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<p>The optimization of manufacturing processes and measurement systems is conventionally addressed through separate, isolated analyses, which can overlook critical interactions and lead to suboptimal overall performance. This study introduces an integrated strategy for the simultaneous optimization of both systems within the context of Fused Deposition Modeling, an additive manufacturing technology. The methodology employs a Response Surface Methodology based on a Central Composite Design to model the effects of key process input variables: layer width, layer height, infill density, and print speed on system performance. The manufacturing process capability was evaluated using the accuracy index (C<sub>a</sub>), while the measurement system was assessed using the capability index for repeatability and bias (C<sub>gk</sub>). Multi-objective optimization using desirability functions identified an optimal parameter configuration: a layer width of 0.6&#xa0;mm, layer height of 0.28&#xa0;mm, infill density of 90%, and a print speed of 50&#xa0;mm/s. This configuration resulted in a quantitatively superior performance, elevating the process from initial "Poor" classifications (e.g., a Ca index as low as -2.37) to a final "Excellent" state for both the manufacturing process (Ca &gt; 0.99) and the measurement system (Cgk &gt; 1.84). The results demonstrate that this integrated optimization strategy is superior to traditional sequential methods. By providing a more holistic and efficient pathway to robust quality control, the proposed framework enables the achievement of globally optimal process configurations, significantly reducing experimental costs and improving the reliability of quality assessments in additive manufacturing.</p>

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Simultaneous optimization of measurement system and operational process: an additive manufacturing application

  • 期刊论文

  • 发表日期：

    2025年11月5日

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  • Francisco Tiago Araújo Barbosa,
  • Lucas Guedes de Oliveira,
  • Matheus Cunha de Almeida,
  • Paulo Rotella Junior,
  • Rogério Santana Peruchi

摘要

The optimization of manufacturing processes and measurement systems is conventionally addressed through separate, isolated analyses, which can overlook critical interactions and lead to suboptimal overall performance. This study introduces an integrated strategy for the simultaneous optimization of both systems within the context of Fused Deposition Modeling, an additive manufacturing technology. The methodology employs a Response Surface Methodology based on a Central Composite Design to model the effects of key process input variables: layer width, layer height, infill density, and print speed on system performance. The manufacturing process capability was evaluated using the accuracy index (Ca), while the measurement system was assessed using the capability index for repeatability and bias (Cgk). Multi-objective optimization using desirability functions identified an optimal parameter configuration: a layer width of 0.6 mm, layer height of 0.28 mm, infill density of 90%, and a print speed of 50 mm/s. This configuration resulted in a quantitatively superior performance, elevating the process from initial "Poor" classifications (e.g., a Ca index as low as -2.37) to a final "Excellent" state for both the manufacturing process (Ca > 0.99) and the measurement system (Cgk > 1.84). The results demonstrate that this integrated optimization strategy is superior to traditional sequential methods. By providing a more holistic and efficient pathway to robust quality control, the proposed framework enables the achievement of globally optimal process configurations, significantly reducing experimental costs and improving the reliability of quality assessments in additive manufacturing.