.png){kind=icon}
返回
Machine learning-based image processing for on-line defect recognition in additive manufacturing
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cirp.2019.03.021.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A machine learning approach for on-line fault recognition via automatic image processing is developed to timely identify material defects due to process non-conformities in Selective Laser Melting (SLM) of metal powders. In-process images acquired during the layer-by-layer SLM processing are analyzed via a bi-stream Deep Convolutional Neural Network-based model, and the recognition of SLM defective condition-related pattern is achieved by automated image feature learning and feature fusion. Experimental evaluations confirmed the effectiveness of the machine learning method for on-line detection of defects due to process non-conformities, providing the basis for adaptive SLM process control and part quality assurance. (C) 2019 Published by Elsevier Ltd on behalf of CIRP.
Keyword:
Machine learning
Additive manufacturing
Fault recognition
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
3.6
论文数:
3.4K
被引数:
1.3W
机构
引用论文
Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing.png)
MATERIALS & DESIGN
OAAI
MATERIALS & DESIGNIF7.9
OAAI分享收藏Feature representation for statistical-learning-based object detection: A reviewPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection面向工业检测自动特征提取的深度卷积神经网络架构设计
PREAI
分享收藏Deep learning-based human motion recognition for predictive context-aware human-robot collaboration基于深度学习的预测上下文感知人机协作的人体运动识别
PREAI
分享收藏Spatter formation in selective laser melting process using multi-laser technology使用多激光技术在选择性激光熔化过程中飞溅的形成MATERIALS & DESIGN
MATERIALS & DESIGNIF7.9
PREAI
分享收藏
查看更多内容