of China Plastics / Zhongguo Suliao is the property of Journal Office of CHINA PLASTICS and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the competitive market of plastic fillers, inexpensive and reliable materials are always sought after. Using a method of thermal conversion called pyrolysis, a potential contender was created from a plant biomass known as soybean hulls (SBH). SBH are a byproduct of the soybean farming industry and represent an abundant and inexpensive feedstock. The thermal conversion of SBH material gives rise to a lightweight carbon-rich filler called pyrolyzed soybean hulls (PSBH). We created two separate lots, lots A and B, with lot A corresponding to SBH pyrolyzed at 450 ° (PSBH-A) and lot B corresponding to SBH pyrolyzed at 500 ° (PSBH-B). Both lots of PSBH were also milled to reduce their particle size and tested against the as-received PSBH fillers. These milled materials were designated as ground soybean hulls (GSBH). Two different polyolefins, linear lowdensity polyethylene (LLDPE) and polypropylene (PP), were used for this study. The PSBH fillers were added to the polyolefins in weight percentages of 10%, 20%, 30%, 40%, and 50%, with the resulting plastic/PSBH composites being tested for their mechanical, thermal, and water absorption properties. In general, the addition of filler increased the maximum stress of the LLDPE/PSBH composites while reducing maximum stress of the PP/PSBH composites. The strain at maximum stress was reduced with increasing amounts of the PSBH filler for all composites. The modulus of elasticity generally increased with increasing filler amount. For thermal properties, the addition of the PSBH filler increased the heat distortion temperature, increased the thermal decomposition temperature, and reduced the heat of fusion of the composites compared to the neat polyolefins. The liquid absorption and thickness swelling in the materials were small overall but did increase with increasing amounts of the PSBH filler and with the time spent submerged in liquid. Milling the PSBH material into GSBH generally had small effects on the various tested material properties and led to easier mixing and a smoother finish on the surface of processed samples. The differences observed between lot A and lot B composites were often small or even negligible. [ABSTRACT FROM AUTHOR]

Highly reliable and accurate melt temperature measurements in the barrel are necessary for stable injection molding. Conventional sheath-type thermocouples are insufficiently responsive for measuring melt temperatures during molding. Herein, machine learning models were built to predict the melt temperature after plasticizing. To supply reliably labeled melt temperatures to the models, an optimized temperature sensor was developed. Based on measured high-quality temperature data, three machine learning models were built. The first model accepted process setting parameters as inputs and was built for comparisons with previous models. The second model accepted additional measured process parameters related to material energy flow during plasticizing. Finally, the third model included the specific heat and part weights reflecting the material energy, in addition to the features of the second model. Thus, the third model outperformed the others, and its loss decreased by more than 70%. Meanwhile, the coefficient of determination increased by about 0.5 more than those of the first model. To reduce the dataset size for new materials, a transfer learning model was built using the third model, which showed a high prediction performance and reliability with a smaller dataset. Additionally, the reliability of the input features to the machine learning models were evaluated by shapley additive explanations (SHAP) analysis.

Microinjection molding has been attracting increasing attention and application in fabricating products with functional surface microstructures. The processing parameters, packing pressure, and melt temperature have important effects on the filling quality. In order to study the mechanisms of the packing pressure and melt temperature on the filling quality of micropillars, a simulation model of injection molding of nanopillars was constructed by molecular dynamics software and a series of injection molding experiments of micropillars were carried out in this paper. Subsequently, the mechanisms were analyzed qualitatively. The results showed that the frozen layers were formed at the interface between the polymer melt and mold under the action of heat transfer, which prevented effective filling of the polymer melt. The filling quality of the micropillars could be improved significantly via increasing the melt temperature and the packing pressure, but the mechanisms were different. To be specific, the increase of the packing pressure could make more polymer melts fill into the cavity fully. Thus, the density of the micropillars was increased and the filling quality could be improved. The forming rate of frozen layers could be slowed down by increasing the melt temperature. As a result, the purpose of improving the filling quality was achieved. [ABSTRACT FROM AUTHOR]

The basic oxygen steelmaking process (BOS) faces the issue of the absence of information about the melt temperature and the carbon concentration in the melt. Although deterministic models for predicting steelmaking process variables are being developed in metallurgical research, machine-learning models can model the nonlinearities of process variables and provide a good estimate of the target process variables. In this paper, five machine learning methods were applied to predict the temperature and carbon concentration in the melt at the endpoint of BOS. Multivariate adaptive regression splines (MARS), support-vector regression (SVR), neural network (NN), k-nearest neighbors (k-NN), and random forest (RF) methods were compared. Machine modeling was based on static and dynamic observations from many melts. In predicting from dynamic melting data, a method of pairing static and dynamic data to create a training set was proposed. In addition, this approach has been found to predict the dynamic behavior of temperature and carbon during melting. The results showed that the piecewise-cubic MARS model achieved the best prediction performance for temperature in testing on static and dynamic data. On the other hand, carbon predictions by machine models trained on joined static and dynamic data were more powerful. In the case of predictions from dynamic data, the best results were obtained by the k-NN-based model, i.e., carbon, and the piecewise-linear MARS model in the case of temperature. In contrast, the neural network recorded the lowest prediction performance in more tests.

The quality of injection moulded parts can be influenced by processing parameters. The effect of mould temperature and melt temperature on the weight and shrinkage of Styrene Acrylonitrile moulded part was observed in this research. It was found that by increasing mold temperatures at constant melt temperature and injection speed, the weight of the part decreased and the shrinkage value slightly decreased. On the other hand, by increasing melt temperature at constant mold temperature and injection speed, the part weight increased. Both mold temperature and melt temperature gave only a small effect because the temperature difference was not too large.

Copyright of China Plastics / Zhongguo Suliao is the property of Journal Office of CHINA PLASTICS and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The study of molten metal flow to the mold of a continuous casting machine (CCM) is still considered to be a complex and imperfect process. This paper is one of the author's publication series that focuses on the theoretical possibility on the motion kinetics of a liquid metal and heat fluxes in a mold under traditional casting based on: hydrodynamic theory, mathematical physics equations, and an approved numerical approach. This well-known calculation methodology makes it possible to calculate the fluxes of liquid metal motions and their temperature in the mold for the proposed method and for other methods of supplying the metal, as well as to compare the results between each other. The formulation and solution of a three-dimensional problem for determining the velocity and temperature fields in a metal supplied to a CCM mold from a submerged nozzle to a round deflector is discussed. This calculation methodology is based on a system of constitutive equations, a numerical method, a numerical scheme, and an algorithm for solving the problem. According to calculations, the hardened metal crust growing on the faces of the mold has been neglected. If traditional and proposed methods are used to objectively analyze the results and solve the problem, the same theoretical (the velocity of drawing from the mold) and the geometric parameters of a rectangular mold are taken. A patent for describing this alternative method of liquid metal supplying to a rectangular mold has been registered (No. 2018108974/02 (013808)). Some results of the numerical solution of the problem are presented, namely, schemes of liquid metal flows and the temperature thereof in various sections of the mold. [ABSTRACT FROM AUTHOR]

Based on the analysis of the interior decoration model of column A, the gate was determined as a latent side gate, four points of feeding was selected, and the mold was designed a structure with "one mold and two cavities". According to the analysis of injection molding process the melt temperature was determined as 232 °C, the holding pressure was set to 105 MPa, the holding time was set as 7 s, and the cooling time was set as 29 s. The plastic volume shrinkage rate in average was 3.42 % for the injection-molded part under this condition. After verifying this group of technical parameters in injection molding, the acoustic port characteristics of the interior trim of column A are formed perfectly, and there s no defect caused by the slender internal flow passage. The clearance between the overall contour and fixture was within the allowable range. [ABSTRACT FROM AUTHOR]

The effect of temperature and the melt cooling rate on the structure of an Al90Y10 alloy after high-pressure solidification is comparatively studied by X-ray diffraction analysis, optical microscopy, and electron microscopy. Only the crystalline α-Al and Al3Y phases form in the Al90Y10 alloy under all considered solidification conditions. The effect of the temperature, the cooling rate, and the pressure on the morphology, the size, and the microhardness of the structural alloy constituents, such as primary yttrium aluminide and eutectic crystals, is found. [ABSTRACT FROM AUTHOR]

The present study aims to optimise the process variables which are involved in the fabrication of Al 6063-SiC composite by squeeze casting process. The variables included squeeze pressure, melt temperature and silicon carbide (SiC) reinforcement. The effects of these variables on ultimate tensile strength and hardness were studied with the help of nine experiments. The analysis of variance confirmed the significance of the process variables. The optimal combination of process variables to maximise the ultimate tensile strength and hardness is achieved as 120 MPa (17.4 ksi) squeeze pressure, 850°C (1562°F) melt temperature and 15 weight percentage SiC reinforcement. The confirmatory test indicated the effectiveness of the employed technique to optimise the squeeze casting process variables for the development of aluminium matrix composites. [ABSTRACT FROM AUTHOR]

ErBa2Cu3O7−δ (Er123) superconductor is one of the best candidates of superconductor solder for the fabrication of superconducting joint between GdBa2Cu3O7−δ (Gd123) coated conductor, due to its high Tc value (93 K) and highest optimized oxygen annealing temperature among RE123 compounds. In this paper, we systematically research the effect of sintering parameters on the phase formation, microstructure and superconducting properties of Er123 powder. The optimized synthesis route to acquire high purity Er123 powder with as good superconducting properties as Gd123 has been uncovered. The melt temperature of Er123 with different dopant compared to Gd123 is also investigated, and the feasible operating temperature range of Er123 superconductor solder is discussed. This work reveals a very important starting point on fabrication high-quality superconducting joint between the commercial Gd123 coated conductor, which can further improve the development of the persistent operating mode on ultra-high field nuclear magnetic resonance and magnetic resonance imaging.

The effects of melt temperatures on the structure relaxation and compressive plasticity of La 62 Al 14 (Cu 5/6 Ag 1/6 ) 14 (Ni 1/2 Co 1/2 ) 10 bulk metallic glasses (BMGs) were studied by using X-ray diffraction, differential scanning calorimetry, dynamic mechanical analysis and compressive tests. Results indicated that the supercooled region Δ T was enlarged, the thermal stability and glass forming ability were improved, and free volume was enhanced with the increasing melt temperature of the prepared BMGs. Furthermore, compared with other samples, the sample that was subjected to the highest melt temperature showed a pronounced β-relaxation that was attributable to the large amount of free volume. The fitting of Arrhenius equation revealed that the activation energies of α-relaxation ( E α ) and β-relaxation ( E β ) decreased with increasing melt temperature, whereas the difference between E α and E β increased. Notably, the specific value of the empirical law ( E β / RT g ) decreased with increasing melt temperature, thus indicating that E β / RT g may be related to the free volume. Given the pronounced β-relaxation, BMGs with a higher overheated level exhibited improved compressive plasticity. [ABSTRACT FROM AUTHOR]

The effects of casting currents on the thermal stability of Cu36Zr48Al8Ag8 amorphous alloys were investigated. Differential scanning calorimetry and resistivity experiments show that the activation energy (∆ Ex,∆ Ep) of Cu36Zr48Al8Ag8 amorphous alloy increases with elevating currents. Annealing experiments reveal that crystallization of low-current samples occurs prior to that of high-current samples. These results indicate that casting currents could enhance the thermal stability of amorphous alloys. The reason is analyzed from the viewpoint of melt state. These results could be used to provide a possible route to fabricate amorphous alloys with improved thermal stability. [ABSTRACT FROM AUTHOR]