4.7 Article

Predicting compressive strength of consolidated molecular solids using computer vision and deep learning

期刊

MATERIALS & DESIGN
卷 190, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.matdes.2020.108541

关键词

Mechanical performance prediction; Image analysis; Random forest; Deep neural network; Machine learning

资金

  1. U.S. Department of Energy by Lawrence Livermore National Laboratory [DE-AC52-07NA27344]
  2. LLNL-LDRD Program [19-SI-001]

向作者/读者索取更多资源

We explore the application of computer vision and machine learning (ML) techniques to predict material properties (e.g., compressive strength) based on SEM images. We show that it's possible to train ML models to predict materials performance based on SEM images alone, demonstrating this capability on the real-world problem of predicting uniaxially compressed peak stress of consolidated molecular solids samples. Our image-based ML approach reduces mean absolute percentage error (MAPE) by an average of 24% over baselines representative of the current state-of-the-practice (i.e., domain-expert's analysis and correlation). We compared two complementary approaches to this problem: (1) a traditional ML approach, random forest (RF), using state-of-the-art computer vision features and (2) an end-to-end deep learning (DL) approach, where features are learned automatically from raw images. We demonstrate the complementarity of these approaches, showing that RF performs best in the small data regime in which many real-world scientific applications reside (up to 24% lower RMSE than DL), whereas DL outpaces RF in the big data regime, where abundant training samples are available (up to 24% lower RMSE than RF). Finally, we demonstrate that models trained using machine learning techniques are capable of discovering and utilizing informative crystal attributes previously underutilized by domain experts. (C) 2020 The Authors. Published by Elsevier Ltd.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据