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<p>A sustainable future depends on the development of cutting-edge technology backed by printed circuit boards (PCBs). Fiber-reinforced glass fibers, the main component of traditional PCBs, are non-biodegradable and greatly worsen the environment by generating an enormous amount of landfill debris. Natural fiber-reinforced composites (NFRCs), on the other hand, are becoming more popular because of their competitive mechanical qualities, biodegradability, and sustainability. Although NFRCs have been investigated for PCB applications in the past, their practicality has been limited by their frequent concentration on single-fiber systems or simple material characterization. This study presents an innovative approach by systematically evaluating multiple natural fibers—sisal, flax, hemp, and jute—for PCB fabrication. Using the hand lay-up technique, composite laminates reinforced with these fibers were fabricated, and their tensile and thermal conductivity properties were examined. This research incorporates finite element modeling (FEM) and machine learning techniques to enhance predictive capabilities. A PCB model incorporating these NFRCs was developed, and its mechanical behavior—including equivalent stresses, shear stresses, and strains—was analyzed under varying ambient temperatures (−22 °C to 50 °C). The dataset generated from FEM simulations was then used to predict the performance of NFRC-based PCBs using machine learning algorithms, providing an advanced computational perspective. The results indicate that hemp fiber-reinforced composites exhibit a 36.65 % lower Young’s modulus, and 23.72 % lower thermal conductivity compared to jute fiber-reinforced composites. The higher equivalent stresses observed in jute composites highlight the impact of mechanical and thermal property mismatches when integrated into PCB structures. By combining experimental, computational, and machine learning methodologies, this study provides novel insights into NFRC-based PCBs, paving the way for eco-friendly, high-performance electronic components with reduced environmental impact.</p>

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Predictive analysis of natural fiber-reinforced composite printed circuit boards using experimental, finite element, and machine learning approaches

  • 期刊论文

  • 发表日期：

    2025年7月26日

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  • Phani Prasanthi,
  • Haritha Akkineni,
  • U. Koteswara Rao,
  • V. V. Venu Madhav,
  • Kuldeep K. Saxena,
  • Rakesh C.,
  • Lakshita Sehgal,
  • Mushtaq Ahmad Ansari

摘要

A sustainable future depends on the development of cutting-edge technology backed by printed circuit boards (PCBs). Fiber-reinforced glass fibers, the main component of traditional PCBs, are non-biodegradable and greatly worsen the environment by generating an enormous amount of landfill debris. Natural fiber-reinforced composites (NFRCs), on the other hand, are becoming more popular because of their competitive mechanical qualities, biodegradability, and sustainability. Although NFRCs have been investigated for PCB applications in the past, their practicality has been limited by their frequent concentration on single-fiber systems or simple material characterization. This study presents an innovative approach by systematically evaluating multiple natural fibers—sisal, flax, hemp, and jute—for PCB fabrication. Using the hand lay-up technique, composite laminates reinforced with these fibers were fabricated, and their tensile and thermal conductivity properties were examined. This research incorporates finite element modeling (FEM) and machine learning techniques to enhance predictive capabilities. A PCB model incorporating these NFRCs was developed, and its mechanical behavior—including equivalent stresses, shear stresses, and strains—was analyzed under varying ambient temperatures (−22 °C to 50 °C). The dataset generated from FEM simulations was then used to predict the performance of NFRC-based PCBs using machine learning algorithms, providing an advanced computational perspective. The results indicate that hemp fiber-reinforced composites exhibit a 36.65 % lower Young’s modulus, and 23.72 % lower thermal conductivity compared to jute fiber-reinforced composites. The higher equivalent stresses observed in jute composites highlight the impact of mechanical and thermal property mismatches when integrated into PCB structures. By combining experimental, computational, and machine learning methodologies, this study provides novel insights into NFRC-based PCBs, paving the way for eco-friendly, high-performance electronic components with reduced environmental impact.