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<p>Selective laser melting (SLM) has increasingly been adopted to produce titanium alloy components across various industries. However, a significant challenge associated with SLM is the high surface roughness of the resulting parts. To address this issue and enhance the mechanical and tribological properties of titanium, silicon carbide (SiC) particles are incorporated into the titanium matrix, resulting in the formation of Ti-SiC composites. The addition of SiC significantly improves the hardness, wear resistance, and overall performance of the material. This study presents a novel optimization framework for fabricating Ti-SiC composites via SLM, focusing on the influence of critical process parameters, including laser power, scanning speed, and SiC content. A full factorial design analysis, combined with empirical modeling and an analysis of variance, was conducted using response surface methodology to evaluate the relationships between these variables. To further refine the optimization, the non-dominated sorting genetic algorithm II, employing a crowding distance approach, was implemented. The optimal solutions were subsequently ranked using the Technique for Order of Preference by Similarity to Ideal Solution, ensuring a comprehensive evaluation and selection of the best outcomes, which yielded favorable mechanical properties in the materials. This research provides valuable insights into the optimization of SLM processes for Ti-SiC composites, with practical implications for industries such as automotive and aerospace, where high-performance titanium alloy components are essential.</p>

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Multi-objective Optimization of the Surface Roughness of Ti-SiC Composites Using NSGA-II Based on TOPSIS and Box–Behnken Design

  • 期刊论文

  • 发表日期：

    2025年5月9日

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  • Jun Fang,
  • Yong Chai Tan,
  • Vin Cent Tai,
  • Shamini Janasekaran,
  • Dongsheng Wang,
  • Youwen Yang,
  • Yingmang Fan

摘要

Selective laser melting (SLM) has increasingly been adopted to produce titanium alloy components across various industries. However, a significant challenge associated with SLM is the high surface roughness of the resulting parts. To address this issue and enhance the mechanical and tribological properties of titanium, silicon carbide (SiC) particles are incorporated into the titanium matrix, resulting in the formation of Ti-SiC composites. The addition of SiC significantly improves the hardness, wear resistance, and overall performance of the material. This study presents a novel optimization framework for fabricating Ti-SiC composites via SLM, focusing on the influence of critical process parameters, including laser power, scanning speed, and SiC content. A full factorial design analysis, combined with empirical modeling and an analysis of variance, was conducted using response surface methodology to evaluate the relationships between these variables. To further refine the optimization, the non-dominated sorting genetic algorithm II, employing a crowding distance approach, was implemented. The optimal solutions were subsequently ranked using the Technique for Order of Preference by Similarity to Ideal Solution, ensuring a comprehensive evaluation and selection of the best outcomes, which yielded favorable mechanical properties in the materials. This research provides valuable insights into the optimization of SLM processes for Ti-SiC composites, with practical implications for industries such as automotive and aerospace, where high-performance titanium alloy components are essential.