Advancing Tilt Angle Control in Friction Stir Welding using Fuzzy Logic
Article Sidebar
Main Article Content
Friction Stir Welding (FSW) is a solid-state joining technique renowned for its ability to produce high-quality welds in aluminum and other non-ferrous metals. A critical parameter influencing weld integrity is the tool tilt angle, which, if improperly set, can lead to defects such as tunnel voids, surface irregularities, or poor material mixing. This study presents a fuzzy logic-based control system for real-time adjustment of the tool tilt angle during FSW. The proposed system utilizes inputs such as torque, temperature, and plunge depth deviation to infer optimal tilt corrections using a Mamdani-type fuzzy inference system. Triangular membership functions and an expert-defined rule base were implemented in MATLAB to simulate adaptive tilt adjustments. Simulation results showed that for a torque input of 10 Nm, a temperature of 45 °C, and a plunge deviation of 0.5 mm, the system produced a tilt adjustment value of 0.224°, as calculated through centroid defuzzification. Surface plots confirmed that the controller responds smoothly to varying conditions, with tilt adjustments ranging from -0.4° to +0.5° based on combined sensor inputs. The proposed system offers a robust and flexible solution suitable for integration into CNC-based FSW platforms for improved automation and weld quality.
Downloads
References
G. Sahu, P. K. Sen, R. Sharma, and S. Bohidar, “A Review on Analysis of Friction Stir Welding Process of Steel,” IJSRST -Themed Sect. Eng. Technol., vol. 1, no. 5, pp. 61–64, 2015.
D. A. P. Prabhakar et al., “A Review of Optimization and Measurement Techniques of the Friction Stir Welding (FSW) Process,” J. Manuf. Mater. Process., vol. 7, no. 5, 2023, doi: 10.3390/jmmp7050181.
E. T. A. and S. A. Akinlabi, Friction stir welding of dissimilar metals. Woodhead Publishing Series in Welding and Other Joining Technologies, 2014. doi: 10.1533/9780857094551.241.
A. H. Elsheikh, “Applications of machine learning in friction stir welding: Prediction of joint properties, real-time control and tool failure diagnosis,” Eng. Appl. Artif. Intell., vol. 121, no. 105961, 2023.
T. A. Shehabeldeen, M. A. Elaziz, A. H. Elsheikh, and J. Zhou, “Modeling of friction stir welding process using adaptive neuro-fuzzy inference system integrated with harris hawks optimizer,” J. Mater. Res. Technol., vol. 8, no. 6, pp. 5882–5892, 2019, doi: 10.1016/j.jmrt.2019.09.060.
I. Erenoglu, I. Eksin, E. Yesil, and M. Guzelkaya, “An intelligent hybrid fuzzy PID controller,” 20th Eur. Conf. Model. Simul. Model. Methodol. Simul. Key Technol. Acad. Ind. ECMS 2006, vol. 5, no. Cd, pp. 62–66, 2006, doi: 10.7148/2006-0062.
R. M. M. and I. S. Noah E El-Zathry*, Stephen Akinlabi, Wai Lok Woo, Vivek Patel, “Enhancing Friction Stir-Based Techniques with Machine Learning: A Comprehensive Review,” Mach. Learn. Sci. Technol, 2025.
M. P. Iqbal, R. Jain, and S. K. Pal, “Numerical and experimental study on friction stir welding of aluminum alloy pipe,” J. Mater. Process. Technol., vol. 274, no. June, p. 116258, 2019, doi: 10.1016/j.jmatprotec.2019.116258.
J. Backer, “Feedback Control of Robotic Friction Stir Welding,” University West SE-46186 Trollhättan Sweden, 2014. [Online]. Available: https://www.diva-portal.org/smash/record.jsf?pid=diva2:706044
M. P. Mubiayi and E. T. Akinlabi, “An overview on friction stir spot welding of dissimilar materials,” in Transactions on Engineering Technologies: World Congress on Engineering and Computer Science 2014, 2015, pp. 537–549. doi: 10.1007/978-94-017-7236-5_38.
S. Verma and V. Kumar, “An Overview of Friction Stir Welding Tools,” Adv. Manuf. Technol. Comput. Mater. Process. Charact., vol. 5762, pp. 19–27, 2022, doi: 10.1201/9781003203681-4.
C. Q. Zhang, J. D. Robson, O. Ciuca, and P. B. Prangnell, “Microstructural characterization and mechanical properties of high power ultrasonic spot welded aluminum alloy AA6111-TiAl6V4 dissimilar joints,” Mater. Charact., vol. 97, pp. 83–91, 2014, doi: 10.1016/j.matchar.2014.09.001.
L. Hang and D. Kim, “Enhanced Model-Based Predictive Control System Based on Fuzzy Logic for Maintaining Thermal Comfort in IoT Smart Space,” MDPI-Applied Sci., vol. 8, no. 1031, 2018, doi: 10.3390/app8071031.
T. Nasir, M. Asmael, Q. Zeeshan, and D. Solyali, “Applications of Machine Learning to Friction Stir Welding Process Optimization,” J. Kejuruter., vol. 32, no. 2, pp. 171–186, 2020, doi: 10.17576/jkukm-2020-32(2)-01.
E. Marliana, A. Wahjudi, L. Nurahmi, I. M. L. Batan, and G. Wei, “Optimizing the Tuning of Fuzzy-PID Controllers for Motion Control of Friction Stir Welding Robots,” J. Robot. Control, vol. 5, no. 4, pp. 1002–1017, 2024, doi: 10.18196/jrc.v5i4.21697.
Y. Chen and M. Jean, “Multi-response optimization of friction stir welding using fuzzy-grey system,” High Temp. Mater. Process., vol. 43, no. 1, 2024, doi: 10.1515/htmp-2024-0005.
K. Balachandar and R. Jegadeeshwaran, “Enhancing Friction Stir Welding: Quality Machine Learning Based Friction Stir Welding Tool Condition Monitoring,” Int. Res. J. Multidiscip. Technovation, vol. 6, no. 3, pp. 245–259, 2024, doi: 10.54392/irjmt24319.
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.