Study of Bending Strength Detection Method for SMC Composites Based on Laser-Induced Breakdown Spectroscopy

Area of Science:

• Materials Science
• Spectroscopy
• Electrical Engineering

Background:

• Non-metallic electric energy metering cabinets are vital for power grid protection.
• Environmental stressors cause aging and performance degradation in these cabinets, posing safety risks.
• Current methods for evaluating cabinet casing performance are insufficient.

Purpose of the Study:

• To investigate the performance degradation mechanisms of Sheet Molding Compound (SMC) under thermal aging.
• To explore the feasibility of using Laser-Induced Breakdown Spectroscopy (LIBS) for quantitative evaluation of aged non-metallic cabinet materials.
• To establish a predictive model for the bending strength of thermally aged SMC.

Main Methods:

• Sheet Molding Compound (SMC) samples underwent thermal aging experiments.
• Laser-Induced Breakdown Spectroscopy (LIBS) was employed for rapid multi-element detection and analysis.
• Multi-analytical techniques were integrated with LIBS to assess material performance and microstructure.
• A multivariate linear regression model was developed using LIBS spectral data (plasma temperature, K, Al, Ca line intensity ratios).

Main Results:

• Thermal aging led to predictable performance degradation and microstructural changes in SMC.
• LIBS spectral data correlated with the attenuation of bending strength over aging duration.
• The developed regression model achieved a high coefficient of determination (R² = 0.9657) for predicting bending strength.
• Key variables identified include plasma temperature and intensity ratios of K, Al, and Ca spectral lines.

Conclusions:

• LIBS is a promising technique for rapid, non-contact evaluation of thermally aged non-metallic materials used in electric energy metering cabinets.
• The study demonstrates the feasibility of quantitatively predicting bending strength using LIBS spectral data.
• Further validation with larger datasets is recommended to improve the model's reliability and generalizability for real-world applications.