Identifying grain size in ASTM A36 steel using ultrasonic backscattered signals and machine learning

Abstract

Ultrasonic nondestructive techniques can be useful tools in the microstructural classification of metallic alloys. Among the most common techniques for characterizing materials, backscattered signal analysis stands out because it does not require a back surface echo. This study classifies five ASTM A36 steel samples with varying grain sizes using ultrasonic waves. For each sample, ultrasound signals were obtained using two ultrasonic array probes with center frequencies of 5 and 10 MHz. An extensive feature engineering process was conducted using the tfresh package in Python, which extracts a wide range of features from time series data, transforming raw ultrasound signals into a structured format. Seven machine learning models were evaluated based on accuracy, precision, recall, and F1 score, with performances ranging from 70 to 100%. The XGBoost model exhibited the best performance with 10 MHz probe signals, achieving 100% accuracy. An additional validation test was conducted to evaluate the models’ generalization capability, considering inter-specimen variabilities. Despite a slight reduction in prediction metrics, the XGBoost model maintained good performance, with accuracy between 89% and 100% across all frequencies. Such findings demonstrate that backscattered grain noise can effectively identify grain sizes provided that an adequate machine learning model is used.