Department of Chemistry, Washington State University, Pullman, WA, 99164, USA.
Alexandra Navrotsky Institute for Experimental Thermodynamics, Washington State University, Pullman, WA, 99164, USA.
Sci Rep. 2023 Apr 11;13(1):5919. doi: 10.1038/s41598-023-33046-w.
We used deep-learning-based models to automatically obtain elastic moduli from resonant ultrasound spectroscopy (RUS) spectra, which conventionally require user intervention of published analysis codes. By strategically converting theoretical RUS spectra into their modulated fingerprints and using them as a dataset to train neural network models, we obtained models that successfully predicted both elastic moduli from theoretical test spectra of an isotropic material and from a measured steel RUS spectrum with up to 9.6% missing resonances. We further trained modulated fingerprint-based models to resolve RUS spectra from yttrium-aluminum-garnet (YAG) ceramic samples with three elastic moduli. The resulting models were capable of retrieving all three elastic moduli from spectra with a maximum of 26% missing frequencies. In summary, our modulated fingerprint method is an efficient tool to transform raw spectroscopy data and train neural network models with high accuracy and resistance to spectra distortion.
我们使用基于深度学习的模型，自动从共振超声光谱（RUS）谱中获取弹性模量，而传统方法需要用户干预已发布的分析代码。通过将理论 RUS 谱有策略地转换为调制指纹，并将其用作数据集来训练神经网络模型，我们获得了成功预测各向同性材料理论测试谱和测量的钢 RUS 谱中高达 9.6%缺失共振的弹性模量的模型。我们进一步训练基于调制指纹的模型，以解析具有三个弹性模量的钇铝石榴石（YAG）陶瓷样品的 RUS 谱。由此产生的模型能够从具有最大 26%缺失频率的光谱中检索所有三个弹性模量。总之，我们的调制指纹方法是一种有效的工具，可以转换原始光谱数据，并使用高精度和对光谱失真的抵抗力来训练神经网络模型。
