Advanced Data-Driven Manufacturing

被引：0

作者：

Gaudin, Theophile ^{[1
]}

Schilter, Oliver ^{[1
]}

Zipoli, Federico ^{[1
]}

Laino, Teodoro ^{[1
]}

机构：

[1] IBM Res Europe, Zurich, Switzerland

来源：

ERCIM NEWS | 2020年 / 122期

关键词：

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In many material manufacturing processes nowadays a large amount of data is created and stored, often without utilizing them to the full potential because of their complexity. Applying state of the art deep learning techniques can be a powerful tool to extract knowledge out of them allowing to get useful insights. In this work we present autoencoder-based machine learning models to find links among composition, properties and processes applied to two prototypical industrial applications.

引用

页码：45 / 46

页数：2