Pengaruh Implementasi Sistem Robotik Berbasis Kecerdasan Buatan terhadap Kualitas Produk Pemesinan CNC
Abstract
This study aims to evaluate the impact of implementing an artificial intelligence (AI)-based robotic system on the quality of CNC machining products at PT Evergrown Technology Batam. A quantitative method was employed using purposive sampling on 25 observation units. The quality parameters analyzed include surface roughness and product cylindricity. Data analysis techniques involved linear regression and ANOVA tests to determine the significance of relationships between variables. The findings indicate that the application of AI technology in robotic systems significantly reduces surface roughness (Y = 2.736 − 0.136X; p < 0.001) and improves the cylindricity accuracy of the machined products (Y = 0.075 − 0.005X; p = 0.005). These results suggest that intelligent robotic systems make a substantial contribution to enhancing machining precision and overall product quality. Therefore, the implementation of such technology can serve as a strategic solution to improve quality outcomes in CNC-based manufacturing processes.
Keywords
Full Text:
PDFReferences
A. Borboni, K. V. V. Reddy, I. Elamvazuthi, M. S. AL-Quraishi, E. Natarajan, and S. S. Azhar Ali, “The Expanding Role of Artificial Intelligence in Collaborative Robots for Industrial Applications: A Systematic Review of Recent Works,” Machines, vol. 11, no. 1, 2023, doi: 10.3390/machines11010111.
A. Hentout, M. Aouache, A. Maoudj, and I. Akli, “Human–robot interaction in industrial collaborative robotics: a literature review of the decade 2008–2017,” Adv. Robot., vol. 33, no. 15–16, pp. 764–799, 2019, doi: 10.1080/01691864.2019.1636714.
IFR, “A positioning paper: Demystifying Collaborative Industrial Robots,” no. December, pp. 1–5, 2018, [Online]. Available: https://web.archive.org/web/20190823143255/https://ifr.org/downloads/papers/IFR_Demystifying_Collaborative_Robots.pdf
Y. Li et al., “A review on interaction control for contact robots through intent detection,” Prog. Biomed. Eng., vol. 4, no. 3, 2022, doi: 10.1088/2516-1091/ac8193.
D. Mukherjee, K. Gupta, L. H. Chang, and H. Najjaran, “A Survey of Robot Learning Strategies for Human-Robot Collaboration in Industrial Settings,” Robot. Comput. Integr. Manuf., vol. 73, no. July 2021, 2022, doi: 10.1016/j.rcim.2021.102231.
S. Robots, “WORLDTM 2022 Program,” Mol. Genet. Metab., vol. 135, no. 2, pp. S7–S14, 2022, doi: 10.1016/j.ymgme.2021.12.015.
L. Gualtieri, E. Rauch, and R. Vidoni, “Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review,” Robot. Comput. Integr. Manuf., vol. 67, no. May 2020, p. 101998, 2021, doi: 10.1016/j.rcim.2020.101998.
M. Maadi, H. A. Khorshidi, and U. Aickelin, “A review on human–ai interaction in machine learning and insights for medical applications,” Int. J. Environ. Res. Public Health, vol. 18, no. 4, pp. 1–21, 2021, doi: 10.3390/ijerph18042121.
E. Matheson, R. Minto, E. G. G. Zampieri, M. Faccio, and G. Rosati, “Human-robot collaboration in manufacturing applications: A review,” Robotics, vol. 8, no. 4, pp. 1–25, 2019, doi: 10.3390/robotics8040100.
M. Faccio et al., “Human factors in cobot era: a review of modern production systems features,” J. Intell. Manuf., vol. 34, no. 1, pp. 85–106, 2023, doi: 10.1007/s10845-022-01953-w.
C. A. Buckner et al., “We are IntechOpen , the world ’ s leading publisher of Open Access books Built by scientists , for scientists TOP 1 %,” Intech, vol. 11, no. tourism, p. 13, 2016, [Online]. Available: https://www.intechopen.com/books/advanced-biometric-technologies/liveness-detection-in-biometrics
F. Semeraro, A. Griffiths, and A. Cangelosi, “Human–robot collaboration and machine learning: A systematic review of recent research,” Robot. Comput. Integr. Manuf., vol. 79, no. August 2022, 2022, doi: 10.1016/j.rcim.2022.102432.
A. Desai, “A Review of Collaborative Robotics ( Cobots ) in Industrial Automation,” vol. 11, no. 1, 2025.
R. G. Lins and S. N. Givigi, “Cooperative Robotics and Machine Learning for Smart Manufacturing: Platform Design and Trends within the Context of Industrial Internet of Things,” IEEE Access, vol. 9, pp. 95444–95455, 2021, doi: 10.1109/ACCESS.2021.3094374.
M. Spezialetti, G. Placidi, and S. Rossi, “Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives,” Front. Robot. AI, vol. 7, no. December, pp. 1–11, 2020, doi: 10.3389/frobt.2020.532279.
Sugiyono, Metode Penelitian Kuantitatif, Kualitatif dan R&D. Bandung: Alfabeta, 2019.
S. Arikunto, Prosedur Penelitian: Suatu Pendekatan Praktik. Jakarta: Rineka Cipta, 2018.
DOI: https://doi.org/10.30596/rmme.v8i2.25524
Refbacks
- There are currently no refbacks.

This work is licensed under a Creative Commons Attribution 3.0 License
Jurnal Rekayasa Material, Manufaktur dan Energi is abstracting & indexing in the following databases:
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Statcounter View My Stats RMME
















