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February 21, 2026Procedia Structural Integrity0 citationsOpen Access

Influence of SLM Processing Parameters on Intrinsic Defects and Mechanical Properties

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DNDaniela NevesTMTeresa MorgadoJAJoão Alves

Key Points

  • The study aims to investigate how different SLM processing parameters influence defect generation and mechanical properties.
  • Utilized characterisation techniques like nanotomography, scanning electron microscopy, and optical microscopy.
  • Measured microhardness to assess mechanical properties.
  • Analyzed the impact of metallic powder reuse on defects and material behavior.
  • Identified various defects such as gas pores and lack of fusion zones.
  • Established a relationship between SLM parameters and defect formation.
  • Provided insights on optimizing parameters to enhance mechanical integrity and reduce defects.

Abstract

Selective Laser Melting (SLM) is an advanced 3D printing technology for producing metallic components, offering design versatility, near-net-shape manufacturing, and potential for material efficiency. Despite these advantages, the process is inherently linked to defect formation, such as gas pores, lack of fusion zones, impurities, and issues associated with the keyhole phenomenon. These defects can significantly affect the material’s mechanical integrity, especially its fatigue performance. This study investigates how different SLM processing parameters influence defect generation and their impact on mechanical properties and, consequently, on structural integrity. To accomplish this, a wide range of characterisation techniques, such as nanotomography, scanning electron microscopy (SEM), optical microscopy, and microhardness measurements, were employed to examine both surface and internal features of the components, thereby detecting and assessing the type, size, and distribution of defects. This research additionally explores the potential impact of metallic powder reuse on defect formation and material behaviour, contributing to the discussion on sustainable practices in additive manufacturing. The results provide valuable insights into optimising SLM parameters to reduce internal defects and support more sustainable production strategies.

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Cite This Study

Neves et al. (2026) studied this question.

synapsesocial.com/papers/69994ad4873532290d01f232https://doi.org/10.1016/j.prostr.2025.12.333
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