A Framework for 3D Plant Simulation of Meal-Kit-Packaging Robot Automation System
Source abstract
Abstract
A data-driven 3D simulation for the robotic automation of the most labor-intensive packaging process in meal kit production was developed using Tecnomatix plant simulation software. The workflow and environments of the existing manual process were analyzed. An existing production site was scanned using a 3D Lidar scanner to create 3D models and design the initial assembly layout. Two types of 3D simulation models, implemented with a single or double delta robot, were designed to determine the optimal robot-automated packaging process. Key performance indicators for simulation models of a manual and two robot automation systems were analyzed. The throughputs of the manual, single delta robot and double delta robot models were 2112, 1510, and 2568 ea/h, respectively. The single robot system achieved only 68.3% of the throughput of the manual process, which is attributed to a cycle time of 2.36 s for picking and placing all components. On the other hand, the cycle time of the double robot system was 1.66 times faster, and the throughput was 1.7 times greater compared to the single robot system. The developed 3D simulation model for the meal kit packaging system demonstrates the potential of robotic automation in addressing the labor shortage issue as well as improving production efficiency.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Manufacturing and Logistics Optimization · Digital Transformation in Industry · Flexible and Reconfigurable Manufacturing Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Smart Food Manufacturing Research Group, Korea Food Research Institute, 245 Nongsaengmyeong-ro, Wanju-gun 55365, Republic of Korea
KyoungHee Corporation, 551-24, Yangcheon-ro, Gangseo-gu, Seoul 55365, Republic of Korea
Funding
Ministry of Trade, Industry and Energy (MOTIE) of Korea
References (26)
Show source-provided references
1. Packaging technology for home meal replacement: Innovations and future prospective10.1016/j.foodcont.2021.108470 ↗
2. A study on the relationship between purchases of meal kits and home meal replacements10.4162/nrp.2024.18.3.425 ↗
3. Processing and packaging automation systems: A review10.1007/s11694-009-9076-2 ↗
4. Extending fieldbus technology to food processing industry: A review10.1016/j.csi.2008.03.027 ↗
5. Bader, F., and Rahimifard, S. (2018, January 21–23). Challenges for industrial robot applications in food manufacturing. Proceedings of the 2nd International Symposium on Computer Science and Intelligent Control, Stockholm, Sweden.10.1145/3284557.3284723 ↗
6. Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing10.1016/j.jmsy.2023.11.019 ↗
7. Florescu, A. (2024). Digital twin for flexible manufacturing systems and optimization through simulation: A case study. Machines, 12.10.3390/machines12110785 ↗
8. Digital twin for smart manufacturing, A review
9. Digital twins-based smart manufacturing system design in Industry 4.0: A review10.1016/j.jmsy.2021.05.011 ↗
10. Improvement of an Automated CAN Packaging System Based on Modeling and Analysis Approach through Robot Simulation Tools
11. Simulation and Kinematic Analysis of KUKA KR5 Arc Robot10.1088/1757-899X/1149/1/012005 ↗
12. Gui, W., Zhou, F., and Tang, F. (2023, January 29–31). Research on Motion Simulation of Stacking Robot Workstation Based on RobotStudio. Proceedings of the 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA), Shenyang, China.10.1109/ICPECA56706.2023.10075799 ↗
13. Robotics and Machine Vision for Primary Food Manipulation and Packaging: A Survey10.1109/ACCESS.2024.3479781 ↗
14. Modelling an Assembly Line Using Tecnomatix Plant Simulation Software
15. Janekova, J., Fabianova, J., and Kadarova, J. (2023). Optimization of the automated production process using software simulation tools. Processes, 11.10.3390/pr11020509 ↗
16. Fedorko, G., Molnár, V., Strohmandl, J., Horváthová, P., Strnad, D., and Cech, V. (2022). Research on Using the Tecnomatix Plant Simulation for Simulation and Visualization of Traffic Processes at the Traffic Node. Appl. Sci., 12.10.3390/app122312131 ↗
17. Fusto, C., Longo, F., Muraca, A., Rudi, L., Timpani, T., and Veltri, P. (2023, January 18–20). Enhancing Efficiency in the Food Industry: A Simulation Model for Optimizing Production Processes. Proceedings of the 9th International Food Operations & Processing Simulation Workshop (FOODOPS 2023), Athens, Greece.
18. Wang, J., Yi, T., Liang, X., and Ueda, T. (2022). Application of 3D Laser Scanning Technology Using Laser Radar System to Error Analysis in the Curtain Wall Construction. Remote Sens., 15.10.3390/rs15010064 ↗
19. Mobile 3D scan LiDAR: A literature review10.1080/19475705.2021.1964617 ↗
20. Plant Simulation for Robot Automation System of Deep-Frying Process of Kimbugak10.1007/s10068-024-01813-8 ↗
21. Reducing the frame vibration of delta robot in pick and place application: An acceleration profile optimization approach10.1155/2018/2945314 ↗
22. Vision-Based Kinematic Analysis of the Delta Robot for Object Catching10.1017/S0263574721001491 ↗
23. Gholami, A., Homayouni, T., Ehsani, R., and Sun, J.Q. (2021). Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time. Robotics, 10.10.3390/robotics10040115 ↗
24. Aliev, K., Antonelli, D., Awouda, A., and Chiabert, P. (2019, January 23–25). Key performance indicators integrating collaborative and mobile robots in the factory networks. Proceedings of the Collaborative Networks and Digital Transformation: 20th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2019, Turin, Italy.10.1007/978-3-030-28464-0_56 ↗
25. Terrestrial laser scanning–new perspectives in 3D surveying
26. A precise scan matching based localization method for an autonomously guided vehicle in smart factories10.1016/j.rcim.2021.102302 ↗