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<p>Escalating environmental imperatives have intensified the demand for machinable, low-carbon composite materials, exposing critical knowledge gaps in the drilling behavior of surface-engineered natural fiber systems. This study addresses this gap by systematically investigating the drilling performance of alkali–silanized Corchorus olitorius filler–reinforced polymer composites. The effects of spindle speed, feed rate, and drill diameter on thrust force, material removal rate, surface roughness, and roundness error were experimentally evaluated. The results demonstrate that high spindle speed combined with low feed rate and smaller drill diameter significantly minimizes drilling-induced damage, yielding lower thrust force, improved surface integrity, and enhanced dimensional accuracy. Chip morphology analysis revealed that parameter-controlled chip segmentation plays a critical role in governing surface quality and material removal efficiency. Furthermore, data-driven prediction models were developed, among which the Support Vector Machine (SVM) exhibited superior accuracy in forecasting drilling responses, outperforming ANN and KNN frameworks. These findings establish optimized drilling guidelines and reliable predictive capability for eco-engineered composites, highlighting their suitability for lightweight, cost-effective structural and residential applications where moderate mechanical performance is sufficient.</p>

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Smart drilling of alkali-silanized jute-epoxy composites: machine learning-driven insights for eco-friendly lightweight materials

  • 期刊论文

  • 发表日期：

    2026年7月7日

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  • Vijay Kumar Mahakur,
  • Rajdeep Paul,
  • Santosh Kumar,
  • Sumit Bhowmik,
  • Manikandaraja G,
  • A. C. Umamaheshwer Rao,
  • P. Venkateshwar Reddy

摘要

Escalating environmental imperatives have intensified the demand for machinable, low-carbon composite materials, exposing critical knowledge gaps in the drilling behavior of surface-engineered natural fiber systems. This study addresses this gap by systematically investigating the drilling performance of alkali–silanized Corchorus olitorius filler–reinforced polymer composites. The effects of spindle speed, feed rate, and drill diameter on thrust force, material removal rate, surface roughness, and roundness error were experimentally evaluated. The results demonstrate that high spindle speed combined with low feed rate and smaller drill diameter significantly minimizes drilling-induced damage, yielding lower thrust force, improved surface integrity, and enhanced dimensional accuracy. Chip morphology analysis revealed that parameter-controlled chip segmentation plays a critical role in governing surface quality and material removal efficiency. Furthermore, data-driven prediction models were developed, among which the Support Vector Machine (SVM) exhibited superior accuracy in forecasting drilling responses, outperforming ANN and KNN frameworks. These findings establish optimized drilling guidelines and reliable predictive capability for eco-engineered composites, highlighting their suitability for lightweight, cost-effective structural and residential applications where moderate mechanical performance is sufficient.