在纤维增强热塑性复合材料的材料挤出增材制造中检测多层次缺陷:对挑战和先进的非破坏性测试技术的审查
Demeke Abay Ashebir1,2, Andreas Hendlmeier2, Michelle Dunn3
1School of Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia.
Polymers
|November 9, 2024
概括
纤维增强热塑性复合材料 (FRTPC) 的制造缺陷阻碍了性能. 具有机器学习和自我报告能力的先进非破坏性测试 (NDT) 为可靠的增材制造提供了更好的缺陷检测.
科学领域:
- 材料科学 材料科学 材料科学
- 制造业 工程 制造工程
- 复合材料 复合材料 复合材料
背景情况:
- 纤维增强热塑性复合材料 (FRTPCs) 的增材制造 (AM) 面临着诸如毛孔性和分层等缺陷的挑战.
- 这些缺陷显著降低机械性能 (拉力强度,疲劳寿命) 并损害结构完整性.
- 传统的非破坏性测试 (NDT) 方法通常不足以检测FRTPC的多尺度缺陷,原因是分辨率,透率和异质性问题.
研究的目的:
- 批判性地审查通过化丝制造 (FFF) 制造的FRTPC的制造缺陷.
- 探索先进的NDT技术和结构健康监测 (SHM) 以检测缺陷.
- 调查机器学习 (ML) 和自我报告能力的整合,以提高缺陷检测和可靠性.
主要方法:
- 基于形态,位置和大小的FFF诱导缺陷的分类.
- 检查先进的NDT技术,包括微型计算机断层扫描 (微型CT).
- 讨论具有自我传感纤维和ML算法的结构健康监测 (SHM) 系统.
主要成果:
- 诸如多孔性之类的缺陷可以达到10-15%,将拉伸强度降低高达30%,疲劳寿命降低20%.
- 先进的NDT,如微型CT,可以检测到10微米以下的空隙.
- 与传统方法相比,ML集成提高了25-30%的NDT缺陷检测灵敏度.
结论:
- 将ML增强的NDT与自报告FRTPC集成,可以显著提高缺陷检测的准确性和效率.
- 这种方法可以生产更可靠,缺陷最小的FRTPC组件,用于苛刻的应用.
- 增强的缺陷检测对于AM在航空航天等行业的更广泛采用至关重要.
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