PDF(Pubmed)
Abstract：
Several advantages of directed energy deposition-arc (DED-arc) have garnered considerable research attention including high deposition rates and low costs. However, defects such as discontinuity and pores may occur during the manufacturing process. Defect identification is the key to monitoring and quality assessments of the additive manufacturing process. This study proposes a novel acoustic signal-based defect identification method for DED-arc via wavelet time-frequency diagrams. With the continuous wavelet transform, one-dimensional (1D) acoustic signals acquired in situ during manufacturing are converted into two-dimensional (2D) time-frequency diagrams to train, validate, and test the convolutional neural network (CNN) models. In this study, several CNN models were examined and compared, including AlexNet, ResNet-18, VGG-16, and MobileNetV3. The accuracy of the models was 96.35%, 97.92%, 97.01%, and 98.31%, respectively. The findings demonstrate that the energy distribution of normal and abnormal acoustic signals has significant differences in both the time and frequency domains. The proposed method is verified to identify defects effectively in the manufacturing process and advance the identification time.
摘要：
定向能量沉积电弧（DED电弧）的几个优点已经引起了相当多的研究关注，包括高沉积速率和低成本。然而,在制造过程中可能会出现不连续和气孔等缺陷。缺陷识别是增材制造过程监控和质量评估的关键。本研究提出了一种新的基于声信号的DED电弧缺陷识别方法，通过小波时频图。用连续小波变换,制造过程中现场采集的一维(1D)声信号被转换成二维(2D)时频图,验证,并测试卷积神经网络(CNN)模型。在这项研究中,对几个CNN模型进行了检查和比较，包括AlexNet,ResNet-18、VGG-16和MobileNetV3。模型的准确率为96.35%,97.92%,97.01%,98.31%,分别。研究结果表明，正常和异常声信号的能量分布在时域和频域上都有显著差异。验证了所提出的方法可以有效地识别制造过程中的缺陷，并提前了识别时间。
