Inconel 625 alloys are widely applied for high-corrosion resistance and as high-efficiency materials in aeronautical, aerospace, chemical, nuclear, petrochemical, and marine industries. Although Inconel 625 alloys are excellent materials, they cannot be formed at room temperature owing to difficulties in processing. To improve the formability of Inconel 625, it is necessary to investigate its formability at a high-temperature range and its strain rate variation. In this study, high-temperature deformation behavior after forming was investigated. A high-temperature compression test was performed with a Gleeble 3500 testing machine at various temperatures (approximately 900–1200 °C) and strain rates (10 and 30 s−1) to obtain the high-temperature deformation characteristics of Inconel 625. Furthermore, high-temperature tensile tests were performed to measure elongations and reductions in the area of the Inconel 625 alloy. The tests focused on obtaining the flow stress data and optimal hot forging conditions under various strain rates and temperatures. The results of this research are expected to contribute to hot forming processes and to formability in hot extrusion and pilger processes.

The aim of present wor is, therfore, to investigated the effect of the damage value prediction equation on the formability of compression specimen and find the optimize forming condition.Although Inconel 625 alloys are excellent materials, Ni-base alloy cannot be formed at room temperature owing to limitation of formability. To improve the formability of Inconel 625, it is necessary to investigate the formability at a high temperature range.A high temperature compression test is performed with a Gleeble 3500 testing machine at various temperatures (approximately 900 1200°C) and strain rates (10/s and 30/s) to obtain high temperature deformation characteristics of Inconel 625. Furthermore, high temperature tensile tests results are used to measure elongations and reductions in the area of Inconel 625.A rigid-plastic finite element simulation is applied to the high temperature compression process to obtain the damage valueThe results of the hot deformation experiment and analysis are presented for various conditions of temperatures and strain rates, and it is expected that damage value will be used in hot forming processes such as hot extrusion and rolling process.

Objectives The purpose of this study is to conduct action research on a mathematics class for students with below basic academic ability using an AI-based adaptive learning system to which a personalized instruction strategy is applied and to explore educational changes between learners and teachers in this process. Methods For this reason, teachers and researchers conducted math classes using an AI-based adaptive learning system that applied personalized instruction strategies derived for middle school students with the achievement of below basic proficiency level for about four months. Collecting main data was from observation logs, reflection logs, interview, learning data, and mathematical learning affective domain test, and the data were analyzed as the mixed research method. Results During research, a class for students with below basic academic ability was changed to a personalized class using the AI-based adaptive learning system, and in this process, planning, execution, and reflection of personalized class using AI-based adaptive learning were analyzed. The learners who participated in this study were diagnosed and recovered the prerequisite learning deficits and the cognitive domains generally increased, while no significant differences were found in the affective domains. Through qualitative analysis, we confirmed that mathematical interest and mathematical self-efficacy improved and learning satisfaction increased. Teachers realized personalized classes, experienced roles of new instructors, and discovered the possibility of personalized and customized instruction within the school. Conclusions Based on the results of this study, the implementation process, advantages, and limitations of classes using the AI-based adaptive learning system applied with personalized instruction strategies were reviewed, and measures were discussed to realize personalized instruction at schools.