Effects of Different Melting Technologies on the Purity of Superalloy GH4738

Exploring the potential of intermetallic alloys as implantable biomaterials: A comprehensive review

This review delves into the utilization of intermetallic alloys (IMAs) as advanced biomaterials for medical implants, scrutinizing their conceptual framework, fabrication challenges, and diverse manufacturing techniques such as casting, powder metallurgy, and additive manufacturing. Manufacturing techniques such as casting, powder metallurgy, additive manufacturing, and injection molding are discussed, with specific emphasis on achieving optimal grain sizes, surface roughness, and mechanical properties. Post-treatment methods aimed at refining surface quality, dimensional precision, and mechanical properties of IMAs are explored, including the use of heat treatments to enhance biocompatibility and corrosion resistance. The review presents an in-depth examination of IMAs-based implantable biomaterials, covering lab-scale developments and commercial-scale implants. Specific IMAs such as Nickel Titanium, Titanium Aluminides, Iron Aluminides, Magnesium-based IMAs, Zirconium-based IMAs, and High-entropy alloys (HEAs) are highlighted, with detailed discussions on their mechanical properties, including strength, elastic modulus, and corrosion resistance. Future directions are outlined, with an emphasis on the anticipated growth in the orthopedic devices market and the role of IMAs in meeting this demand. The potential of porous IMAs in orthopedics is explored, with emphasis on achieving optimal pore sizes and distributions for enhanced osseointegration. The review concludes by highlighting the ongoing need for research and development efforts in IMAs technologies, including advancements in design and fabrication techniques.

Assessment of Porosity Defects in Ingot Using Machine Learning Methods during Electro Slag Remelting Process

The porosity defects in the ingot, which are caused by moisture absorption in slag during the electroslag remelting process, deserve the researcher’s attention in the summer wet season. The prediction of slag weight gain caused by moisture absorption is critical for developing slag baking and scheduling strategies and can assist workshop managers in making informed decisions during industrial production of electro slag remelting. The moisture absorption in slag under the conditions of different air humidity, experimental time, slag particle size, and CaO content in the slag are investigated by slag weight gain experiments. The purpose of this study is to predict the rate of weight gain in slag using observed weight gain data and machine learning (ML) models. The observation dataset includes features and rate of weight growth, which serve as independent and dependent variables, respectively, for ML models. Four machine learning models: linear regression, support vector regression, random forest regression, and multi-layer perceptron, were employed in this study. Additionally, parameters for machine learning models were selected using 5-fold cross-validation. Support vector regression outperformed the other three machine learning models in terms of root-mean-square errors, mean squared errors, and coefficients of determination. Thus, the ML-based model is a viable and significant method for forecasting the slag weight gain rate, whereas support vector regression can produce results that are competitive and satisfying. The results of slag weight gain data and ML models show that the slag weight gain increases with the increase of air humidity, experimental time, slag particle size, and CaO content in the slag. The porosity defect in the ingot during the ESR process often appears when the moisture in the slag exceeds 0.02%. Considering saving electric energy, the complexity of on-site scheduling, and 4 h of scheduling time, the slag T3 (CaF2:CaO:Al2O3:MgO = 37:28:30:5) is selected to produce H13 steel ESR ingot in the winter, and slag T2 (CaF2:CaO:Al2O3:MgO = 48:17:30:5) is selected to produce H13 steel ESR ingot in the summer.

Study on the Melting Temperature of CaF2-CaO-MgO-Al2O3-TiO2 Slag under the Condition of a Fixed Ratio of Titanium and Aluminum in the Steel during the Electroslag Remelting Process

During the process of electroslag remelting (ESR) of steel containing titanium and aluminum, the activity ratio between titania and alumina in CaF₂-CaO-MgO-Al₂O₃-TiO₂ slag must be fixed in order to guarantee the titanium and aluminum contents in the ESR ingots. Under the condition of fixed activity ratio between titania and alumina in the slag, the melting temperature of slag should be investigated to improve the surface quality of ESR ingots. Therefore, this paper focuses on finding a kind of slag with low melting temperature that can be used for producing steel containing titanium. In the current study, the thermodynamic equilibrium of 3[Ti] + 2(Al₂O₃) = 4[Al] + 3(TiO₂) between SUS321 steel and the two slag systems (CaF₂:MgO:CaO:Al₂O₃:TiO₂ = 46:4:25:(25 − x):x and CaF₂:MgO:CaO:Al₂O₃:TiO₂ = 46:4:(25 − 0.5 x):(25 − 0.5 x):x) are studied in an electrical resistance furnace based on Factsage software. After obtaining the equilibrium slag with fixed activity ratio between titania and alumina, the melting temperatures of the two slag systems are studied using slag melting experimental measurements and phase diagrams. The results show that the slag systems CaF₂:MgO:CaO:Al₂O₃:TiO₂ = 46:4:25:(25 − x):x, which consists of pre-melted slag S0 (CaF₂:MgO:CaO:Al₂O₃ = 46:4:25:25) and pre-melted slag F1 (CaF₂:MgO:CaO:TiO₂ = 46:4:25:25), can not only control the aluminum and titanium contents in steel, but also have the desired low melting temperature property.

Effect of Turning Amount on Metallurgical Qualities and Mechanical Properties of GH4169 Superalloy

The determination of an appropriate amount of turning for superalloy ingot surfaces, in a scientific and reasonable manner, is vital to the improvement of the metallurgical quality and comprehensive performance of superalloy ingots. In the present study, scanning electron microscopy with energy-dispersive spectroscopy, a high-temperature testing machine, a Brinell hardness tester and the Image-Pro Plus software were used to analyze and compare the types and amounts of inclusions, the average area of the (Al,Mg)O inclusions, and the mechanical properties of points at different distances from the edge of the GH4169 superalloy vacuum arc remelting (VAR) ingot edge. The effects of the amount of turning to which the superalloy is subjected, the metallurgical qualities, and the mechanical properties were systematically studied. The results showed that the five inclusion types did not change as the sampling locations moved away from the ingot edge, but the amount of inclusions and the average area of the (Al,Mg)O inclusions first decreased and then stabilized. Similarly, the tensile strength, elongation, section shrinkage, hardness, and fatigue life first increased and then stabilized. Finally, this experiment tentatively determined that an appropriate amount of turning for a GH4169 superalloy ingot is 36–48 mm.

Effects of Withdrawal Rate on the Microstructure of Directionally Solidified GH4720Li Superalloys

Increasing the ingot size of GH4720Li superalloys makes it difficult to control their microstructure, and the withdrawal rate is an important factor in controlling and refining the microstructure of GH4720Li superalloys. In this study, GH4720Li superalloy samples were prepared via Bridgman-type directional solidification with different withdrawal rates. The morphology and average size of the dendrites in the stable growth zone during directional solidification in each sample, morphology and average size of the γ’ phases, and microsegregation of each alloying element were analyzed using optical microscopy, Photoshop, Image Pro Plus, field emission scanning electron microscopy, and electron probe microanalysis. Increasing the withdrawal rate significantly helped in refining the superalloy microstructure; the average secondary dendrite arm spacing decreased from 133 to 79 µm, whereas the average sizes of the γ’ phases in the dendrite arms and the interdendritic regions decreased from 1.02 and 2.15 µm to 0.69 and 1.26 µm, respectively. Moreover, the γ’ phase distribution became more uniform. The microsegregation of Al, Ti, Cr, and Co decreased with the increase in the withdrawal rate; the segregation coefficients of Al, Cr, and Co approached 1 at higher withdrawal rates, whereas that of Ti remained above 2.2 at all the withdrawal rates.