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The phase transformation temperature plays an important role in the design, production and heat treatment process of steels. In the present work, an improved version of the gradient-boosting method LightGBM has been utilized to study the influencing factors of the four phase transformation temperatures, namely Ac1, Ac3, the martensite transformation start (MS) temperature and the bainitic transformation start (BS) temperature. The effects of the alloying element were discussed in detail by comparing their influencing mechanisms on different phase transformation temperatures. The training accuracy was significantly improved by further introducing appropriate features related to atomic parameters. The melting temperature and coefficient of linear thermal expansion of the pure metals corresponding to the alloying elements, atomic Waber–Cromer pseudopotential radii and valence electron number were the top four among the eighteen atomic parameters used to improve the trained model performance. The training and prediction processes were analyzed using a partial dependence plot (PDP) and Shapley additive explanation (SHAP) methods to reveal the relationships between the features and phase transformation temperature.

Shape memory alloys (SMAs) are a type of metal with two distinct properties: superelasticity and shape memory. When SMAs are subjected to thermomechanical treatment, they become responsive to stimuli such as thermal gradient. As a result, these alloys have shown to be useful in a variety of applications, including sensors and actuators, as well as different medical devices. When it comes to shape memory materials, Ni–Ti alloys are famous and have been used in a variety of applications. The demand for shape memory alloys with high transformation temperatures (HTSMA) has risen, owing not just to academic interest but also to market demand, particularly from the transportation and oil industries, as well as robots. The actuation fatigue performance of ternary HTSMA—Ni 50 Ti 30 Zr 20 /Hf 20 (at %) and quaternary HTSMA—Ni 50 Ti 30 Zr 10 Hf 10 was evaluated and compared. Actuation fatigue tests were performed on both ternary and quaternary HTSMA on application of constant loads ranging from 250 to 450 MPa until failure occurred. This work concentrates on studying and comparing the temperatures at which phase transformation occurs before and after actuation fatigue tests were conducted, and to correlate the results for future development of HTSMAs.

Shape memory alloys (SMAs) derive their unique functional properties from martensitic transformations, with the martensitic transformation temperature (TM) serving as a key design parameter. However, existing empirical rules, such as the valence electron concentration (VEC) and lattice volume (V) criteria, are typically restricted to specific alloy families and lack general applicability. In this work, we used a data-driven methodology to find a generalizable empirical formula for TM in SMAs by combining high-throughput first-principles calculations, feature engineering, and symbol regression techniques. Key factors influencing TM were first identified and a predictive machine learning model was subsequently trained based on these features. Furthermore, an empirical formula of TM = 82(ρ¯·MP¯)−700 was derived, where ρ¯ and MP¯ represent the weight-average value of density and melting point, respectively. The empirical formula exhibits strong generalizability across a wide range of SMAs, such as NiMn-based, NiTi-based, TiPt-based, and AuCd-based SMAs, etc., offering practical guidance for the compositional design and optimization of shape memory alloys.

TC6 titanium alloy blades with thin-walled and variable cross-section characteristics are key components of aero-engine compressors. Conventional die forging methods for such parts have issues with low dimensional accuracy and poor microstructural uniformity. This study employs the DEFORM software for the simulation and optimization of preform design and investigates the effects of isothermal forging and heat treatment processes on the microstructure and properties of TC6 titanium alloy blade forgings. The process parameters were used to manufacture TC6 titanium alloy blades, and the microstructure and mechanical properties were characterized. The results indicate that an optimized preform is conducive to obtaining blade forgings with high dimensional precision. The forging temperature should be controlled at 45-75°C below the β phase transformation temperature, with a holding time of 60-120 minutes, a deformation speed of 0.01-0.1 mm/s, and a deformation degree of 25-50%. The heat treatment method and parameters significantly affect the content and morphology of primary α and secondary α in the blade forgings. The duplex annealing parameters of “850°C for 1 hour, air cooling + 600°C for 2 hours, air cooling” can achieve the optimal balance of strength and plasticity in the blade forgings.

High-molybdenum-vanadium high-speed steel is a new type of high-hardenability tool steel with excellent wear resistance, castability, and high-temperature red hardness. This paper proposes a composition design of high-molybdenum-vanadium high-speed steel for rolls, and its specific chemical composition is as follows (wt.%): C2%, Mo7.0%, V7.0%, Si0.3%, Mn0.3%, Ni0.4%, Cr3.0%, and the rest of the iron. This design is characterized by the increase in molybdenum and vanadium in high-speed steel to replace traditional high-speed steel rolls with the tungsten element in order to reduce the heavy elements’ tungsten-specific gravity segregation caused by centrifugal casting so that the roll performance is uniform and the stability of use is improved. JMatPro (version 7.0) simulation software is used for the composition design of high-molybdenum-vanadium high-speed steel. The phase composition diagram is analyzed under different temperatures. The content of different phases of the organization in different temperatures is also studied. The martensitic transformation temperature and different tempering temperatures with the different types of compounds and grain sizes are calculated. The process parameters of heat treatment of high-molybdenum-vanadium high-speed steel are optimized. The selection of carbon content and the temperature of M50 are calculated and optimized, and the results show that the range of pouring temperatures, quenching temperatures, annealing temperatures, and tempering temperatures are 1360~1410 °C, 1190~1200 °C, 818~838 °C, and 550~600 °C, respectively. Scanning electron microscope (SEM) analysis of the samples obtained by using the above heat treatment parameters is consistent with the simulation results, which indicates that the simulation has important reference significance for guiding the actual production.

We investigated whether low transformation temperature (LTT) welding materials are beneficial to the generation of compressive residual stress around a weld zone, thus enhancing the fatigue performance of the welded joint. An experimental and numerical study were conducted in order to analyze the residual stress in multi-pass T-welded joints using LTT welding wire. It was found that, compared to the conventional welded joint, greater tensile residual stress was induced in the flange plate of the LTT welded joints. This was attributed to the reheat temperature of the LTT weld pass during the multi-pass welding. The formerly-formed LTT weld pass with a reheat temperature lower than the austenite finish temperature converted the compressive residual stress into tensile stress. The compressive residual stress was generated in the regions with a reheat temperature higher than the austenite finish temperature, indicating that LTT welding materials are more suitable for single-pass welding.

Shape memory alloys (SMA) are functional materials known for their shape memory and pseudoelastic properties, which originated from a thermoelastic phase transition between two solid phases: austenite and martensite. The ranges of temperature at which austenite and martensite are stable depend primarily on the chemical composition and the thermomechanical history of the alloy. This work presents a broad overview of shape memory alloys presenting the thermoelastic phase transition at cryogenic temperatures—that is, at temperatures below the freezing point of water. Currently, this class of SMA is not very well explored due to the difficulties in conducting both structural and functional experimentations at very low temperatures. However, these materials are of great importance for extreme environments such as space. In this work, the different classes of cryogenic SMA will first be presented as a function of their phase transformation temperatures. Hints of their mechanical performance will also be reported. Cu-based systems have been identified as cryogenic SMA presenting the lowest phase transformation temperatures. The lowest measured Ms (45 K) was found for the Cu-8.8Al-13.1Mn (wt.%) alloy.

Dynamic mechanical analyzers (DMA) are applied in the thermomechanical characterization of shape memory alloys (SMA). The great sensitivity and capacity to detect small changes in mechanical properties, especially under temperature sweeping mode, make these equipment excellent options to characterize these smart metals. However, previous studies have reported differences between transformation temperatures of SMA measured by DMA in comparison with other characterization techniques, such as differential scanning calorimetry (DSC) and electrical resistance variation with temperature (ERT). In this sense, this work proposes an analysis of the use of DMA equipment to measure SMA phase transformation phenomena. For this, a micro-thermocouple was installed directly on a NiTi SMA ribbon sample during tests in the DMA equipment using increasing heating rates. A comparison of the transformation temperatures obtained from DMA with those obtained by DSC and ERT showed a significant difference in these temperatures, which was associated with the significant heat transfer between the sample and clamps. It was observed that the heat exchanges during DMA tests cause an intense temperature heterogeneity on the sample, even at heating rates as low as 1 °C·min −1 .

The martensitic transformation temperature is the basis for the application of shape memory alloys (SMAs), and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance. In this work, machine learning (ML) methods were utilized to accelerate the search for shape memory alloys with targeted properties (phase transition temperature). A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data. Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys. The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression (SVR) model. The results show that the machine learning model can obtain target materials more efficiently and pertinently, and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature. On this basis, the relationship between phase transition temperature and material descriptors is analyzed, and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms. This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys.

Binary-shape memory alloys that are based on copper, mainly copper–aluminium, copper–zinc and copper–tin alloys, either with or without ternary elemental additions, are of special interest to the industry and academia because of their good shape recovery, ease of processing, larger recovery strain and lower cost. However, unlike Ni–Ti shape memory alloys, their uses are moderately limited due to shortcomings, such as stabilization of martensite due to ageing, brittleness and low mechanical strength. Therefore, efforts have been made over the years to overcome these limitations using appropriate ternary and quaternary elemental additions. This work takes into account the data obtained from the experimental work carried out by the authors of this paper as well as the data obtained from the experimental and theoretical works carried out by earlier researchers in this area that have been published in the literature over the years. It is observed in quaternary shape memory alloys based on copper that with an increase in the atomic radius of the quaternary element, the hysteresis width is found to increase. With the addition of ternary elements to binary Cu-based alloys (Cu–Al and Cu–Zn), and quaternary elements to ternary Cu-based alloys (Cu–Al–Fe, Cu–Al–Ni, Cu–Al–Mn, Cu–Zn–Al, Cu–Zn–Ni and Cu–Zn–Si), the M s temperature either increases or decreases. This influence is directly correlated with the e v / a ratio and c v values. It is also observed that as the concentration of electrons decreases, the M s temperature decreases too. In addition, in this paper, we have tried to obtain relationships between the M s temperature and the mass or atomic% of different elements through multiple regressions to generalize the interpretations.