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Purpose <p>This study explores the optimization of processing parameters in Additive manufacturing (AM) to enhance the mechanical and thermal properties of fibre-reinforced polymer composite components. The research aims to improve tensile strength, thermal stability, and durability for aerospace applications.</p> Methodology <p>The effects of critical processing parameters such as layer thickness, raster angle, and infill density on AM-fabricated composite performance were analyzed. An opposition-based learning cat and mouse-based optimizer (OLCMBO) was applied to optimize extrusion temperature and printing velocity. The optimization process was conducted over 20 iterations to achieve superior material properties.</p> Findings <p>Compared to existing optimization approaches, ant colony optimization (ACO) demonstrates modest results, with a final tensile strength of only 85&#xa0;MPa, which is significantly lower than the 125&#xa0;MPa achieved by the Genetic Algorithm (GA) and 140&#xa0;MPa by OLCMBO. In terms of thermal stability, ACO reaches just 440&#xa0;°C, while OLCMBO achieves 580&#xa0;°C, showing a 31.8% improvement. ACO levels off at a durability of 85%, whereas OLCMBO reaches 99.5%. Although ACO performs well in terms of cost reduction, achieving an overall 85% decrease, this comes at the expense of structural and thermal performance, highlighting OLCMBO’s superior balance in high-performance engineering applications.</p>

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Assessing Additive Manufacturing Techniques for Polymer and Metal Matrix Composites in Aerospace Applications

  • 期刊论文

  • 发表日期：

    2025年7月24日

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  • A. Anu Kuttan,
  • Ankush B. Khansole,
  • A. Sri Harsha,
  • R. Prabhakaran,
  • Kallepalli Santosh Kumar,
  • B. Iftekhar Hussain

摘要

Purpose

This study explores the optimization of processing parameters in Additive manufacturing (AM) to enhance the mechanical and thermal properties of fibre-reinforced polymer composite components. The research aims to improve tensile strength, thermal stability, and durability for aerospace applications.

Methodology

The effects of critical processing parameters such as layer thickness, raster angle, and infill density on AM-fabricated composite performance were analyzed. An opposition-based learning cat and mouse-based optimizer (OLCMBO) was applied to optimize extrusion temperature and printing velocity. The optimization process was conducted over 20 iterations to achieve superior material properties.

Findings

Compared to existing optimization approaches, ant colony optimization (ACO) demonstrates modest results, with a final tensile strength of only 85 MPa, which is significantly lower than the 125 MPa achieved by the Genetic Algorithm (GA) and 140 MPa by OLCMBO. In terms of thermal stability, ACO reaches just 440 °C, while OLCMBO achieves 580 °C, showing a 31.8% improvement. ACO levels off at a durability of 85%, whereas OLCMBO reaches 99.5%. Although ACO performs well in terms of cost reduction, achieving an overall 85% decrease, this comes at the expense of structural and thermal performance, highlighting OLCMBO’s superior balance in high-performance engineering applications.