摘要
基于深度学习的机械智能制造质量控制是当前工业生产中关注的热点领域。本文系统地探讨了基于深度学习的机械智能制造质量控制的关键技术和主要流程,提出了基于深度学习的机械智能制造质量控制的应用策略,包括基于智能传感器的实时质量监测、基于预测分析的故障检测与预警、基于远程监控的分布式质量控制,以及基于知识图谱的知识推理与决策,以供参考。
Quality control of mechanical intelligent manufacturing based on deep learning is a hot area of concern in current industrial production.This paper systematically discusses the key technologies and main processes of deep learning-based mechanical intelligent manufacturing quality control,and puts forward the application strategies of deep learning-based mechanical intelligent manufacturing quality control,including real-time quality monitoring based on intelligent sensors,fault detection and early warning based on predictive analytics,distributed quality control based on remote monitoring and distributed quality control,as well as knowledge reasoning and decision-making based on knowledge graph,for reference.
作者
袁芬
YUAN Fen(Department of Automation Engineering,Nanjing Institute of Mechanical and Electrical Technology,Nanjing 211306,Jiangsu China)
出处
《锻压装备与制造技术》
2023年第6期128-131,共4页
China Metalforming Equipment & Manufacturing Technology
基金
2023年度江苏高校“青蓝工程”资助
2023年度江苏高校哲学社会科学研究项目研究成果(2023SJYB0826)
南京机电职业技术学院种子基金项目研究成果(LD202301)。
关键词
机械智能制造
质量控制
深度学习
数据处理
实时监测
Mechanical intelligent manufacturing
Quality control
Deep learning
Data processing
Real-time monitoring
