Automation and Process Optimization in Friction Stir Welding for Enhanced Manufacturing Efficiency
Main Article Content
Abstract
This paper focuses on Automation and Process Optimization in Friction Stir Welding for Enhanced Manufacturing Efficiency. Friction Stir Welding is an advanced solid-state joining process in which a non-consumable rotating tool generates frictional heat and plastic deformation to join similar or dissimilar materials without melting them. The study highlights that FSW is highly suitable for aluminium, magnesium, copper, titanium, steel, polymers and lightweight engineering materials because it reduces common fusion welding defects such as porosity, cracking, distortion, shrinkage and poor mechanical strength. The paper further explains the importance of automation in FSW through CNC machines, robotic systems, programmable controllers, sensors, real-time monitoring and feedback control mechanisms. Automation improves repeatability, consistency, accuracy, weld quality and productivity by reducing human error and maintaining stable process conditions. Along with automation, process optimization plays a key role in selecting the best welding parameters such as tool rotational speed, welding speed, axial force, plunge depth, tool tilt angle, tool geometry and tool material. Optimization techniques such as Taguchi method, Response Surface Methodology, Analysis of Variance, Grey Relational Analysis, Genetic Algorithm, Artificial Neural Network and machine learning help improve tensile strength, hardness, surface finish, microstructure and defect reduction. The study also discusses how automated FSW enhances manufacturing efficiency by reducing rework, saving material, lowering energy consumption, improving production speed and supporting sustainable manufacturing. However, challenges such as high initial cost, tool wear, fixture design, robotic stiffness, sensor reliability, dissimilar material joining, skilled manpower and maintenance requirements must be addressed. Overall, the paper concludes that the integration of automation and process optimization makes FSW more reliable, intelligent, economical and suitable for modern industrial manufacturing.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Ahmadkhaniha, D., Heydarzadeh Sohi, M., Zarei-Hanzaki, A., & Bayazid, S. M. (2015). Taguchi optimization of process parameters in friction stir processing of pure magnesium. Journal of Magnesium and Alloys, 3(2), 168–172.
Elangovan, K., & Balasubramanian, V. (2008). Influences of tool pin profile and tool shoulder diameter on the formation of friction stir processing zone in AA6061 aluminium alloy. Materials & Design, 29(2), 362–373.
Mehta, K. P., & Badheka, V. J. (2016). A review on dissimilar friction stir welding of copper to aluminum: Process, properties and variants. Materials and Manufacturing Processes, 31(3), 233–254.
Mishra, D., Sahu, S. K., & Maity, K. P. (2020). Real time monitoring and control of friction stir welding process using multiple sensors. Journal of Manufacturing Processes, 52, 1– 12.
Mishra, R. S., & Ma, Z. Y. (2005). Friction stir welding and processing. Materials Science and Engineering: R: Reports, 50(1–2), 1–78.