Design and Application for End of Arm Tooling in Plastic Injection Molding
DOI:
https://doi.org/10.58641/cest.v4i2.208Keywords:
EOAT, injection, molding, plasticAbstract
This study discusses the implementation of End of Arm Tooling (EOAT) in the injection molding production process at Toyoda Kakou Co., Ltd. EOAT is a device attached to the end of a robotic arm that functions to automatically remove products from the mold. The research method used is descriptive qualitative with a case study approach. The evaluation results show that the use of EOAT is able to reduce the cycle time from approximately 26,0 seconds to 22.5 seconds, product defect rate decrease from 4% to 1%, and reduce from 1 employee to zero. This study also presents the detailed cost of EOAT components using the Yushin brand, with a total estimated value of IDR 6,350,000. The conclusion of this research is that EOAT has been proven to improve the efficiency and safety of the production process. It is recommended that EOAT be implemented more widely in other production lines.
References
Abdelkhalick, M., Sun, E., Zhou, J., et al. (2026). Robotic end-effectors for manufacturing: Recent developments and future research challenges. International Journal of Machine Tools and Manufacture, 216, 104367. doi:10.1016/j.ijmachtools.2026.104367
Andronas, E., Mavroeidis, V., Papadopoulos, G., Papavasileiou, A., & Makris, S. (2026). Towards intelligent object handling: An advanced end-effector for integrated bin-picking and assembly of electrical components. The International Journal of Advanced Manufacturing Technology, 142, 5967–5991. doi:10.1007/s00170-026-17451-9
Basuki, P., Zulaidah, A., & Prasdiantika, R. (2022). Studi kasus pemilihan jenis runner pada injection molding plastik: Case study selection type of runner for injection molding plastic. 139–145.
Cai, Q., Han, J., Zhou, X., Zhao, S., Li, L., Liu, H., Xu, C., Chen, J., Liu, C., & Zhu, H. (2026). A comprehensive review of human-robot collaborative manufacturing systems: Technologies, applications, and future trends. Sustainability, 18(1), 515. doi:10.3390/su18010515
Charan, S., Vennapusa, R., Charan, S., Vennapusa, R., & Tejani, J. G. (2024). Automated robotics solutions for precision molding in rubber manufacturing.
Czepiel, M., & Ba, M. (2023). Advanced injection molding methods: Review.
Daniel Ong U Jing, Devine, D. M., & Lyons, J. G. (2018). Robotic automation. Robotics, 7(3), 49. https://doi.org/10.3390/robotics7030049
Dhanda, M., Rogers, B. A., Hall, S., Dekoninck, E., & Dhokia, V. (2025). Reviewing human-robot collaboration in manufacturing: Opportunities and challenges in the context of Industry 5.0. Robotics and Computer-Integrated Manufacturing, 93, 102937. https://doi.org/10.1016/j.rcim.2024.102937
Du, Y., et al. (2026). Towards seamless and safe human-robot collaboration in manufacturing. International Journal of Production Research. doi:10.1080/00207543.2026.2639732
Fomekong, F., Merveille, R., & Fred, B. (2024). Enhancing manufacturing efficiency: A new robotic arm design for injection molding with improved adaptability and precision.
Hedayati-Dezfooli, M., & Moayyedian, M. (2026). Integrated optimization for reducing injection molding defects in charcoal canisters. Journal of Manufacturing and Materials Processing, 10(4), 114. doi:10.3390/jmmp10040114
Kim, M., Jeon, J., Rhee, B., Turng, L.-S., Choi, J. H., & Gim, J. (2026). Elimination of halo gloss transition surface defects on injection-molded parts. Scientific Reports, 16, 11629. doi:10.1038/s41598-026-42688-5
Pae, C.-U., Kim, S.-J., & Chae, H.-S. (2026). Optimization of injection molding process parameters for shrinkage and warpage reduction. The International Journal of Advanced Manufacturing Technology. doi:10.1007/s00170-026-17601-z
Peris, V. J., Gámez Martínez, J. L., Ferrándiz Bou, S., & Jordá Vilaplana, A. (2026). Application of digital twins in the metaverse for plastic injection training. The International Journal of Advanced Manufacturing Technology. doi:10.1007/s00170-025-17187-y
Rosli, M. U., & Khor, C. Y. (2026). Multi-factor optimization on metal injection molding in orthodontic single brackets manufacturing. The International Journal of Advanced Manufacturing Technology. doi:10.1007/s00170-026-17826-y
Roy, B., et al. (2026). Real-time optimal parameter recommendation for injection molding. AI, 7(2), 49. doi:10.3390/ai7020049
Tracht, K. (2014). Grasping devices and methods in automated production processes.
Van Emburg, C., Chen, H., Pilla, S., Li, G., & Carbajales-Dale, M. (2026). Quantifying energy consumption variability in injection molding: A meta-regression analysis. Resources, Conservation and Recycling, 227, 108730. doi:10.1016/j.resconrec.2025.108730
Wang, C.-C., Shen, H.-G., & Chien, C.-H. (2026). Cold-start quality prediction in multi-machine manufacturing systems via weak-consistency data fusion with human-in-the-loop learning. The International Journal of Advanced Manufacturing Technology, 143, 1499–1516. doi:10.1007/s00170-026-17673-x
Wang, C.-C., Ye, T.-Y., & Chien, C.-H. (2026). Evidence-driven event-triggered closed-loop decision-support architecture for real-time weight stabilization in IC-tray injection molding. The International Journal of Advanced Manufacturing Technology. doi:10.1007/s00170-026-17944-7
Wang, J., Chang, C.-T., & Ke, K.-C. (2026). Data-driven representation learning and stability validation for multi-quality prediction in smart injection molding. The International Journal of Advanced Manufacturing Technology, 143, 5465–5479. doi:10.1007/s00170-026-17655-z
Wang, J., Zheng, K., Liu, T., Liu, H., Cui, H., Li, H., Liang, Y., Zhou, S., Li, F., Wang, D., Fan, C., Shang, J., He, Q., Cheng, Y., Jiang, B., Qi, J., Chang, L., & Shuai, W. (2026). 4D injection molding. Nature Communications. doi:10.1038/s41467-026-71538-1
Wang, Y., & Lee, C. (2023). Design and optimization of conformal cooling channels for increasing cooling efficiency in injection molding. Applied Sciences.
Wu, Y.-F., & Yao, W.-S. (2026). Adaptive model predictive control with Lyapunov-based compensation for energy optimization in injection molding machines. The International Journal of Advanced Manufacturing Technology. doi:10.1007/s00170-026-17641-5
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