Strong classification system for wear identification on milling processes using computer vision and ensemble learning-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Strong classification system for wear identification on milling processes using computer vision and ensemble learning

Published:2021-10 Issue: Volume:456 Page:678-684
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Riego Virginia,Castejón-Limas Manuel,Sánchez-González Lidia,Fernández-Robles Laura,Perez Hilde,Diez-Gonzalez Javier,Guerrero-Higueras Ángel-Manuel

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference31 articles.

1. Intelligent and vision-based fire detection systems: a survey;Bu;Image Vis. Comput.,2019

2. Deep learning to improve breast cancer detection on screening mammography;Shen;Sci. Rep.,2019

3. An intelligent vision-based system applied to visual quality inspection of beans;Belan,2016

4. A semi-supervised laplacian extreme learning machine and feature fusion with cnn for industrial superheat identification;Lei;Neurocomputing,2020

5. Multi-level awareness of energy used in production processes;Shrouf;J. Cleaner Prod.,2017

Cited by 19 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A novel system for the classification of zinc-plated components by benchmarking deep neural networks;Expert Systems with Applications;2024-12

2. Defining a feature-level digital twin process model by extracting machining features from MBD models for intelligent process planning;Journal of Intelligent Manufacturing;2024-05-14

3. A Generative AI approach to improve in-situ vision tool wear monitoring with scarce data;Journal of Intelligent Manufacturing;2024-05-10

4. Feasible spindle speed interval identification method for large aeronautical component robotic milling system;Mechatronics;2024-05

5. Optimized network for detecting burr-breakage in images of milling workpieces;Logic Journal of the IGPL;2024-03-22

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3