Simulasi dan Analisa Time Motion Robot Lengan 6-Axis Pada Proyek Otomasi Heating Line untuk Manufaktur Pegas Daun di PT.XYZ

Arini Latifah; Denny Irawan

doi:10.30595/jrre.v7i1.26451

Authors

Arini Latifah Universitas Muhammadiyah Gresik
Denny Irawan Program Studi Teknik Elektro, Fakultas Teknik, Universitas Muhammadiyah Gresik

DOI:

https://doi.org/10.30595/jrre.v7i1.26451

Keywords:

Otomasi, Robot, Manufaktur, Studi Kelayakan

Abstract

Penelitian ini bertujuan untuk melakukan studi kelayakan berbasis simulasi terhadap penerapan sistem otomasi menggunakan robot lengan 6-axis pada lini produksi heating di industri manufaktur pegas daun. Studi kelayakan dilakukan dengan menggunakan perangkat lunak Roboguide untuk menganalisis kelayakan teknis, waktu siklus, dan produktivitas dari sistem yang diusulkan. Hasil simulasi menunjukkan bahwa sistem robotik mampu mengurangi waktu siklus transfer material sebesar 50%, di mana waktu siklus transfer robot tercatat 17 detik, dibandingkan dengan 34 detik pada transfer manual. Penggunaan robot juga berhasil mengoptimalkan suhu pemanasan material, dengan suhu yang tercatat 920°C, lebih rendah 5% dibandingkan dengan transfer manual 970°C, yang berdampak pada efisiensi bahan bakar dari heating furnace. Dalam hal produktivitas, sistem robotik meningkatkan output produksi hingga 200%, dengan biaya operasional yang berkurang 64%, serta biaya manufaktur per unit yang turun hingga 82%. Secara keseluruhan, simulasi ini mengindikasikan bahwa integrasi robot lengan dalam sistem otomasi dapat meningkatkan efisiensi biaya, kualitas produk, serta daya saing industri manufaktur.

References

[1] D. A. Gök, “Prototype production and investigation of mechanical properties of leaf springs used in air suspension systems,” Eng. Solid Mech., vol. 12, no. 1, pp. 33–40, 2024, doi: 10.5267/j.esm.2023.7.004.

[2] S. Sanjeev Vyaas, G. Shamsudeen Babu, S. S. Raghulnath, P. Manikandan, and E. Joel, “Robotic Arm for Industry Automation,” in Intelligent Robots and Cobots, 1st ed., V. Ramasamy, S. Balamurugan, and S. Peng, Eds., Wiley, 2025, pp. 203–220. doi: 10.1002/9781394198252.ch10.

[3] M. Gooroochurn, “Robotics and Automation in Industry 4.0,” in Intelligent and Sustainable Engineering Systems for Industry 4.0 and Beyond, 1st ed., Boca Raton: CRC Press, 2025, pp. 71–91. doi: 10.1201/9781003511298-4.

[4] R. Adekola Adebayo, N. Constance Obiuto, I. Clinton Festus-Ikhuoria, and O. Kayode Olajiga, “Robotics in Manufacturing: A Review of Advances in Automation and Workforce Implications,” Int. J. Adv. Multidiscip. Res. Stud., vol. 4, no. 2, pp. 632–638, Mar. 2024, doi: 10.62225/2583049X.2024.4.2.2549.

[5] P. Pasang, P. Kerdsomya, J. Kunanoppadol, V. Mettanant, and T. Katejanekarn, “Simulation of a welding station with RobotStudio,” Sci. Eng. Health Stud., p. 24040010, Dec. 2024, doi: 10.69598/sehs.18.24040010.

[6] F. Müller, M. Koch, and A. Hasse, “User Study to Validate the Performance of an Offline Robot Programming Method That Enables Robot-Independent Kinesthetic Instruction through the Use of Augmented Reality and Motion Capturing,” Robotics, vol. 13, no. 3, p. 35, Feb. 2024, doi: 10.3390/robotics13030035.

[7] H. Fu, Y. Bai, R. Sun, and C. Pang, “Technical analysis and research based on offline programming software for robots,” in International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2022), Q. Deng, Ed., Shijiazhuang, China: SPIE, Aug. 2022, p. 105. doi: 10.1117/12.2645794.

[8] Petroleum-Gas University of Ploiesti, Romania, D.-L. Baboi, J. Andreoiu, Petroleum-Gas University of Ploiesti, Romania, G. Bucur, and Petroleum-Gas University of Ploiesti, Romania, e-mail:[email protected], “ARC WELDING ROBOTIC FLEXIBLE CELL SIMULATION USING ROBOGUIDE SOFTWARE,” Romanian J. Pet. Gas Technol., vol. 5 (76), no. 1, pp. 145–158, Aug. 2024, doi: 10.51865/JPGT.2024.01.11.

[9] A. A. Santos, J. Haladus, F. Pereira, C. Felgueiras, and R. Fazenda, “Simulation Case Study for Improving Painting Tires Process Using the Fanuc Roboguide Software,” in Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems, F. J. G. Silva, A. B. Pereira, and R. D. S. G. Campilho, Eds., in Lecture Notes in Mechanical Engineering. , Cham: Springer Nature Switzerland, 2024, pp. 517–524. doi: 10.1007/978-3-031-38241-3_58.

[10] N. Rawashdeh, R. Bondalapati, P. Patil, G. S. Ajmani, and S. Akki, “Hardware-in-the-loop Simulation of a Programmable Logic Controller, Industrial Robots and Conveyor Systems Using RoboGuide,” in 2024 22nd International Conference on Research and Education in Mechatronics (REM), Amman, Jordan: IEEE, Sep. 2024, pp. 132–137. doi: 10.1109/REM63063.2024.10735684.

[11] B. Lal, V. S. N, M. A. Kumar, N. Chinthamu, and S. Pokhriyal, “Development of Product Quality with Enhanced Productivity in Industry 4.0 with AI Driven Automation and Robotic Technology,” in 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India: IEEE, Aug. 2023, pp. 184–189. doi: 10.1109/ICAISS58487.2023.10250736.

[12] H. Zhang, “Optimization and Efficiency Improvement of Robot-based Industrial Production Process,” Int. J. New Dev. Eng. Soc., vol. 8, no. 2, 2024, doi: 10.25236/IJNDES.2024.080214.

[13] H. K. Banga, P. Kalra, R. Kumar, S. Singh, and C. I. Pruncu, “Optimization of the cycle time of robotics resistance spot welding for automotive applications,” J. Adv. Manuf. Process., vol. 3, no. 3, p. e10084, Jul. 2021, doi: 10.1002/amp2.10084.

[14] M. Zhang, “Practical Analysis of Mechanical Automation Technology in Automobile Manufacturing,” J. Electron. Res. Appl., vol. 7, no. 5, pp. 26–31, Sep. 2023, doi: 10.26689/jera.v7i5.5367.

[15] Z. Zhang, R. Dershan, A. M. S. Enayati, M. Yaghoubi, D. Richert, and H. Najjaran, “A High-Fidelity Simulation Platform for Industrial Manufacturing by Incorporating Robotic Dynamics Into an Industrial Simulation Tool,” IEEE Robot. Autom. Lett., vol. 7, no. 4, pp. 9123–9128, Oct. 2022, doi: 10.1109/LRA.2022.3190096.

[16] L. Zhang and Z. Yu, “Industrial robot simulation manufacturing based on big data and virtual reality technology,” J. Robot. Intell. Agents Artif. Intell., vol. 14, no. 1, Aug. 2023, doi: https://doi.org/10.1515/pjbr-2022-0124.

[17] M. Gautam, H.-M. Yonamine, and F. Christophe, “Robotic Simulation to implementation: An industrial case study,” presented at the The Eleventh International Conference on Engineering Computational Technology, Montpellier, France, pp. 1–8. doi: 10.4203/ccc.2.16.2.

[18] W. A. Szulc and P. Czop, “The Effectiveness of a Robotic Workstation Simulation Implementation in the Automotive Industry Using a Closed-Form Solution of the Absolute Orientation Problem,” Robotics, vol. 13, no. 11, p. 161, Oct. 2024, doi: 10.3390/robotics13110161.

[19] L. Klingel, A. Heine, S. Acher, N. Dausend, and A. Verl, “Simulation-Based Predictive Real-Time Collision Avoidance for Automated Production Systems,” in 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), Auckland, New Zealand: IEEE, Aug. 2023, pp. 1–6. doi: 10.1109/CASE56687.2023.10260637.