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<p>This study investigates the machinability of a novel origami-inspired 3D printed hybrid composite material composed of polyamide 6 (PA6) and polylactic acid (PLA), using Abrasive Water Jet Machining (AWJM). The research addresses the challenge of efficiently machining complex composite structures while maintaining high surface quality and material removal rates. The key machining parameters (water pressure, traverse speed, and standoff distance) were optimized using the Teaching–Learning-Based Optimization (TLBO) algorithm, selected for its effectiveness in handling complex, multi-objective problems. Experimental trials, guided by a Taguchi L9 design, evaluated surface roughness, material removal rate (MRR), and machining time. The optimized conditions resulted in significantly improved surface quality, enhanced MRR, and reduced machining time. Statistical validation through regression modeling and ANOVA confirmed the reliability of the findings. This work contributes valuable insights into the integration of bio-based hybrid composites, advanced infill design, and modern machining optimization techniques for high-performance manufacturing applications.</p> Graphical abstract <p></p>

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Analysing the machinability of origami inspired 3D printed pattern PA6-PLA hybrid composite material by using TLBO technique

  • 期刊论文

  • 发表日期：

    2025年5月25日

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  • Rajashakar Reddy Jambula,
  • Madhu Salumari,
  • Madan Mohan Reddy Nune

摘要

This study investigates the machinability of a novel origami-inspired 3D printed hybrid composite material composed of polyamide 6 (PA6) and polylactic acid (PLA), using Abrasive Water Jet Machining (AWJM). The research addresses the challenge of efficiently machining complex composite structures while maintaining high surface quality and material removal rates. The key machining parameters (water pressure, traverse speed, and standoff distance) were optimized using the Teaching–Learning-Based Optimization (TLBO) algorithm, selected for its effectiveness in handling complex, multi-objective problems. Experimental trials, guided by a Taguchi L9 design, evaluated surface roughness, material removal rate (MRR), and machining time. The optimized conditions resulted in significantly improved surface quality, enhanced MRR, and reduced machining time. Statistical validation through regression modeling and ANOVA confirmed the reliability of the findings. This work contributes valuable insights into the integration of bio-based hybrid composites, advanced infill design, and modern machining optimization techniques for high-performance manufacturing applications.

Graphical abstract