Optimizing IRB1410 industrial robot painting processes through Taguchi method and fuzzy logic integration with machine learning-Reference-Cited by-同舟云学术

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Optimizing IRB1410 industrial robot painting processes through Taguchi method and fuzzy logic integration with machine learning

Published:2024-03-11 Issue:2 Volume:8 Page:380-400
ISSN:2366-5971
Container-title:International Journal of Intelligent Robotics and Applications
language:en
Short-container-title:Int J Intell Robot Appl

Author:

Santhosh R.,Sut Dhruba Jyoti,Uma M.,Sethuramalingam Prabhu

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s41315-024-00325-2.pdf

Reference25 articles.

1. Al-Refaie, A., Chen, T., Al-Athamneh, R., et al.: Fuzzy neural network approach to optimizing process performance by using multiple responses. J. Ambient Intell. Hum. Comput. 7, 801–816 (2016). https://doi.org/10.1007/s12652-015-0340-5

2. Benesl, T., Bradac, Z., Bastan, O., Arm, J., Kaczmarczyk, V.: Methods to decrease power consumption in industrial robotics. IFAC-PapersOnLine 51(6), 271–276 (2018). https://doi.org/10.1016/j.ifacol.2018.07.165

3. Casanova, J., Lima, J., Costa, P.: A Simulation tool for optimizing a 3D spray painting system. https://doi.org/10.1007/978-3-030-91885-9_9. (2021)

4. Chakraborty, S., Aithal, S.: Demonstration of drawing by robotic arm using RoboDK and C#. Int. J. Appl. Eng. Manag. Lett. 11, 153–158 (2021). https://doi.org/10.47992/IJAEML.2581.7000.0099

5. Foteinopoulos, P., Esnault, V., Komineas, G., et al.: Cement-based additive manufacturing: experimental investigation of process quality. Int. J. Adv. Manuf. Technol. 106, 4815–4826 (2020). https://doi.org/10.1007/s00170-020-04978-8

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1. Focused section on new trends on intelligent automation by industrial robots;International Journal of Intelligent Robotics and Applications;2024-05-31

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