Research on spacecraft mechanisms production line balance based on digital twin technology

doi:10.11996/JG.j.2095-302X.2024020332

Abstract

Abstract:

With the development of China’s aerospace, the increasing number of spacecrafts launches, and the rising demand for shape memory alloy hold-down and release mechanisms for spacecrafts, it is urgent to establish a digital and intelligent production line tailored to the production characteristics of spacecraft products. In this paper, according to the production characteristics of spacecraft shape memory alloy hold-down and release mechanisms, three digital twin models were proposed: the product digital twin model, equipment digital twin model and manufacturing process digital twin model in the manufacturing process of spacecraft mechanisms. The functional module of the digital twin workshop was designed, and a process balancing method of the production line based on industrial engineering (IE) and genetic algorithms was proposed. Furthermore, an information integration and application prototype system based on the digital twin was developed, along with the development of digital twin service interfaces and human-machine interfaces. An automated, digital, and intelligent production line has been established, achieving the information integration and application of digital twins of spacecraft mechanisms, equipment and processes. This has enabled synchronous operation and real-time interaction between virtual and real workshops, significantly boosting the production capacity of spacecraft shape memory alloy hold-down and release mechanisms.

Key words: digital twin, production line, balance, spacecraft mechanisms, genetic algorithm

CLC Number:

TP391

PANG Bo, YANG Hui, YU Rongrong, ZHANG Haoyue, LUO Qiang. Research on spacecraft mechanisms production line balance based on digital twin technology[J]. Journal of Graphics, 2024, 45(2): 332-338.

Figures/Tables 8

References 12

[1]	田凌, 刘果, 刘思超. 数字孪生与生产线仿真技术研究[J]. 图学学报, 2021, 42(3): 349-358.
	TIAN L, LIU G, LIU S C. Digital twin and production line simulation technology[J]. Journal of Graphics, 2021, 42(3): 349-358 (in Chinese).
[2]	胡富琴, 杨芸, 刘世民, 等. 航天薄壁件旋压成型数字孪生高保真建模方法[J]. 计算机集成制造系统, 2022, 28(5): 1282-1292.
	HU F Q, YANG Y, LIU S M, et al. Digital twin high-fidelity modeling method for spinning forming of aerospace thin-walled parts[J]. Computer Integrated Manufacturing Systems, 2022, 28(5): 1282-1292 (in Chinese).
[3]	戴璐, 邵一夫, 郭宇元, 等. 基于数字孪生的卫星装备智能设计系统[J]. 兵工学报, 2022, 43(S2): 139-145.
	DAI L, SHAO Y F, GUO Y Y, et al. Intelligent design system of satellite equipment based on digital twin[J]. Acta Armamentarii, 2022, 43(S2): 139-145 (in Chinese).
[4]	吴军, 周国华, 刘望. 航天非火工压紧释放装置研究综述[J]. 航天器工程, 2023, 32(1): 125-137.
	WU J, ZHOU G H, LIU W. Review of space non-pyrotechnic hold-down and release mechanisms[J]. Spacecraft Engineering, 2023, 32(1): 125-137 (in Chinese).
[5]	仲作阳, 张海联, 周建平, 等. 航天器非火工连接分离技术研究综述[J]. 载人航天, 2019, 25(1): 128-142.
	ZHONG Z Y, ZHANG H L, ZHOU J P, et al. Review of non-pyrotechnic connection and separation technology of spacecraft[J]. Manned Spaceflight, 2019, 25(1): 128-142 (in Chinese).
[6]	郭东升, 鲍劲松, 史恭威, 等. 基于数字孪生的航天结构件制造车间建模研究[J]. 东华大学学报: 自然科学版, 2018, 44(4): 578-585, 607.
	GUO D S, BAO J S, SHI G W, et al. Research on modeling of aerospace structural parts manufacturing workshop based on digital twin[J]. Journal of Donghua University: Natural Science, 2018, 44(4): 578-585, 607 (in Chinese).
[7]	金飞翔, 王康文, 吕刚, 等. 基于数字孪生技术的智能生产线应用探索[J]. 现代制造工程, 2023(2): 18-26.
	JIN F X, WANG K W, LÜ G, et al. Exploration of intelligent production line applications based on digital twin technology[J]. Modern Manufacturing Engineering, 2023(2): 18-26 (in Chinese).
[8]	林筠, 李随成. 多品种可变流水线平衡及改善[J]. 工业工程, 2000, 3(2): 46-49.
	LIN J, LI S C. Varied convertible assembly line balance and improve[J]. Industrial Engineering Journal, 2000, 3(2): 46-49 (in Chinese).
[9]	唐雨蒙. 遗传算法在H公司生产线平衡中的应用研究[D]. 大连: 大连海事大学, 2020.
	TANG Y M. Research for the application of genetic algorithm in the line balance of H company[D]. Dalian: Dalian Maritime University, 2020 (in Chinese).
[10]	张超, 李慧. 基于粒子群算法和仿真分析的飞机移动装配线平衡研究[J]. 航空科学技术, 2016, 27(11): 61-67.
	ZHANG C, LI H. Study of aircraft moving assembly line balancing based on PSO algorithm and discrete event simulation[J]. Aeronautical Science & Technology, 2016, 27(11): 61-67 (in Chinese).
[11]	赵云飞. 基于遗传算法的生产线平衡改善研究[D]. 南昌: 南昌大学, 2014.
	ZHAO Y F. Study on improvement of the assembly line balance based on genetic algorithm[D]. Nanchang: Nanchang University, 2014 (in Chinese).
[12]	张于贤, 梁师文, 杨梦珂. 多目标约束下装配线再平衡研究[J]. 兵器装备工程学报, 2019, 40(1): 214-219.
	ZHANG Y X, LIANG S W, YANG M K. Research on rebalancing of multi-objective constraints assembly line[J]. Journal of Ordnance Equipment Engineering, 2019, 40(1): 214-219 (in Chinese).

原则	原因分析	解决方案
取消 Eliminate	对于没必要做的工作就取消	直接取消
合并 Combine	对于2个及以上的工序考虑能否合并	将需要检查的2处工作交给同一员工
调整顺序 Rearrange	对于人员、地点、时间的顺序是否能调整	术业专攻、统筹安排
简化 Simplify	有没有更加简单地作业方法	引进设备、制作工装

原则	原因分析	解决方案
取消 Eliminate	对于没必要做的工作就取消	直接取消
合并 Combine	对于2个及以上的工序考虑能否合并	将需要检查的2处工作交给同一员工
调整顺序 Rearrange	对于人员、地点、时间的顺序是否能调整	术业专攻、统筹安排
简化 Simplify	有没有更加简单地作业方法	引进设备、制作工装

序号	阶段	站点数/个	生产节拍/h	平衡率/%
1	原状态	10	4.8	77.3
2	建设后	10	3.2	91.2

序号	阶段	站点数/个	生产节拍/h	平衡率/%
1	原状态	10	4.8	77.3
2	建设后	10	3.2	91.2

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