Abstract
Issues about cycle time optimization is of great importance in the field of automotive production, the industrial robots are widely used in the welding process of automobiles, but there is little research on the optimization of intra station rhythm during the design phase. By conducting research on workstation with industrial robot processing as key process, this paper carries out analysis from the selection of equipment layout within the workstation, planning production rhythm, and the facility performance analysis within the workstation. The finding shows the cycle time within the workstation has been reduced by 12 s. This article aims at improving the rhythm of robotic cells in complex production environment, and raising production efficiency of workstation. The robot path is optimized by using intelligent algorithms, the human machine collaborative work has been validated in virtual scenes, some digital design is adopted for modelling and simulating, the designed workstation has been verified from multiple perspectives, and finally achieve the workstation design of applying industrial robots in the production scenario.
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Ahmadi-Javid, A., Ardestani-Jaafari, A.: The unequal area facility layout problem with shortest single-loop AGV path: how material handling method matters. Int. J. Prod. Res. 59, 2352–2374 (2020). https://doi.org/10.1080/00207543.2020.1733124
Al-Zubaidi, S.Q.D., Fantoni, G., Failli, F.: Analysis of drivers for solving facility layout problems: a literature review. J. Ind. Inf. Integr. (2021). https://doi.org/10.1016/j.jii.2020.100187
Araújo, W.F.S., Silva, F.J.G., Campilho, R.D.S.G., et al.: Manufacturing cushions and suspension mats for vehicle seats: a novel cell concept. Int. J. Adv. Manuf. Technol. 90, 1539–1545 (2016). https://doi.org/10.1007/s00170-016-9475-6
Bix, S., Witt, P.: Introducing constraints to improve new product development performance. Res. Technol. Manage. 63, 29–37 (2020). https://doi.org/10.1080/08956308.2020.1790238
Buechler, T., Schumacher, F., Reimann, P., et al.: Methodology for an automatic and early manufacturing technology selection on a component level. Prod. Eng. Res. Devel. 16, 23–41 (2021). https://doi.org/10.1007/s11740-021-01070-2
Chen, W., Liu, H., Qi, E.: Discrete event-driven model predictive control for real-time work-in-process optimization in serial production systems. J. Manuf. Syst. 55, 132–142 (2020). https://doi.org/10.1016/j.jmsy.2020.03.002
Diaz, R., Ardalan, A.: Innovating in data-driven production environments: simulation analysis of Net-CONWIP priority rule. Ind. Manage. Data Syst. (2023). https://doi.org/10.1108/imds-10-2022-0629
Erik, A., Kuvvetli, Y.: Integration of material handling devices assignment and facility layout problems. J. Manuf. Syst. 58, 59–74 (2021). https://doi.org/10.1016/j.jmsy.2020.11.015
Ferraro, A., Rossit, D.A., Toncovich, A.: Flow shop scheduling problem with non-linear learning effects: a linear approximation scheme for non-technical users. J. Comput. Appl. Math. 424, 14 (2023). https://doi.org/10.1016/j.cam.2022.114983
Forghani, K., Ghomi, S., Kia, R.: Concurrent scheduling and layout of virtual manufacturing cells considering assembly aspects. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 235, 1036–1049 (2021). https://doi.org/10.1177/0954405420980685
Fu, Y., Yang, G., Ma, H., Chen, H., Zhu, B.: Event-triggered feedforward predictive control for dimension quality optimization in BIW assembly process. IEEE Trans. Ind. Inf. 18, 1083–1090 (2022). https://doi.org/10.1109/TII.2021.3082187
Guo, Y., Zhang, W., Qin, Q., Chen, K., Wei, Y.: Intelligent manufacturing management system based on data mining in artificial intelligence energy-saving resources. Soft. Comput. 27, 4061–4076 (2023). https://doi.org/10.1007/s00500-021-06593-5
Guner, F., Gorur, A.K., Satir, B., et al.: A constraint programming approach to a real-world workforce scheduling problem for multi-manned assembly lines with sequence-dependent setup times. Int. J. Prod. Res. (2023). https://doi.org/10.1080/00207543.2023.2226772
Guzman, E., Andres, B., Poler, R.: Models and algorithms for production planning, scheduling and sequencing problems: a holistic framework and a systematic review. J. Ind. Inf. Integr. (2022). https://doi.org/10.1016/j.jii.2021.100287
Hou, L., Zhao, J., Ma, C.: Task scheduling method of production line workflow based on firefly algorithm. Int. J. Data Min. Bioinf. 27, 92–106 (2022). https://doi.org/10.1504/ijdmb.2022.130340
Huang, Y., Shi, X., Zhou, Y., Xiong, Z.: Autonomous navigation of mobile robot in radiation environment with uneven terrain. Int. J. Intell. Robot. Appl. 7, 497–509 (2023). https://doi.org/10.1007/s41315-022-00255-x
Hyun, H., Yoon, I., Lee, H.S., Park, M., Lee, J.: Multiobjective optimization for modular unit production lines focusing on crew allocation and production performance. Autom. Construct (2021). https://doi.org/10.1016/j.autcon.2021.103581
Ji, S., Xue, Y., Zhu, G.: Physical Internet-enabled automobile production-distribution joint optimisation with multistage workshop. Kybernetes 52, 898–920 (2023). https://doi.org/10.1108/k-03-2021-0217
Khajemahalle, L., Emami, S., Keshteli, R.N.: A hybrid nested partitions and simulated annealing algorithm for dynamic facility layout problem: a robust optimization approach. INFOR Inf. Syst. Oper. Res. 59, 74–101 (2020). https://doi.org/10.1080/03155986.2020.1788328
Lee, J., Hu, S.J., Ward, A.C.: Workspace synthesis for flexible fixturing of stampings. J. Manuf. Sci. Eng. 121(3), 478–484 (1999). https://doi.org/10.1115/1.2832706
Li, Yi., Chen, X., An, Y., Zhao, Z., Cao, H., Jiang, J.: Integrating machine layout, transporter allocation and worker assignment into job-shop scheduling solved by an improved non-dominated sorting genetic algorithm. Comput. Ind. Eng. 179, 19 (2023). https://doi.org/10.1016/j.cie.2023.109169
Liu, Y., Zheng, J.: Intelligent management of supply chain logistics based on 5g LoT. Clust. Comput. 25, 2271–2280 (2022). https://doi.org/10.1007/s10586-021-03487-x
Ma, H., Huang, X., Hu, Z., et al.: Multi-objective production scheduling optimization and management control system of complex aerospace components: a review. Int J Adv Manuf Technol. Review; Early Access (2023). https://doi.org/10.1007/s00170-023-11707-4
Ma, Y., Li, S., Qiao, F., Lu, X., Liu, J.: A data-driven scheduling knowledge management method for smart shop floor. Int. J. Comput. Integr. Manuf. 35, 780–793 (2022). https://doi.org/10.1080/0951192x.2022.2025622
Malega, P., Gazda, V., Rudy, V.: Optimization of production system in plant simulation. Simul. Trans. Soc. Model. Simul. Int. 98, 295–306 (2022). https://doi.org/10.1177/00375497211038908
Najlae, A., Abdelouahid, L., Abdelfettah, S.: Product-driven manufacturing launch of semi-finished product. In: Kacprzyk, J., Balas, V.E., Ezziyyani, M. (eds.) Advanced Intelligent Systems for Sustainable Development (AI2SD’2020), pp. 1251–1260. Springer, Cham (2022)
Pourvaziri, H., Pierreval, H., Marian, H.: Integrating facility layout design and aisle structure in manufacturing systems: Formulation and exact solution. Eur. J. Oper. Res. 290, 499–513 (2021). https://doi.org/10.1016/j.ejor.2020.08.012
Saidat, S., Junoh, A.K., Muhamad, W., et al.: Modified job shop scheduling via Taguchi method and genetic algorithm. Neural Comput. Appl. 34, 1963–1980 (2022). https://doi.org/10.1007/s00521-021-06504-7
Shao, Z., Shao, W., Pi, D.: LS-HH: A learning-based selection hyper-heuristic for distributed heterogeneous hybrid blocking flow-shop scheduling. IEEE Trans. Emerg. Top. Comput. Intell. 7, 111–127 (2023). https://doi.org/10.1109/tetci.2022.3174915
Son, Y.H., Park, K.T., Lee, D., Jeon, S.W., Noh, S.D.: Digital twin-based cyber-physical system for automotive body production lines. Int. J. Adv. Manuf. Technol. 115, 291–310 (2021). https://doi.org/10.1007/s00170-021-07183-3
Trattner, A., Hvam, L., Haug, A.: Why slow down? Factors affecting speed loss in process manufacturing. Int. J. Adv. Manuf. Technol. 106, 2021–2034 (2019). https://doi.org/10.1007/s00170-019-04559-4
Tropschuh, B., Cegarra, J., Battaia, O.: Integrating physiological and mental aspects in employee scheduling: an overview for practitioners in production management. Int. J. Prod. Res. (2023). https://doi.org/10.1080/00207543.2023.2217278
Uribe, N.R., Herrán, A., Colmenar, J.M., et al.: An improved GRASP method for the multiple row equal facility layout problem. Expert Syst. Appl. (2021). https://doi.org/10.1016/j.eswa.2021.115184
Vieira, M., Moniz, S., Goncalves, B.S., Pinto-Varela, T., Barbosa-Povoa, A.P., Neto, P.: A two-level optimisation-simulation method for production planning and scheduling: the industrial case of a human-robot collaborative assembly line. Int. J. Prod. Res. 60, 2942–2962 (2022). https://doi.org/10.1080/00207543.2021.1906461
Viles, E., Bultó, R., Mateo, R., Jurburg, D.: Production ramp-up in European automotive production systems: a performance analysis. Prod. Plan. Control 32, 34–51 (2020). https://doi.org/10.1080/09537287.2020.1711980
Wang, Ge, Li, Di, Tu, Yuqing, Zhang, Chunhua, Li, Fang & Wang, Shiyong.: A product-process-resource based formal modelling framework for customized manufacturing in cyber-physical production systems. Int. J. Comput. Int. Manu. 35(6), 598–618 (2022). https://doi.org/10.1080/0951192X.2021.1992662
Wang, Z., Liao, W.: Smart scheduling of dynamic job shop based on discrete event simulation and deep reinforcement learning. J. Intell. Manuf. (2023). https://doi.org/10.1007/s10845-023-02161-w
Yang, S., Xu, Z.: Intelligent scheduling and reconfiguration via deep reinforcement learning in smart manufacturing. Int. J. Prod. Res. 60, 4936–4953 (2022). https://doi.org/10.1080/00207543.2021.1943037
Yu, K.: Robust fixture design of compliant assembly process based on a support vector regression model. Int. J. Adv. Manuf. Technol. 1031(4), 111–126 (2019). https://doi.org/10.1007/s00170-019-03488-6
Yuvaraj, K., Vigneshwaran, S.: Investigation on spot welding and roller hemming by robots. Mater. Today Proc. 45, 1075–1080 (2021). https://doi.org/10.1016/j.matpr.2020.03.185
Zhao, H., Zhang, B., Sun, J., Yang, L., Yu, H.: Spot-welding path planning method for curved surface workpiece of body-in-white based on memetic algorithm. Int. J. Adv. Manuf. Technol. 117, 3083–3100 (2021). https://doi.org/10.1007/s00170-021-07728-6
Zhang, Y., Cheng, Y., Wang, V., Xi, Z., Ray Y., Zhang, Y., Tao, F.: Data-driven smart production line and its common factors. Int. J. Adv. Manuf. Technol. 103, 1211–1223 (2019). https://doi.org/10.1007/s00170-019-03469-9
Zhang, X., Ming, X., Bao, Y.: A flexible smart manufacturing system in mass personalization manufacturing model based on multi-module-platform, multi-virtual-unit, and multi-production-line. Comput. Ind. Eng. 171, 20 (2022). https://doi.org/10.1016/j.cie.2022.108379
Zhang, X., Zhang, Z., Gong, X., Yin, Y.: An exact branch-and-bound algorithm for seru scheduling problems with sequence-dependent setup time. Soft. Comput. 27, 6415–6436 (2023). https://doi.org/10.1007/s00500-023-07846-1
Acknowledgements
This paper is supported by Jilin Provincial Science and Technology Department (Grant No.20200301038RQ); Jilin Provincial Science and Technology Department (Grant No. 20200401114G X); Jilin Provincial Science and Technology Department (Grant No. 20210201050GX); Jilin Provincial Science and Technology Department (Grant No. 20210301033GX); Jilin Provincial Science and Technology Department (Grant No. 20230201095GX).
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All authors contributed to the study's conception and design. The first draft of the manuscript was written by Qi Xia and Bangcheng Zhang. Meanwhile, other authors directed and supervised the research; All authors reviewed, commented, and corrected previous manuscript versions. All authors read and approved the final manuscript.
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Xia, Q., Zhang, B., Zhang, X. et al. Investigation on robotic cells design improvement in the welding process of body in white. Int J Intell Robot Appl (2024). https://doi.org/10.1007/s41315-023-00317-8
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DOI: https://doi.org/10.1007/s41315-023-00317-8