摘要
针对装配车间数据源分散、装配信息不能及时感知、数据采集不精确等问题,提出了一种车间数字孪生监控方法。首先,组合使用层次分析法和熵权法,选取关键监控项目,并基于客户端/服务器架构搭建了信息传递网络;其次,提出一种粒子群PSO-SG(particle swarm optimizationSavitzky-Golay)滤波数据处理方法,即利用粒子群算法优化SG滤波算法的参数,提高数据处理效果;然后,基于有限状态机理论构建了数字孪生监控平台,采用二维的数据可视化看板与三维虚拟模型相结合的方式,建立起装配车间的监控体系;最后,以某船用柴油机关键件自动化装配产线中的一个站位为例,验证了本研究的可行性。
A digital twin monitoring method for assembly workshops was proposed to address issues such as dispersed data sources,untimely perception of assembly information,and inaccurate data collection.Initially,key monitoring items were selected using a combination of the Analytic Hierarchy Process(AHP)and the Entropy Weight Method,and an information transmission network was established based on a client/server architecture.Secondly,a particle swarm optimization-Savitzky-Golay(PSOSG)filtering data processing method is proposed,where the parameters of the Savitzky-Golay(SG)filter are optimized using the PSO algorithm to improve the data processing performance.Furthermore,a digital twin monitoring platform was constructed based on finite state machine theory,integrating twodimensional data visualization dashboards with three-dimensional virtual models to establish a comprehensive monitoring system for the assembly workshop.Finally,the feasibility of this research was validated through an example involving a workstation within an automated assembly line for key components of a marine diesel engine.
作者
冯炳玉
张春燕
张胜文
白正清
FENG Bingyu;ZHANG Chunyan;ZHANG Shengwen;BAI Zhengqing(School of Mechanical Engineering,Jiangsu University of Science and Technology,Zhenjiang 212100,CHN)
出处
《制造技术与机床》
北大核心
2025年第7期207-216,共10页
Manufacturing Technology & Machine Tool
