Classifying Radiation Degradation of Epoxy Molding Compound by Using Machine Learning and its Effect on Thermal and Mechanical Properties

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Journal of Electrical Engineering & Technology Pub Date : 2024-08-21 DOI:10.1007/s42835-024-01986-6
Dong-Hyeon Kim, Dong-Seok Kim, Sung-Uk Zhang
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Abstract

Power semiconductors play a crucial role in power conversion applications within nuclear power plants. These semiconductors are enclosed using polymeric materials for cost-effectiveness. Researchers have substantiated that polymeric materials are subject to radiation-induced degradation in nuclear power plants, prompting reliability studies. Consequently, investigating the radiation degradation behavior of polymeric materials becomes imperative to ensure their reliability and stability. This study focuses on the degradation of epoxy molding compound (EMC), a type of polymeric material, under the influence of total ionizing dose (TID). To analyze the effects of TID conditions on EMC, data was collected and subjected to various tests, including FTIR (Fourier Transform Infrared Spectroscopy) spectroscopy, thermal conductivity testing, and nanoindentation testing. These tests were conducted to assess chemical changes, thermal properties, and mechanical properties of EMC as a consequence of TID exposure. TID induces random ionization damage of EMC. Five EMC samples with different total cumulative doses were produced by varying the TID exposure time. Spectral data were obtained from the fabricated EMC samples by FTIR spectroscopy. FTIR spectral data was used to build a machine learning model, and the degree of EMC performance degradation due to TID exposure was determined. In our study, we selected an optimal algorithm among six machine learning algorithms. Dimensionality reduction methods such as ReliefF and PCA were also applied to build a more simplified discriminant model. As a result, it was confirmed that radiation changed the thermal properties of EMC materials. However, no change in the mechanical properties of EMC was observed under our test conditions.

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利用机器学习对环氧树脂模塑料的辐射降解及其对热性能和机械性能的影响进行分类
功率半导体在核电站的功率转换应用中发挥着至关重要的作用。这些半导体使用聚合材料封装,以提高成本效益。研究人员已经证实,聚合材料在核电站中会受到辐射引起的降解,从而引发可靠性研究。因此,为了确保聚合物材料的可靠性和稳定性,研究聚合物材料的辐射降解行为势在必行。本研究主要关注高分子材料环氧模塑料(EMC)在总电离剂量(TID)影响下的降解。为分析 TID 条件对 EMC 的影响,收集了数据并进行了各种测试,包括傅立叶变换红外光谱、热导率测试和纳米压痕测试。进行这些测试的目的是评估 EMC 因暴露于 TID 而产生的化学变化、热性能和机械性能。TID 会对 EMC 造成随机电离损伤。通过改变 TID 暴露时间,制作了五个具有不同总累积剂量的 EMC 样品。利用傅立叶变换红外光谱法获得了所制 EMC 样品的光谱数据。傅立叶变换红外光谱数据用于建立机器学习模型,并确定 TID 暴露导致的 EMC 性能下降程度。在研究中,我们从六种机器学习算法中选择了一种最佳算法。此外,我们还采用了降维方法,如 ReliefF 和 PCA,以建立更简化的判别模型。结果证实,辐射改变了 EMC 材料的热性能。不过,在我们的测试条件下,没有观察到 EMC 的机械性能发生变化。
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来源期刊
Journal of Electrical Engineering & Technology
Journal of Electrical Engineering & Technology ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
4.00
自引率
15.80%
发文量
321
审稿时长
3.8 months
期刊介绍: ournal of Electrical Engineering and Technology (JEET), which is the official publication of the Korean Institute of Electrical Engineers (KIEE) being published bimonthly, released the first issue in March 2006.The journal is open to submission from scholars and experts in the wide areas of electrical engineering technologies. The scope of the journal includes all issues in the field of Electrical Engineering and Technology. Included are techniques for electrical power engineering, electrical machinery and energy conversion systems, electrophysics and applications, information and controls.
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