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Updated: May 14, 2026

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Published on: June 10, 2019
Study of Bending Strength Detection Method for SMC Composites Based on Laser-Induced Breakdown Spectroscopy
Hongbo Wang1,2, Mengke Gao1,2, Zhe Qiao1,2
1State Key Laboratory of Smart Power Distribution Equipment and System, State Grid Jibei Electric Power Company Limited, Beijing 100054, China.
Abstract:
Electric energy metering cabinets serve as critical nodes in power grid operations, providing essential protection for key components in distribution networks. Under environmental stressors, the non-metallic casings of electric energy metering cabinets are susceptible to aging-induced performance degradation, which may result in electrical safety hazards. However, rapid and precise methods for evaluating the performance of these non-metallic casings are still lacking. Laser-Induced Breakdown Spectroscopy (LIBS), capable of rapid multi-element detection with non-contact analytical advantages, was employed in this study. Thermal aging experiments were conducted to investigate the performance degradation mechanisms of sheet molding compound (SMC)-a representative non-metallic cabinet material. The research analyzed time-dependent trends in material performance and microstructural evolution during aging. By integrating LIBS with multi-analytical techniques, this study further explored the feasibility of quantitatively evaluating the bending strength of thermally aged SMC, which has rarely been reported in previous studies. Based on LIBS spectral data, bending strength characterization revealed its attenuation patterns with aging duration. The relationships between bending strength and plasma temperature, as well as the characteristic line intensity ratios of K, Al, and Ca, were systematically examined. A multivariate linear regression model incorporating these key variables was subsequently developed, yielding a high coefficient of determination (R2 = 0.9657) between the predicted and measured bending strength values. This model represents a promising initial step, but further validation with a larger dataset is necessary to enhance its reliability and generalizability.
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