Scholarlink

<p>Variability in part quality due to spatial positioning remains an open challenge in laser-powder bed fusion (L-PBF). Although optimizing laser scanning parameters remains essential, the influence of print position cannot be neglected as it is influenced by inert gas flow dynamics, recoating strategy, build plate geometry, and laser incidence. To address this gap, a novel data-driven methodology for mapping and optimizing dimensional accuracy and surface texture as functions of print location was proposed. The systematic approach integrates experimental measurements, regression modelling, and multi-objective optimization to generate spatially resolved printability maps and optimize print location. To validate the methodology, two different lasers were used to process 316L stainless steel on a circular-platform L-PBF system. Printability maps revealed clear spatial trends driven by laser deflection and thermal gradients across the powder bed, as well as operational differences between different laser sources. Multi-objective optimization identified central positions as the ones that allow obtaining the specimens with the best dimensional and surface characteristics. Porosity analysis carried out on the specimens printed in those areas revealed uniform porosity percentages across all locations, confirming energy input as the dominant densification factor. Although further studies are needed to improve modelling accuracy, the proposed methodology provides a scalable and adaptable framework for understanding and controlling the influence of build plate geometry and machine configuration on part quality.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Addressing positional and laser source variability in laser powder bed fusion: a mapping method for quality optimization

  • 期刊论文

  • 发表日期：

    2025年7月29日

查看全文
  • Gabriele Locatelli,
  • Mariangela Quarto,
  • Gianluca D’Urso,
  • Claudio Giardini

摘要

Variability in part quality due to spatial positioning remains an open challenge in laser-powder bed fusion (L-PBF). Although optimizing laser scanning parameters remains essential, the influence of print position cannot be neglected as it is influenced by inert gas flow dynamics, recoating strategy, build plate geometry, and laser incidence. To address this gap, a novel data-driven methodology for mapping and optimizing dimensional accuracy and surface texture as functions of print location was proposed. The systematic approach integrates experimental measurements, regression modelling, and multi-objective optimization to generate spatially resolved printability maps and optimize print location. To validate the methodology, two different lasers were used to process 316L stainless steel on a circular-platform L-PBF system. Printability maps revealed clear spatial trends driven by laser deflection and thermal gradients across the powder bed, as well as operational differences between different laser sources. Multi-objective optimization identified central positions as the ones that allow obtaining the specimens with the best dimensional and surface characteristics. Porosity analysis carried out on the specimens printed in those areas revealed uniform porosity percentages across all locations, confirming energy input as the dominant densification factor. Although further studies are needed to improve modelling accuracy, the proposed methodology provides a scalable and adaptable framework for understanding and controlling the influence of build plate geometry and machine configuration on part quality.