在增材制造中用于印刷质量预测的光谱特征分析:基于声学的方法
Michael Olowe1,2, Michael Ogunsanya1,2, Brian Best3
1Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
使用机器学习进行3D打印的Fused Deposition Modeling (FDM) 声学分析准确地预测了打印质量. 极端梯度提升实现了91.3%的准确性,突出了增材制造质量控制的声学.
科学领域:
- 材料科学与工程 材料科学与工程
- 制造业 制造技术 制造技术
- 声学和信号处理
背景情况:
- 在增材制造 (AM) 中的质量预测对于航空航天和生物医学等高风险行业至关重要.
- 声学传感提供了一种非破坏性的方法来检测3D打印过程中的变化.
- 沉积建模 (FDM) 是一种广泛使用的AM技术,在这种技术中,质量控制是必不可少的.
研究的目的:
- 研究声学数据分析在FDM中预测3D打印质量的有效性.
- 提取和分析时间和频域声学特征以进行质量分类.
- 实现和比较各种机器学习算法用于基于声学的印刷质量预测.
主要方法:
- 从具有不同层厚度的FDM3D打印样本收集的声学数据流.
- 预处理的音频样本使用波-敲击声源分离 (HPSS) 和提取的光谱特征使用Librosa.
- 采用了八个机器学习分类器 (包括XGBoost,随机森林,SVM) 来根据声学特征预测打印质量.
主要成果:
- 识别了光谱平度,光谱中心点,功率光谱密度和RMS能量作为关键的声学特征.
- 极端梯度提升 (XGBoost) 模型实现了最高的预测准确度 (91.3%),精度 (88.8%),回忆 (92.9%),F1得分 (90.8%),和AUC (96.3%).
- 证明了声学特征在区分印刷品质量级别方面的潜力.
结论:
- 基于声学的分析与机器学习相结合,为FDM 3D打印中的实时质量预测提供了一个强大的方法.
- 这种方法为增材制造中的基于声学的质量控制系统奠定了基础.
- 该方法可以扩展到其他AM技术和各种制造质量评估应用.
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