Abstract
Robot-based painting industries optimize operations and enhance product quality by leveraging insights from real and virtual studies, encompassing trajectory patterns, paint film qualities, and machine learning for fault identification. Automation of fault identification procedures is the novel aspect of the study that helps to reduce human error and maintain consistent quality standards in manufacturing. This in-depth investigation examines the analysis of paint paths for robot painting with a focus on three distinctive movement patterns: linear, circular, and zigzag. The investigation includes assessments of smoothness for each route, along with morphological evaluations using Scanning Electron Microscope (SEM) pictures. The surface quality is assessed methodically using Taguchi L9 orthogonal testing, while Analysis of Variance (ANOVA) is utilised to identify the key factors that contribute to variations in paint qualities. In order to enhance quality control, machine learning is included to automate the classification and identification of flaws, utilising sophisticated picture analysis techniques. It is essential to incorporate virtual-environment experiments to ensure the accuracy and applicability of the results in real-world situations. This technique reveals crucial observations on the temporal difference between virtual and real surroundings, providing significant information for enhancing the painting process to better match the actual operational parameters. In addition, the analysis determines that the best combination of roughness is A3B3C2 using the Taguchi method, which results in an outstanding finish with a roughness value of 0.0347 µm. Verifying the efficacy of cutting-edge technology in honing painting techniques and improving end product quality, the machine learning model demonstrates a remarkable 94% accuracy in real-time flaw detection.
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Abbreviations
- SEM:
-
Scanning Electron Microscope
- ANOVA:
-
Analysis of Variance
- HVLP:
-
High-velocity, low-pressure
- LP:
-
Lower pressure
- LV:
-
Lower volume
- Ra:
-
Surface Roughness
- DOE:
-
Design of Experiments
- S/N Ratio:
-
Signal-to-noise ratio
- A(αi):
-
Centroid of the specific rule
- n:
-
Total number of rules
- Y:
-
Surface roughness (μm)
- A:
-
Coefficient
- X1 :
-
Nozzle Distance (mm)
- X2 :
-
Robot Speed (mm/sec)
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Acknowledgements
The authors thank the Robotics lab of Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nādu, India, for providing Robot facilities and necessary support.
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It is certified on behalf of corresponding author (Prabhu Sethuramalingam) that present research is not funded by any external agency.
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All authors contributed to the study conception and design. Taguchi L9 orthogonal experimental analysis and paint properties were performed by [Mr.Santhosh R and Prof.Prabhu Sethuramalingam] and Machine learning algorithm was prepared and executed by [Dr.M.Uma] and Fuzzy logic expert system was prepared and executed by [Mr.Dhruba Jyoti Sut]. The first draft of the manuscript was written and verified by [Mr.Santhosh R and Prof.Prabhu Sethuramalingam]. All authors read and approved the final manuscript.
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Santhosh, R., Sut, D.J., Uma, M. et al. Optimizing IRB1410 industrial robot painting processes through Taguchi method and fuzzy logic integration with machine learning. Int J Intell Robot Appl 8, 380–400 (2024). https://doi.org/10.1007/s41315-024-00325-2
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DOI: https://doi.org/10.1007/s41315-024-00325-2