Prediction of micro-hardness in thread rolling of St37 by convolutional neural networks and transfer learning.

ABSTRACT: This study introduces a non-destructive method by applying convolutional neural networks (CNN) to predict the micro-hardness of the thread-rolled steel. Material microstructure images were collected for our research, and micro-hardness tests were conducted to label the extracted microstructure images. In recent years, researchers have used machine learning (ML) and deep learning (DL) models to predict material properties for forming, machining, additive manufacturing, and other processes. However, they encountered industrial limitations primarily because of the absence of historical information on new and unknown materials, which are necessary to predict material properties by DL models. These problems can be solved by employing CNN models. In our work, we used a CNN model with two convolu

SUBMITTER: Soleymani M 

PROVIDER: S-EPMC9646279 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature