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<p>Holes are among the most common features in aerospace composite components, serving critical functions such as clearance, fastening, alignment and weight reduction. Given the high quantity of holes required in such components, often numbering in the hundreds or thousands per part, minimizing drilling time is essential to enhance productivity, reduce tool wear and lower energy consumption. In this work, a natural fiber biocomposite plate, representative of lightweight aerospace materials, was machined using a high-precision CNC center. The drilling process produced two distinct chip types: continuous helical chips at lower parameters from viscoelastic behavior, and fragmented chips at higher parameters caused by shear-induced fiber-matrix separation. The experimental protocol involved 75 holes, divided into three sets of 25, each under varying cutting conditions, to simulate industrial-scale hole production demands. Three drill types were tested: a high-speed steel (HSS) drill, an HSS drill with 5% cobalt, and a solid carbide drill. Through optimized sequencing, machining time was reduced to 55.96&#xa0;s per set while maintaining tight positional accuracy. To achieve this, an optimization method combining the Traveling Salesman Problem (TSP) with a Genetic Algorithm (GA) was implemented. This approach minimized toolpath distance and total drilling time while accounting for material heterogeneity and machining parameters. The results demonstrate how computational optimization can significantly improve productivity in bio-composite machining by reducing cycle times while maintaining quality, directly addressing key aerospace industry requirements.</p>

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Enhanced productivity and chip morphology analysis in natural fiber bio-composite drilling: a computational toolpath optimization via TSP-GA hybrid approach

  • 期刊论文

  • 发表日期：

    2026年1月23日

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  • Mustapha Arslane,
  • Mohamed Slamani,
  • Salah Amroune,
  • Abdelmalek Elhadi,
  • Borhen Louhichi,
  • Mohamad A. Alawad

摘要

Holes are among the most common features in aerospace composite components, serving critical functions such as clearance, fastening, alignment and weight reduction. Given the high quantity of holes required in such components, often numbering in the hundreds or thousands per part, minimizing drilling time is essential to enhance productivity, reduce tool wear and lower energy consumption. In this work, a natural fiber biocomposite plate, representative of lightweight aerospace materials, was machined using a high-precision CNC center. The drilling process produced two distinct chip types: continuous helical chips at lower parameters from viscoelastic behavior, and fragmented chips at higher parameters caused by shear-induced fiber-matrix separation. The experimental protocol involved 75 holes, divided into three sets of 25, each under varying cutting conditions, to simulate industrial-scale hole production demands. Three drill types were tested: a high-speed steel (HSS) drill, an HSS drill with 5% cobalt, and a solid carbide drill. Through optimized sequencing, machining time was reduced to 55.96 s per set while maintaining tight positional accuracy. To achieve this, an optimization method combining the Traveling Salesman Problem (TSP) with a Genetic Algorithm (GA) was implemented. This approach minimized toolpath distance and total drilling time while accounting for material heterogeneity and machining parameters. The results demonstrate how computational optimization can significantly improve productivity in bio-composite machining by reducing cycle times while maintaining quality, directly addressing key aerospace industry requirements.