Desain Adaptif dan Fleksibel pada Robotika Industri : Membuka Jalan Untuk Produksi Berkelanjutan dan Otomatisasi yang Efisien
DOI:
https://doi.org/10.58812/jmws.v2i6.435Kata Kunci:
Desain Adaptif, Desain Fleksibel, Robotika Industri, Produksi Berkelanjutan, Otomatisasi EfisienAbstrak
Studi penelitian ini mengkaji adopsi desain adaptif dan fleksibel dalam robotika industri dan implikasinya terhadap produksi yang berkelanjutan dan otomasi yang efisien. Fokus dari penelitian ini adalah memberikan rekomendasi untuk penerapan prinsip-prinsip desain ini dalam konteks sektor manufaktur Indonesia. Pendekatan penelitian kualitatif dengan menggunakan desain studi kasus digunakan, menggabungkan wawancara mendalam dengan pemangku kepentingan industri utama dan analisis dokumentasi dan laporan yang relevan. Temuan-temuannya mengungkapkan praktik-praktik terbaik yang diamati di negara-negara Eropa (ERofA) terkait desain yang adaptif dan fleksibel, termasuk integrasi sensor yang kuat, sistem kontrol cerdas, robotika kolaboratif, dan desain modular dan dapat dikonfigurasi ulang. Studi ini juga mengidentifikasi tantangan seperti investasi awal yang tinggi, kompleksitas teknologi, serta pelatihan dan penerimaan tenaga kerja. Berdasarkan temuan-temuan ini, rekomendasi yang diberikan untuk Indonesia menekankan pada dukungan pemerintah, kolaborasi dan berbagi pengetahuan, pengembangan keterampilan dan program pelatihan, proyek percontohan dan demonstrasi, kerangka kerja peraturan dan standar keselamatan, kolaborasi industri-akademisi, serta pemantauan dan evaluasi yang berkelanjutan. Dengan mengadopsi rekomendasi-rekomendasi ini, Indonesia dapat mendorong adopsi desain yang adaptif dan fleksibel dalam robotika industri, yang mengarah pada produksi yang berkelanjutan dan otomasi yang efisien di sektor manufaktur.
Referensi
Atstaja, D., Koval, V., Grasis, J., Kalina, I., Kryshtal, H., & Mikhno, I. (2022). Sharing Model in Circular Economy towards Rational Use in Sustainable Production. In Energies (Vol. 15, Issue 3). https://doi.org/10.3390/en15030939
Azad, F. A., Rahimi, S., Yazdi, M. R. H., & Masouleh, M. T. (2020). Design and Evaluation of Adaptive and Sliding Mode Control for a 3-DOF Delta Parallel Robot. 2020 28th Iranian Conference on Electrical Engineering (ICEE), 1–7. https://doi.org/10.1109/ICEE50131.2020.9261040
Benotsmane, R., Dudás, L., & Kovács, G. (2020). Survey on artificial intelligence algorithms used in industrial robotics. Multidiszciplináris Tudományok, 10, 194–205. https://doi.org/10.35925/j.multi.2020.4.23
Birglen, L. (2015). Enhancing versatility and safety of industrial grippers with adaptive robotic fingers. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2911–2916. https://doi.org/10.1109/IROS.2015.7353778
Chuyen, T. D., Doan, H. Van, Minh, P. Van, & Thong, V. V. (2023). Design of Robust Adaptive Controller for Industrial Robot Based on Sliding Mode Control and Neural Network. International Journal of Mechanical Engineering and Robotics Research, 12(3), 145–150. https://doi.org/10.18178/ijmerr.12.3.145-150
Dzedzickis, A., Subačiūtė-Žemaitienė, J., Šutinys, E., Samukaitė-Bubnienė, U., & Bučinskas, V. (2022). Advanced Applications of Industrial Robotics: New Trends and Possibilities. In Applied Sciences (Vol. 12, Issue 1). https://doi.org/10.3390/app12010135
Elouni, M., Hamdi, H., Rabaoui, B., & Braiek, N. B. (2022). Adaptive PID Fault-Tolerant Tracking Controller for Takagi-Sugeno Fuzzy Systems with Actuator Faults: Application to Single-Link Flexible Joint Robot. International Journal of Robotics and Control Systems; Vol 2, No 3 (2022). https://doi.org/10.31763/ijrcs.v2i3.762
Fair, N. (2022). Underpinnings Sociotechnical Theory : Theoretical. 1–7.
Gevorkyan, E., Chmiel, J., Wiśnicki, B., Dzhuguryan, T., Rucki, M., & Nerubatskyi, V. (2022). Smart Sustainable Production Management for City Multifloor Manufacturing Clusters: An Energy-Efficient Approach to the Choice of Ceramic Filter Sintering Technology. In Energies (Vol. 15, Issue 17). https://doi.org/10.3390/en15176443
Jagannath, V., Sanil, S., Kumar, P., Malhotra, A., Vighneswar, J., Pethakar, A. S., & Sangeetha, M. (2021). Locomotion and Path Planning for Roller Skating Dog Robot. 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom), 681–684.
Li, Y., Wang, J.-Q., & Chang, Q. (2018). Event-Based Production Control for Energy Efficiency Improvement in Sustainable Multistage Manufacturing Systems. Journal of Manufacturing Science and Engineering, 141(2). https://doi.org/10.1115/1.4041926
Lim, G. H., Lau, N., Pedrosa, E., Amaral, F., Pereira, A., Luís Azevedo, J., & Cunha, B. (2019). Precise and efficient pose estimation of stacked objects for mobile manipulation in industrial robotics challenges. Advanced Robotics, 33(13), 636–646. https://doi.org/10.1080/01691864.2019.1617780
Liu, C.-H., Chen, T.-L., Pai, T.-Y., Chiu, C.-H., Peng, W.-G., & Hsu, M.-C. (2018). Topology Synthesis, Prototype, and Test of an Industrial Robot Gripper with 3D Printed Compliant Fingers for Handling of Fragile Objects. 2018 WRC Symposium on Advanced Robotics and Automation (WRC SARA), 189–194. https://doi.org/10.1109/WRC-SARA.2018.8584239
Liu, X., Yang, C., Chen, Z., Wang, M., & Su, C.-Y. (2018a). Adaptive Neural Fuzzy Observer Design for Flexible Robot Joint Control. Neurocomputing, 275, 73–82. https://doi.org/https://doi.org/10.1016/j.neucom.2017.05.011
Liu, X., Yang, C., Chen, Z., Wang, M., & Su, C.-Y. (2018b). Neuro-adaptive observer based control of flexible joint robot. Neurocomputing, 275, 73–82. https://doi.org/https://doi.org/10.1016/j.neucom.2017.05.011
Matsunaga, F., Zytkowski, V., Valle, P., & Deschamps, F. (2022). Optimization of Energy Efficiency in Smart Manufacturing Through the Application of Cyber–Physical Systems and Industry 4.0 Technologies. Journal of Energy Resources Technology, 144. https://doi.org/10.1115/1.4053868
Molfino, R., Zoppi, M., Cepolina, F., Yousef, J., & Cepolina, E. E. (2014). Design of a Hyper-flexible cell for handling 3D Carbon fiber fabric. Recent Advances in Mechanical Engineering and Mechanics, 165–170.
Picard, M. (2020). An overview of the csa recent activities in space robotics. October.
Sahu, U. K., Patra, D., & Subudhi, B. (2021). Deep Reinforcement Learning Controller for Vision-Based Serial Flexible Link Manipulator. 2021 International Symposium of Asian Control Association on Intelligent Robotics and Industrial Automation (IRIA), 331–336. https://doi.org/10.1109/IRIA53009.2021.9588674
Shao, F., Meng, W., Ai, Q., & Xie, S. Q. (2021). Neural Network Adaptive Control of Hand Rehabilitation Robot Driven by Flexible Pneumatic Muscles. 2021 7th International Conference on Mechatronics and Robotics Engineering (ICMRE), 59–63. https://doi.org/10.1109/ICMRE51691.2021.9384827
Song, C., Huang, D., Xia, J., & Li, Y. (2022). Adaptive Virtual Guides for Compliance Control Skill Teaching in Partially Known Tasks. 2022 IEEE 17th Conference on Industrial Electronics and Applications (ICIEA), 786–791. https://doi.org/10.1109/ICIEA54703.2022.10005937
Sucipto, S., Sumbayak, P. W., & Perdani, C. G. (2020). Evaluation of Good Manufacturing Practices (GMP) and Sanitation Standard Operating Procedure (SSOP) Implementation for Supporting Sustainable Production in Bakery SMEs. Turkish Journal of Agriculture - Food Science and Technology, 8(1 SE-Research Paper), 7–12. https://doi.org/10.24925/turjaf.v8i1.7-12.1960
Takagi, S., & Uchiyama, N. (2005). Robust control system design for SCARA robots using adaptive pole placement. IEEE Transactions on Industrial Electronics, 52(3), 915–921. https://doi.org/10.1109/TIE.2005.847578
Top, N., Sahin, I., Mangla, S. K., Sezer, M. D., & Kazancoglu, Y. (2023). Towards sustainable production for transition to additive manufacturing: a case study in the manufacturing industry. International Journal of Production Research, 61(13), 4450–4471. https://doi.org/10.1080/00207543.2022.2152895
Valori, M., Scibilia, A., Fassi, I., Saenz, J., Behrens, R., Herbster, S., Bidard, C., Lucet, E., Magisson, A., Schaake, L., Bessler, J., Prange-Lasonder, G. B., Kühnrich, M., Lassen, A. B., & Nielsen, K. (2021). Validating Safety in Human–Robot Collaboration: Standards and New Perspectives. In Robotics (Vol. 10, Issue 2). https://doi.org/10.3390/robotics10020065
Zhang, C., Zhou, G., Yang, T., Song, N., Wang, X., Zhang, K., & Zhang, Z. (2020). Research on the identification of the moment of inertia of PMSM for industrial robots. 2020 Chinese Automation Congress (CAC), 1329–1334. https://doi.org/10.1109/CAC51589.2020.9327128
Zirkohi, M. M., & Izadpanah, S. (2017). Direct adaptive fuzzy control of flexible-joint robots including actuator dynamics using particle swarm optimization. Journal of AI and Data Mining, 5(1), 137–147.
Unduhan
Diterbitkan
Cara Mengutip
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2023 Supriandi
Artikel ini berlisensiCreative Commons Attribution-ShareAlike 4.0 International License.
Facebook 
Instagram 
Twitter 
Youtube 
+62 821 1557 5700
westscience.journals@gmail.com