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Journal of Polymer & Composites Original Research Special issue

Optimizing Glass to Metal Composite Seal Performance: An integrated Approach with Artificial Neural Network, Multiple Regression, and Taguchi

  1. Vinod Kumar Verma Department of Mechanical Engineering, Government Engineering College,Ajmer
  2. Kunj Bihari Rana Department of Mechanical Engineering, Rajasthan Technical University, Kota
  3. Brajesh Tripathi Department of Mechanical Engineering, Rajasthan Technical University, Kota

Abstract

Composite materials, particularly glass to metal composites, are critical components in solar receiver tubes, where vacuum leakage can significantly compromise the efficiency of solar plants. This research addresses the technical barriers associated with the development of durable and high-quality glass to metal composite seals. We investigate the principles that can enhance the physical and chemical properties of these composite seals, focusing on the incorporation of TiO2 and MgO nanoparticles into boro-silicate glasses with a minor B2O3 content. The transition from muffle furnaces to induction furnaces has markedly improved the controllability of the sealing and peroxidation processes, resulting in a substantial reduction in both pre-oxidation and sealing times. The integration of nanomaterials and modifications to the B2O3 composition have led to a significant enhancement in shear strength of the composite seals. Furthermore, the vacuum leak rates have been reduced to ultra-high vacuum levels, demonstrating the effectiveness of the optimized sealing parameters. Key factors such as surface wettability were refined through adjustments in surface roughness, contact angle, and spreading area. The optimization of strength and leak rates for glass to metal composite joints was achieved by systematically varying input parameters, including material composition, oxidation time, temperature, and the ratios of B2O3, TiO2, and MgO nanomaterials. A comprehensive dataset was generated using a design of experiments approach, specifically the Taguchi L32 method, and the experimental outcomes were analyzed through multiple prediction techniques, including Multiple Regression and Artificial Neural Networks (ANN). The results were subsequently ranked based on precision and error, providing a robust framework for future advancements in glass to metal composite sealing technologies.

Keywords

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