Bearings are mechanical components that support loads and transmit rotation. The inner and outer rings come into contact with the rotating mechanism, requiring a very high level of hardness. To meet this requirement, heat treatment is commonly performed. The heat treatment process inherently involves thermal deformation. Particularly in the case of large bearings, significant deformation relative to the bearing's shape can occur, making accurate deformation prediction during heat treatment essential. However, predicting deformation in heat treatment is challenging due to the simultaneous consideration of phase transformation, heat transfer, and bearing deformation. In this study, an analysis of heat treatment-induced deformation in bearings was conducted, taking phase transformation into account. The thermal and mechanical properties were calculated based on the chemical composition of the bearing material. This information was then used to perform a deformation-heat transfer-phase transformation analysis. To validate the reliability of the analysis, experiments were conducted under the same conditions. When comparing the analysis and experimental results, differences in deformation were observed. These differences were attributed to variations in phase transformation conditions between the analysis and experiments. Consequently, it is anticipated that supplementing these results will enable the prediction of deformation while considering phase transformation conditions in bearings.

Changes in microstructure and mechanical properties of Mg-1.0Al-1.0Zn-0.2Mn-0.5Ca (AZMX1100) alloy sheet manufactured by normal casting and twin roll casting process, were studied according to process and heat treatment. Non-uniform microstructure was observed in the initial sheet produced through both processes, and in particular, tilted dendrites and shifted central segregation were observed in the twin roll casting sheet. It was homogenized through hot rolling and heat treatment, and heat treated at 350℃ and 400℃ to compare the effect of heat treatment temperature. Both sheets were homogenized by the hot rolling process, and the grain size increased as the heat treatment temperature and time increased. It was confirmed that the grain size, deviation, and distribution of the second phase were finer and more homogenized in the TRC sheet. Accordingly, mechanical properties such as hardness, formability, and tensile strength also showed better values. However, unlike other previously reported AZMX alloy systems, it showed low formability (Erichsen value), which was judged by the influence of Al2Ca present in the microstructure.

600 MPa-grade deformed bar samples were manufactured by conventional hot rolling and subsequent Tempcore heat treatment processes. Considering the short-time water quenching step of the Tempcore process for hot-rolled steel, it is inevitable that the temperature profile of the deformed bar depends strongly on its position throughout the sample thickness. As a result, its microstructure can be easily divided into two regions, the surface and the core regions. The former is expected to have a fresh martensite microstructure under rapid cooling conditions, but self-tempering occurs due to the intense heat flow from the hot core region after the process. The latter is generally known to exhibit a mixed microstructure of ferrite and pearlite due to its slow cooling rate. In this study, detailed microstructural evolutions were examined through the thickness direction. The large variation of the microstructure through the thickness direction in the deformed bar samples is partly due to the easy carbon diffusion from the limited additions of alloying elements.

This study develops and evaluates a deep learning model for predicting oxide and nitride layers based on plasma process data. We introduce a novel deep learning-based Varying Coefficient Regressor (VCR) by adapting the VCR, which previously relied on an existing unique function. This model is employed to forecast the oxide and nitride layers within the plasma. Through comparative experiments, the proposed VCR-based model exhibits superior performance compared to Long Short-Term Memory, Random Forest, and other methods, showcasing its excellence in predicting time series data. This study indicates the potential for advancing prediction models through deep learning in the domain of plasma processing and highlights its application prospects in industrial settings.

In this study, hydrogen embrittlement (HE) behavior of a SA-723 steel via controlling gaseous hydrogen pre-charging condition has been analyzed. The gaseous hydrogen charging of the SA-723 steel was performed under a constant pressure of 20 MPa of gaseous H₂ at 150℃ and 300℃ for 2 and 6h, and TDS, SSRT and Charpy tests were conducted to analyze the hydrogen embrittlement (HE) behavior of the SA-723 steel. Furthermore, prior to commencing the test, these specimens were coated with Zn to prevent hydrogen from diffusing out of a specimen during the tests. The TDS results showed that the 300℃-6h and 150℃-6h charged steels contain larger amounts of hydrogen than 300℃-2h and 150℃-2h charged steel. The SSRT and Charpy test results also showed the similar trends that the mechanical properties of the steels deteriorate as the amount of hydrogen charged in the steel increases. Therefore, this study suggests that, for SA-723 steel, the charging time parameter is more effective to charge more amount of hydrogen into SA-723 steel, rather than the charging temperature.

Damping capacities of Mg-2.5%Al and Mg-2.5%Zn (in atomic) solid solutions were comparatively investigated in order to clarify the influence of solutionized Al and Zn elements on the damping characteristics of Mg. In this study, solid solutions with similar grain size were obtained by solution treatment at 678 K for different times (24 h for Mg-2.5%Al and 36 h for Mg-2.5%Zn), followed by water quenching at RT. The Mg-2.5%Al and Mg-2.5%Zn solid solutions showed similar damping capacities in the strain-amplitude independent region of 1 × 10^-6 ~ 1 × 10^-5 and in the strain-amplitude dependent region below 6 × 10^-4, over which the Mg-2.5%Zn solid solution possessed better damping capacity than the Mg-2.5%Al solid solution. The damping tendencies depending on strain-amplitude for the two solid solutions were analyzed and discussed in terms of similar length between weak pinning points (solutes) and different solute/dislocation interaction forces in Granato-Lücke model.

Aus-tempering heat treatment is suitable for thin and small-sized in precision parts. However, the heat treatment process relies on the experience and skill of the operator, making it challenging to produce precision parts due to the cold forging process. The aims of this study is to explore suitable machine learning models using data from the aus-tempering heat treatment process and analyze the factors that significantly impact the mechanic properties (e.g. hardness). As a result, the study analyzed, from a machine learning perspective, how hardness prediction varies based on the quenching temperature, carbon (C), and copper (Cu) contents.

In order to examine the effect of cementite precipitated on the steel surface on the carburizing rate, the carburizing process was carried out at various boost times to measure the mass gain and carbon flux, phase analysis and carbon concentration analysis were performed on the surface of the carburized specimen. In the case of the only boost type, the longer the boost time, the more the mass gain by the diffused carbon follows the parabolic law and tends to increase. In particular, as the boost time increased, the depth of cementite precipitation and the average size of cementite on the steel surface increased. At a boost time of 7 min, the fraction of cementite precipitated on the surface is 7.32 vol.%, and the carburizing rate of carbon into the surface (surface-carbon flux) is about 17.4% compared to the calculated value because the area of the chemical (catalyst) where the carburization reaction takes place is reduced. The measured carbon concentration profile of the carburized specimen tended to be generally lower than the carbon concentration calculated by the model without considering precipitated cementite. On the other hand, in the pulse type, the mass gain by the diffused carbon increased according to the boost time following a linear law. At a boost time of 7 min, the fraction of cementite precipitated on the surface was 3.62 vol.%, and the surface-carbon flux decreased by about 4.1% compared to the calculated value. As a result, a model for predicting the actual carbon flux was presented by applying the carburization resistace coefficient derived from the surface cementite fraction as a variable.

As the demand for mechatronic systems and high performance increases in the machinery industry, the importance of improving friction characteristics is emphasized. During relative movement of objects, friction and wear occur on two surfaces in contact, and various methods are being designed to increase the lifespan and energy efficiency of machines. The energy increase effect using lubricants is a well-known method. In this study, a micro-sized rectangular grid pattern was produced by applying a precise micro-pattern photo lithography process. Rectangular grid patterns of the same shape and friction behavior according to the size of the pattern were produced in convex and concave shapes, and the tribological characteristics of each were analyzed.