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    The improvement of deformability in AA7075 alloy through cryogenic treatment and its correlation with microstructural evolution and FE modelling
    (2024-12-01)
    Chankitmunkong, Suwaree
    ;
    Eskin, Dmitry G.
    ;
    Patakham, Ussadawut
    ;
    Chancharoen, Wares
    ;
    Seehanam, Saran
    Cryogenic treatment has high potential for improving the deformation behavior through the recrystallization at a low temperature. In this work, true stress–strain curves were obtained via compression tests to understand the deformation behavior of an AA7075 under cryogenic conditions. Results showed a significant improvement in the flow stress of AA7075, increasing from 260 to 560 MPa at the yield point. The strain hardening exponent (n) also increased from 0.25 to 0.35 after deformation at cryogenic temperatures. The presence of Al<inf>2</inf>CuMg phase influenced the deformation texture of the tested aluminum alloy, resulting in more elongated grains and fine sub-grains after deformation at cryogenic temperatures, due to the hindered recrystallization. Microstructure evolution after deformation at room and cryogenic temperatures was investigated using EBSD technique to characterize texture and recrystallized grains. The results indicated that the spacing of the high-angle grain boundaries (HAGBs) in the sample deformed at room temperature was slightly larger than in the cryogenically treated sample. The alloy deformed at the cryogenic temperature exhibited a higher strain hardening exponent (n = 0.35) compared to room temperature deformation (n = 0.25). Furthermore, finite element analysis supported the experimental findings, showing that the Plastic Equivalent Strain (PEEQ) of the model tested at cryogenic temperature was higher than at room temperature, attributed to grain refinement during low-temperature deformation. The calculated effective stress responses at cryogenic temperatures for the investigated flow stress aligned well with the experimental results. These new aspects and mechanisms of deformation of aluminum alloys at cryogenic temperatures can improve the formability of high-strength alloys in the future production of more complex and integrated lightweight components.
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    Predicting Three-Dimensional Dose Distribution of Prostate Volumetric Modulated Arc Therapy Using Deep Learning
    (2021-12-01)
    Kummanee, Patiparn
    ;
    Chancharoen, Wares
    ;
    Tangtisanon, Kanut
    ;
    Fuangrod, Todsaporn
    Background: Volumetric modulated arc therapy (VMAT) planning is a time-consuming process of radiation therapy. With a deep learning approach, 3D dose distribution can be predicted without the need for an actual dose calculation. This approach can accelerate the process by guiding and confirming the achievable dose distribution in order to reduce the replanning iterations while maintaining the plan quality. Methods: In this study, three dose distribution predictive models of VMAT for prostate cancer were developed, evaluated, and compared. Each model was designed with a different input data structure to train and test the model: (1) patient CT alone (PCT alone), (2) patient CT and generalized organ structure (PCTGOS), and (3) patient CT and specific organ structure (PCTSOS). The generative adversarial network (GAN) model was used as a core learning algorithm. The models were trained slice-by-slice using 46 VMAT plans for prostate cancer, and then used to predict and evaluate the dose distribution from 8 independent plans. Results: VMAT dose distribution was generated with a mean prediction time of approximately 3.5 s per patient, whereas the PCTSOS model was excluded due to a mean prediction time of approximately 17.5 s per patient. The highest average 3D gamma passing rate was 80.51 ± 5.94, while the lowest overall percentage difference of dose-volume histogram (DVH) parameters was 6.01 ± 5.44% for the prescription dose from the PCTGOS model. However, the PCTSOS model was the most reliable for the evaluation of multiple parameters. Conclusions: This dose prediction model could accelerate the iterative optimization process for the planning of VMAT treatment by guiding the planner with the desired dose distribution.