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<p>The key aspects of this work were the enhancement of the repeatability of the melt flow index (MFI) measurement and improvement of reproducibility for testing certain hygrothermal-sensitive materials, which are sensitive to temperature and water content. The present work was structured into two main parts. Firstly, several test variables affecting MFI were evaluated using a blocked response surface methodology (RSM), and multivariable linear regression was applied to assess the influence of test load, test temperature, drying time, and pre-heat time on MFI of nylon-based thermoplastic. The impact of test variables was analyzed by 20 randomized experiments, including 8 “two-level full factorial run” with 6 “centre run” replicates. The test parameters were optimized based on the RSM result and the ANSYS<sup>®</sup> simulation-based prediction. Later, these process variables were chosen to evaluate repeatability and reproducibility of the test of PA and 20 wt% vCF/PA for verification, which was diagnosed by measurement system analysis (MSA) for consistency and reliability assessment. The optimization test parameters for these composites were pre-heat time 1–3&#xa0;min and drying time 6.6&#xa0;h. By this means, the repeatability and reproducibility of these composites were up to 3.179% and 12.208%, respectively. The findings provide a practical testing framework that reduces uncertainty, enhances credibility, and ensures meaningful use of MFI in process optimization and quality control for polymer composite manufacturing.</p>

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Combining response surface methodology and measurement system analysis to identify melt flow index of hygrothermal-sensitive composites

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月24日

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  • Ning Su,
  • Xiaoling Liu,
  • Xiang Chen,
  • Jiafei Gu,
  • Jing Bai

摘要

The key aspects of this work were the enhancement of the repeatability of the melt flow index (MFI) measurement and improvement of reproducibility for testing certain hygrothermal-sensitive materials, which are sensitive to temperature and water content. The present work was structured into two main parts. Firstly, several test variables affecting MFI were evaluated using a blocked response surface methodology (RSM), and multivariable linear regression was applied to assess the influence of test load, test temperature, drying time, and pre-heat time on MFI of nylon-based thermoplastic. The impact of test variables was analyzed by 20 randomized experiments, including 8 “two-level full factorial run” with 6 “centre run” replicates. The test parameters were optimized based on the RSM result and the ANSYS® simulation-based prediction. Later, these process variables were chosen to evaluate repeatability and reproducibility of the test of PA and 20 wt% vCF/PA for verification, which was diagnosed by measurement system analysis (MSA) for consistency and reliability assessment. The optimization test parameters for these composites were pre-heat time 1–3 min and drying time 6.6 h. By this means, the repeatability and reproducibility of these composites were up to 3.179% and 12.208%, respectively. The findings provide a practical testing framework that reduces uncertainty, enhances credibility, and ensures meaningful use of MFI in process optimization and quality control for polymer composite manufacturing.