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使用增强模糊认知AI和制造过程参数预测复合高压容器中的多孔性

Lina Achour^1,2, Zyed Zalila^3,4, Zoheir Aboura⁵

¹Roberval Université de Technologie de Compiègne Royallieu Research Center, Compiègne, CS 60319-60203, France. lina.achour@utc.fr.

Scientific reports

|February 19, 2026

概括

这项研究使用人工智能来预测高压储容器中的多孔性. 可解释的AI规则识别了减少缺陷的制造参数,提高了复合材料质量和安全性.

科学领域:

材料科学材料科学材料科学
机械工程机械工程
人工智能的人工智能

背景情况:

高压储容器 (HPV) 对于能源至关重要,但易受孔性等内部缺陷的影响.
孔隙性可能会损害碳纤维增强复合HPV的结构完整性,造成安全风险.

研究的目的:

使用XTRACTIS (XTS) 人工智能系统预测IV型HPV的多孔性数量和速率类别.
确定影响孔隙形成的关键制造变量,并开发可解释的质量控制模型.

主要方法:

应用XTRACTIS (XTS),一个通用推理AI,用于复合HPV的多孔性预测建模.
模糊IF...THEN规则的开发由XTS进行,在58个制造变量中选择15个来建模孔隙数.
使用RMSE对模型性能进行评估,并在外部测试数据集上进行相关性.

主要成果:

XTS成功地模拟了孔径数的log10的RMSE为7.94%和0.824.8的相关性.
可解释的规则显示,统一的纤维张力,优化的旋速度和控制的绕参数可以降低孔隙性.
人工智能识别了关键的工艺缺陷关系,突出了纤维张力,绕角度和层体积的影响.

结论:

增强的模糊认知AI (XTS) 有效地揭示了复合材料制造中的可解释的,与领域相关的相互作用.
这些发现支持加强质量控制和流程优化,以减少HPV的缺陷.
了解这些相互作用对于确保储能系统的安全性和可靠性至关重要.

关键词:

增强的模糊认知AI.一般推理人工智能AI 高压容器 (HPV) 是一种多孔性多孔性射线断层扫描X射线断层扫描

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