Abstract
Low-voltage electrical apparatuses (LVEAs) have many workpieces and intricate geometric structures, and the assembly process is rigid and labor-intensive, and has little balance. The assembly process cannot readily adapt to changes in assembly situations. To address these issues, a collaborative assembly is proposed. Based on the requirements of collaborative assembly, a colored Petri net (CPN) model is proposed to analyze the performance of the interaction and self-government of robots in collaborative assembly. Also, an artificial potential field based planning algorithm (AFPA) is presented to realize the assembly planning and dynamic interaction of robots in the collaborative assembly of LVEAs. Then an adaptive quantum genetic algorithm (AQGA) is developed to optimize the assembly process. Lastly, taking a two-pole circuit-breaker controller with leakage protection (TPCLP) as an assembly instance, comparative results show that the collaborative assembly is cost-effective and flexible in LVEA assembly. The distribution of resources can also be optimized in the assembly. The assembly robots can interact dynamically with each other to accommodate changes that may occur in the LVEA assembly.
摘要
低压电器设备由较多零部件组成, 结构较为复杂, 其现有装配方法多是刚性、 劳动密集和低平衡的装配工艺过程, 不能随装配环境变化迅速改变. 本文提出一种面向低压电器的协同装配方法. 首先, 根据协同装配的性能要求, 构建着色Petri网模型, 以分析协同装配中各机器人的自治性能和交互特性. 其次, 在装配控制中提出一种基于规划的人工势场算法(artificial potential field based planning algorithm, AFPA), 以实现低压电器设备协同装配中机器人静态全局规划和动态交互控制, 并引入自适应量子遗传算法(adaptive quantum genetic algorithm, AQGA)对整个装配过程进行平衡优化. 最后, 以带漏电保护装置的二相断路器为例, 对协同装配方法进行模拟分析. 结果表明, 低压电器装配中, 协同装配方法具有较好的成本效益和柔性, 同时装配资源得到较好分配. 装配机器人能够相互间动态交互以适应低压电器设备装配中的变化.
This is a preview of subscription content, log in via an institution to check access.
Data availability
The data that support the findings of this study are available within the article and its supplementary materials.
References
André É, Benmoussa MM, Choppy C, 2016. Formalising concurrent UML state machines using coloured Petri nets. Form Aspects Comput, 28(5):805–845. https://doi.org/10.1007/s00165-016-0388-9
Battaïa O, Dolgui A, 2013. A taxonomy of line balancing problems and their solution approaches. Int J Prod Econ, 142(2): 259–277. https://doi.org/10.1016/j.ijpe.2012.10.020
Baybars I, 1986. A survey of exact algorithms for the simple assembly line balancing problem. Manag Sci, 32(8):909–932. https://doi.org/10.1287/mnsc.32.8.909
Baykasoglu A, 2006. Multi-rule multi-objective simulated annealing algorithm for straight and U type assembly line balancing problems. J Intell Manuf, 17(2):217–232. https://doi.org/10.1007/s10845-005-6638
Charbonnier F, Alla H, David R, 1999. Discrete-event dynamic systems. IEEE Trans Contr Syst Technol, 7(2): 175–187. https://doi.org/10.1109/87.748144
Chen ST, Tan DP, 2018. A SA-ANN-based modeling method for human cognition mechanism and the PSACO cognition algorithm. Complexity, 2018:6264124. https://doi.org/10.1155/2018/6264124
Çil ZA, Mete S, Özceylan E, et al., 2017. A beam search approach for solving type II robotic parallel assembly line balancing problem. Appl Soft Comput, 61:129–138. https://doi.org/10.1016/j.asoc.2017.07.062
Deb K, 1998. Genetic algorithm in search and optimization: the technique and applications. Proc Int Workshop on Soft Computing and Intelligent Systems, p.58-87.
Desel J, Reisig W, 1998. Place/transition Petri nets. Proc Advanced Course on Petri Nets, p.122-173. https://doi.org/10.1007/3-540-65306-6_15
Gao J, Sun LY, Wang LH, et al., 2009. An efficient approach for type II robotic assembly line balancing problems. Comput Ind Eng, 56(3):1065–1080. https://doi.org/10.1016/j.cie.2008.09.027
Ge M, Ji SM, Tan DP, et al., 2021. Erosion analysis and experimental research of gas-liquid-solid soft abrasive flow polishing based on cavitation effects. Int J Adv Manuf Technol, 114(11–12):3419–3436. https://doi.org/10.1007/s00170-021-06752-w
Grzechca W, 2014. Assembly line balancing problem with reduced number of workstations. IFAC Proc Vol, 47(3):6180–6185. https://doi.org/10.3182/20140824-6-ZA-1003.02530
Jensen K, 1990. Coloured petri nets: a high level language for system design and analysis. Proc Int Conf on Application and Theory of Petri Nets, p.342-416. https://doi.org/10.1007/3-540-53863-1_31
Ji SM, Weng XX, Tan DP, 2012. Analytical method of softness abrasive two-phase flow field based on 2D model of LSM. Acta Phys Sin, 61(1):010205 (in Chinese). https://doi.org/10.7498/aps.61.010205
Johannsmeier L, Haddadin S, 2017. A hierarchical human-robot interaction-planning framework for task allocation in collaborative industrial assembly processes. IEEE Robot Autom Lett, 2(1):41–48. https://doi.org/10.1109/LRA.2016.2535907
Khatib O, 1986. Real-time obstacle avoidance for manipulators and mobile robots. Int J Robot Resh, 5(1):90–98. https://doi.org/10.1177/027836498600500106
Levitin G, Rubinovitz J, Shnits B, 2006. A genetic algorithm for robotic assembly line balancing. Eur J Oper Res, 168(3): 811–825. https://doi.org/10.1016/j.ejor.2004.07.030
Li DL, 2004. Electrical Control & the Principle and Application of PLC. Publishing House of Electronics Industry, Beijing, China (in Chinese).
Li L, Lu JF, Fang H, et al., 2020. Lattice Boltzmann method for fluid-thermal systems: status, hotspots, trends and outlook. IEEE Access, 8:27649–27675. https://doi.org/10.1109/ACCESS.2020.2971546
Li L, Tan DP, Yin ZC, et al., 2021. Investigation on the multiphase vortex and its fluid-solid vibration characters for sustainability production. Renew Energy, 175:887–909. https://doi.org/10.1016/j-renene.2021.05.027
Li SY, Li PC, 2006. Quantum genetic algorithm based on real encoding and gradient information of object function. J Harbin Inst Technol, 38(8):1216–1218, 1223 (in Chinese). https://doi.org/10.3321/j.issn:0367-6234.2006.08.002
Li XL, Xing KY, Lu QC, 2021. Hybrid particle swarm optimization algorithm for scheduling flexible assembly systems with blocking and deadlock constraints. Eng Appl Artif Intell, 105:104411. https://doi.org/10.1016/j.engappai.2021.104411
Montiel O, Sepúlveda R, Orozco-Rosas U, 2015. Optimal path planning generation for mobile robots using parallel evolutionary artificial potential field. J Intell Robot Syst, 79(2): 237–257. https://doi.org/10.1007/s10846-014-0124-8
New S, 1994. Modeling and analysis of manufacturing systems. J Oper Res Soc, 45(6):725–726. https://doi.org/10.1057/jors.1994.112
Nilakantan JM, Ponnambalam SG, Jawahar N, et al., 2015. Bio-inspired search algorithms to solve robotic assembly line balancing problems. Neur Comput Appl, 26(6): 1379–1393. https://doi.org/10.1007/s00521-014-1811-x
Özcan U, Toklu B, 2009. A new hybrid improvement heuristic approach to simple straight and U-type assembly line balancing problems. J Intell Manuf, 20(1): 123–136. https://doi.org/10.1007/s10845-008-0108-2
Pan Y, Ji SM, Tan DP, et al., 2020. Cavitation-based soft abrasive flow processing method. Int J Adv Manuf Technol, 109(9):2587–2602. https://doi.org/10.1007/s00170-020-05836-3
Ren CX, Zhang H, Fan YZ, 2015. Optimizing dispatching of public transit vehicles using genetic simulated annealing algorithm. J Syst Simul, 17(9):2075–2077, 2081 (in Chinese). https://doi.org/10.3969/j.issn.1004-731X.2005.09.008
Rizwan M, Patoglu V, Erdem E, 2020. Human robot collaborative assembly planning: an answer set programming approach. Theory Pract Log Program, 20(6):1006–1020. https://doi.org/10.1017/S1471068420000319
Rubinovitz J, Bukchin J, Lenz E, 1993. RALB: a heuristic algorithm for design and balancing of robotic assembly lines. CIRP Ann, 42(1):497–500. https://doi.org/10.1016/S0007-8506(07)62494-9
Samouei P, Fattahi P, Ashayeri J, et al., 2016. Bottleneck easing-based assignment of work and product mixture determination: fuzzy assembly line balancing approach. Appl Math Modell, 40(7–8):4323–4340. https://doi.org/10.1016/j.apm.2015.11.011
Tan DP, Chen ST, Bao GJ, et al., 2018. An embedded lightweight GUI component library and ergonomics optimization method for industry process monitoring. Front Inform Technol Electron Eng, 19(5):604–625. https://doi.org/10.1631/FITEE.1601660
Tavakoli A, 2020. Multi-criteria optimization of multi product assembly line using hybrid tabu-SA algorithm. SN Appl Sci, 2(2):151. https://doi.org/10.1007/s42452-019-1863-8
Wang H, Chen Z, Huang JH, et al., 2022. Development of highspeed on-off valves and their applications. Chin J Mech Eng, 35(1):67. https://doi.org/10.1186/s10033-022-00720-5
Wang JX, Gao SB, Tang ZJ, et al., 2023. A context-aware recommendation system for improving manufacturing process modeling. J Intell Manuf, 34:1347–1368. https://doi.org/10.1007/s10845-021-01854-4
Wang YY, Zhang YL, Tan DP, et al., 2021. Key technologies and development trends in advanced intelligent sawing equipments. Chin J Mech Eng, 34(1):30. https://doi.org/10.1186/s10033-021-00547-6
Xie N, 2006. Research on Modeling, Scheduling and Controller of Reconfigurable Manufacturing System Using Petri Nets. PhD Thesis, Tongji University, Shanghai, China (in Chinese).
Yang JN, Li B, Zhang ZQ, 2003. Research of quantum genetic algorith and its application in blind source separation. J Electron, 20(1):62–68. https://doi.org/10.1007/s11767-003-0089-4
Yu MY, Yang JJ, 2018. Research on flexible assembly system for multi-variety and small-batch products. Mech Eng Autom, (6):39–41 (in Chinese).
Zelenka J, 2010. Discrete event dynamic systems framework for analysis and modeling of real manufacturing system. Proc 14th Int Conf on Intelligent Engineering System, p.287-291. https://doi.org/10.1109/INES.2010.5483829
Zeng X, Ji SM, Jin MS, et al., 2014. Investigation on machining characteristic of pneumatic wheel based on softness consolidation abrasives. Int J Prec Eng Manuf, 15(10):2031–2039. https://doi.org/10.1007/s12541-014-0560-1
Zhang JY, Liu T, 2007. Optimized path planning of mobile robot based on artificial potential field. Acta Aeronaut Astronaut Sin, 28(S1):S183–S188 (in Chinese). https://doi.org/10.3321/j.issn:1000-6893.2007.z1.033
Zhang K, Zhang JH, Gan MY, et al., 2022. Modeling and parameter sensitivity analysis of valve-controlled helical hydraulic rotary actuator system. Chin J Mech Eng, 35(1):66. https://doi.org/10.1186/s10033-022-00737-w
Zheng SH, Yu YK, Qiu MZ, et al., 2021. A modal analysis of vibration response of a cracked fluid-filled cylindrical shell. Appl Math Modell, 91:934–958. https://doi.org/10.1016/j.apm.2020.09.040
Author information
Authors and Affiliations
Contributions
Huanpei LYU, Libin ZHANG, and Dapeng TAN proposed the idea. All the authors designed the research. Huanpei LYU drafted the paper. Libin ZHANG, Dapeng TAN, and Fang XU revised and finalized the paper.
Corresponding author
Ethics declarations
Huanpei LYU, Libin ZHANG, Dapeng TAN, and Fang XU declare that they have no conflict of interest.
Additional information
Project supported by the National Natural Science Foundation of China (No. 52175124), the Zhejiang Provincial Natural Science Foundation of China (No. LZ21E050003), and the Fundamental Research Funds for Zhejiang Universities, China (No. RF-C2020004)
List of supplementary materials
Fig. S1 Main content of this paper
Fig. S2 Architecture of the collaborative assembly methodology
Fig. S3 Definition of the artificial potential field (APF)
Fig. S4 Formal definition of the colored Petri net (CPN)
Fig. S5 Setting of places in the CPN collaborative assembly model
Fig. S6 Setting of model transitions in the CPN collaborative assembly model
Fig. S7 Relationship between the tasks required for a TPCLP
Fig. S8 Average number of assembly tasks of the assembly robots for different confidence intervals
Fig. S9 Number of assembly tasks for each assembly robot for different numbers of simulation batches
Fig. S10 Setting of relevant parameters of the compared algorithms
Fig. S11 Comparison of the number of robots required for the different algorithms
Fig. S12 LE values of each assembly mode for different assembly cycles
Fig. S13 SI values of each assembly mode for different assembly cycles
Fig. S14 Number of robots required for each assembly mode for different assembly cycles
Fig. S15 Analysis of the robot assembly performance with dynamic balance of AFPA
Fig. S16 Relationship between the assembly tasks required for type 2
Table S1 Studies of assembly line construction and balance optimization
Table S2 Assembly tasks corresponding to each assembly robot
Table S3 Relationship between the assembly cycle and the robot number
Table S4 Number of assembly tasks for each type of robot
Table S5 Value range of the critical parameters
Table S6 Comparison of optimal target values for each algorithm
Table S7 Statistical data of different algorithms
Table S8 Descriptive statistics of different algorithms
Table S9 Ranks for different algorithms
Table S10 Friedman test statistics of different algorithms
Table S11 Statistical data of different assembly lines
Table S12 Descriptive statistics of different assembly lines
Table S13 Ranks of different assembly lines
Table S14 Friedman test statistics of different assembly lines
Table S15 Comparison between the collaborative assembly and basic assembly lines
Supplementary materials for
Rights and permissions
About this article
Cite this article
Lyu, H., Zhang, L., Tan, D. et al. A collaborative assembly for low-voltage electrical apparatuses. Front Inform Technol Electron Eng 24, 890–905 (2023). https://doi.org/10.1631/FITEE.2100423
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.2100423
Key words
- Low-voltage electrical apparatus
- Collaborative assembly
- Artificial potential field based planning
- Adaptive quantum genetic algorithm
- Dynamic interaction