Engineering topics

Willard, Matthew A.

Publications and source records attributed to Willard, Matthew A..

3 records

DOE OSTI2024

Exploring 2D X-ray diffraction phase fraction analysis with convolutional neural networks: Insights from kinematic-diffraction simulations

Abstract Deep-learning models are effective for analyzing the complex information in 2D X-ray diffraction (XRD) patterns. Accurately collecting parameters of the material sample is crucial during model training, significantly impacting model performance. In this study, we employ a kinematic-diffraction simulator to generate simulated 2D XRD patterns for Ti–6Al–4V alloy, allowing precise control of sample parameters. These simulated patterns are used to train convolutional neural networks, predicting $$\upbeta$$ β -phase volume fractions. The training data set consists exclusively of 2D XRD patterns with pure $$\upalpha$$ α - or pure $$\upbeta$$ β -phase, while the testing set incorporates patterns with intermediate phase volume fraction. In particular, we investigate how the architectures of the model influence prediction reliability and computational performance. Experimental results reveal that, with appropriate training, the convolutional neural network accurately detects intermediate phase volume fractions even trained with only pure-phase patterns, achieving a mean square error accuracy of $$9.4 \times 10^{-4}$$ 9.4 × 10 - 4 . Graphical abstract

Yue, Weiqi↗

DOE OSTI2024

Phase Identification in Synchrotron X-ray Diffraction Patterns of Ti–6Al–4V Using Computer Vision and Deep Learning

X-ray diffraction patterns contain information about the atomistic structure and microstructure (defect population) of materials, extracting detailed information from diffraction patterns is complex, demanding and relies on prior knowledge. Here, we hypothesize that deep-learning techniques can help to perform an effective and accurate analysis with high throughput rates. To demonstrate this concept, we applied a novel deep learning framework to determine the evolution of the β-phase volume fraction in a Ti–6Al–4V alloy during heat-treatment from video sequences of 2D diffraction patterns recorded in transmission and with highly monochromatic radiation in a synchrotron beamline. In particular, we studied the impact of network design on prediction reliability and computational performance. Networks of different architectures were trained using 3008 experimental 2D patterns. A well-tuned model was found to reproduce the phase fractions of another experimental data set, consisting of 1100 diffraction patterns, with a mean-square error as small as 2.6 x 10 -4 . The average prediction error of β-phase volume fraction was within 1.6 x 10 -2 (in each diffraction pattern) of the values obtained by conventional methods. Our work demonstrates that convolutional neural networks can evaluate high energy X-ray diffraction patterns with a remarkable level of reliability. Furthermore, it demonstrates the significance of network design on the reliability of predictions and computational performance. The most complex models do not necessarily result in highest accuracy and may even fail to learn from the data.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Structural and magnetic properties of magnetostrictive Fe-Ga-Zr nanocrystalline alloy

(Fe 1–x Ga x ) 92 Zr 8 amorphous and nanocrystalline alloys with x = 0.15 to x = 0.36 were investigated to improve the magnetic softness of Galfenol-type alloys and to evaluate their magnetostrictive properties. The samples were prepared by melt spinning of arc melted ingots. The rapidly solidified ribbons were annealed at 823 K for 1 h to produce a nanocrystalline structure. X-ray Diffraction data showed that after annealing, body-centered cubic (BCC) Fe-Ga phase crystallized for x = 0.15 to x = 0.26. When x exceeded 0.26, the ribbons crystallized into BCC and a ternary intermetallic phase (i.e., ZrFe 6 Ga 6 ) which has deteriorating effect on saturation magnetization, magnetic softness and magnetostriction coefficient. The annealed ribbons’ saturation magnetization value decreases from 126 Am 2 /kg to 54 Am 2 /kg as the Ga content increased from x = 0.15 to x = 0.36. The alloy with x = 0.26 annealed at 823 K for 1 h shows a peak magnetostriction of 10 ppm with saturation magnetization of 110 Am 2 /kg and coercivity of 260 A/m.

36 MATERIALS SCIENCE↗