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Abstract <p>Selective Laser Melting (SLM) is an additive manufacturing process used to fabricate complex metallic components by building them layer by layer. In this technique, a high-energy laser serves as the heat source, and fine metal powder is used as the raw material. However, balancing part quality and energy efficiency remains challenging. This study presents an integrated optimization framework to enhance energy efficiency, microhardness, and surface roughness in SLM. Design of experiments is employed to systematically plan and analyze the experiments. Analysis of Variance identifies the influence of key process parameters, while regression models establish quantitative relationships between inputs and responses. These models are integrated with a Multi-Objective Particle Swarm Optimization algorithm in MATLAB to obtain Pareto-optimal solutions. Validation experiments confirm notable improvements in both energy efficiency and mechanical performance.</p> Graphical abstract <p></p>

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Multi-objective optimization of selective laser melting parameters for enhanced energy efficiency, surface quality and microhardness

  • 期刊论文

  • 发表日期：

    2026年5月26日

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  • R. Swetha,
  • L. Siva Rama Krishna,
  • A. Manmadhachary

摘要

Abstract

Selective Laser Melting (SLM) is an additive manufacturing process used to fabricate complex metallic components by building them layer by layer. In this technique, a high-energy laser serves as the heat source, and fine metal powder is used as the raw material. However, balancing part quality and energy efficiency remains challenging. This study presents an integrated optimization framework to enhance energy efficiency, microhardness, and surface roughness in SLM. Design of experiments is employed to systematically plan and analyze the experiments. Analysis of Variance identifies the influence of key process parameters, while regression models establish quantitative relationships between inputs and responses. These models are integrated with a Multi-Objective Particle Swarm Optimization algorithm in MATLAB to obtain Pareto-optimal solutions. Validation experiments confirm notable improvements in both energy efficiency and mechanical performance.

Graphical abstract