Identification of Grain Oriented SiFe Steels Based on Imaging the Instantaneous Dynamics of Magnetic Barkhausen Noise Using Short-Time Fourier Transform and Deep Convolutional Neural Network

An Evaluation of 3D-Printed Materials' Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data

This article describes an approach to evaluating the structural properties of samples manufactured through 3D printing via active infrared thermography. The mentioned technique was used to test the PETG sample, using halogen lamps as an excitation source. First, a simplified, general numerical model of the phenomenon was prepared; then, the obtained data were used in a process of the deep neural network training. Finally, the network trained in this manner was used for the material evaluation on the basis of the original experimental data. The described methodology allows for the automated assessment of the structural state of 3D-printed materials. The usage of a generalized model is an innovative method that allows for greater product assessment flexibility.

Predicting Mechanical Properties of Cold-Rolled Steel Strips Using Micro-Magnetic NDT Technologies

Multiple micro-magnetic non-destructive testing (NDT) technologies are suitable candidates for predicting the mechanical properties of cold-rolled steel strips. In this work, based on magnetic domain dynamics behavior and magnetization theory, the correlation between electromagnetic characteristics extracted by multiple micro-magnetic NDT technologies and the influence factors was investigated. It was found that temperature and tension can subsequently affect the electromagnetic parameters by altering the domain structure and domain walls’ motion properties. Pearson’s correlation coefficients were employed to reflect the dependence of micromagnetic characteristics on influencing factors. The lift-off was determined as the largest influence factor among influence factors. A pseudo-static detection was reached by polynomial fitting, which could eliminate the influence of lift-off on the detection results. The number of training models was optimized, and the detection accuracy was improved via the improved Generalized Regression Neural Network (GRNN) model, based on the Gaussian Mixture Clustering (GMC) algorithm.