Aircraft final assembly line(AFAL)involves thousands of processes that must be completed before delivery.However,the heavy reliance on manual labor in most assembly processes affects the quality and prolongs the deliv...Aircraft final assembly line(AFAL)involves thousands of processes that must be completed before delivery.However,the heavy reliance on manual labor in most assembly processes affects the quality and prolongs the delivery time.While the advent of artificial intelligence of things(AIoT)technologies has introduced advancements in certain AFAL scenarios,systematically enhancing the intelligence level of the AFAL and promoting the widespread deployment of artificial intelligence(AI)technologies remain significant challenges.To address these challenges,we propose the intelligent and collaborative aircraft assembly(ICAA)framework,which integrates AI technologies within a cloud-edge-terminal architecture.The ICAA framework is designed to support AI-enabled applications in the AFAL,with the goal of improving assembly efficiency at both individual and multiple process levels.We analyze specific demands across various assembly scenarios and introduce corresponding AI technologies to meet these demands.The three-tier ICAA framework consists of the assembly field,edge data platform,and assembly cloud platform,facilitating the collection of heterogeneous terminal data and the deployment of AI technologies.The framework enhances assembly efficiency by reducing reliance on manual labor for individual processes and fostering collaboration across multiple processes.We provide detailed descriptions of how AI functions at each level of the framework.Furthermore,we apply the ICAA framework to a real AFAL,focusing explicitly on the flight control system testing process.This practical implementation demonstrates the effectiveness of the framework in improving assembly efficiency and promoting the adoption of AIoT technologies.展开更多
为了实现飞机总装生产线精确建模和提高建模效率,提出层次时间着色Petri网(Hierachical Timed Color Petri Net,HTCPN)多层级理论模型,定义了HTCPN层次化建模方法。针对某飞机总装生产线站位划分的多层级、多粒度特点,利用CPN Tools工具...为了实现飞机总装生产线精确建模和提高建模效率,提出层次时间着色Petri网(Hierachical Timed Color Petri Net,HTCPN)多层级理论模型,定义了HTCPN层次化建模方法。针对某飞机总装生产线站位划分的多层级、多粒度特点,利用CPN Tools工具对A1、A2和A3三种型号飞机总装配过程进行HTCPN建模,根据交货期前后设置优先级,并在每个站位的装配过程中嵌套质量检测模型。最后对模型进行10次仿真,统计平均作业时间分别为78.4、93.2、102.4 h,产品总收益率为82.7%。展开更多
基金supported in part by the National Natural Science Foundation of China under Grants 92167205,61933009,62025305,and 62103268.
文摘Aircraft final assembly line(AFAL)involves thousands of processes that must be completed before delivery.However,the heavy reliance on manual labor in most assembly processes affects the quality and prolongs the delivery time.While the advent of artificial intelligence of things(AIoT)technologies has introduced advancements in certain AFAL scenarios,systematically enhancing the intelligence level of the AFAL and promoting the widespread deployment of artificial intelligence(AI)technologies remain significant challenges.To address these challenges,we propose the intelligent and collaborative aircraft assembly(ICAA)framework,which integrates AI technologies within a cloud-edge-terminal architecture.The ICAA framework is designed to support AI-enabled applications in the AFAL,with the goal of improving assembly efficiency at both individual and multiple process levels.We analyze specific demands across various assembly scenarios and introduce corresponding AI technologies to meet these demands.The three-tier ICAA framework consists of the assembly field,edge data platform,and assembly cloud platform,facilitating the collection of heterogeneous terminal data and the deployment of AI technologies.The framework enhances assembly efficiency by reducing reliance on manual labor for individual processes and fostering collaboration across multiple processes.We provide detailed descriptions of how AI functions at each level of the framework.Furthermore,we apply the ICAA framework to a real AFAL,focusing explicitly on the flight control system testing process.This practical implementation demonstrates the effectiveness of the framework in improving assembly efficiency and promoting the adoption of AIoT technologies.
文摘为了实现飞机总装生产线精确建模和提高建模效率,提出层次时间着色Petri网(Hierachical Timed Color Petri Net,HTCPN)多层级理论模型,定义了HTCPN层次化建模方法。针对某飞机总装生产线站位划分的多层级、多粒度特点,利用CPN Tools工具对A1、A2和A3三种型号飞机总装配过程进行HTCPN建模,根据交货期前后设置优先级,并在每个站位的装配过程中嵌套质量检测模型。最后对模型进行10次仿真,统计平均作业时间分别为78.4、93.2、102.4 h,产品总收益率为82.7%。
