Semantic Scholar Open Access 2019 438 sitasi

Using deep neural network with small dataset to predict material defects

Shuo Feng Huiyu Zhou Hongbiao Dong

Lihat Sumber DOI

Abstrak

Abstract Deep neural network (DNN) exhibits state-of-the-art performance in many fields including microstructure recognition where big dataset is used in training. However, DNN trained by conventional methods with small datasets commonly shows worse performance than traditional machine learning methods, e.g. shallow neural network and support vector machine. This inherent limitation prevented the wide adoption of DNN in material study because collecting and assembling big dataset in material science is a challenge. In this study, we attempted to predict solidification defects by DNN regression with a small dataset that contains 487 data points. It is found that a pre-trained and fine-tuned DNN shows better generalization performance over shallow neural network, support vector machine, and DNN trained by conventional methods. The trained DNN transforms scattered experimental data points into a map of high accuracy in high-dimensional chemistry and processing parameters space. Though DNN with big datasets is the optimal solution, DNN with small datasets and pre-training can be a reasonable choice when big datasets are unavailable in material study.

Topik & Kata Kunci

Materials Science

Penulis (3)

Shuo Feng

Huiyu Zhou

Hongbiao Dong

Format Sitasi

APA MLA BibTeX

Feng, S., Zhou, H., Dong, H. (2019). Using deep neural network with small dataset to predict material defects. https://doi.org/10.1016/J.MATDES.2018.11.060

Akses Cepat

Lihat di Sumber doi.org/10.1016/J.MATDES.2018.11.060

Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 438×
Sumber Database: Semantic Scholar
DOI: 10.1016/J.MATDES.2018.11.060
Akses: Open Access ✓