Multi-objective optimization using Taguchi based grey relational analysis in friction stir welding for dissimilar aluminium alloy

• Published in: International journal on interactive design and manufacturing 2024-04, Vol.18 (3), p.1627-1644
• Main Authors: Karumuri, Srikanth, Haldar, Barun, Pradeep, A., Karanam, Sai Ashish Kumar, Sri, M. Naga Swapna, Anusha, P., Sateesh, N., Subbiah, Ram, Vijayakumar, S.
• Format: Article
• Language: English
• Subjects: Alloys; Aluminum; Aluminum base alloys; CAE) and Design; Composite materials; Computer-Aided Engineering (CAD; Content analysis; Corrosion resistance; Dissimilar material joining; Electronics and Microelectronics; Engineering; Engineering Design; Friction stir welding; Friction welding; Industrial Design; Instrumentation; Intermetallic compounds; Investigations; Mechanical Engineering; Mechanical properties; Multiple criterion; Multiple objective analysis; Nanoparticles; Neural networks; Optimization; Optimization techniques; Original Paper; Orthogonal arrays; Process variables; Taguchi methods; Tensile strength; Wear rate
• ISSN: 1955-2513; 1955-2505
• DOI: 10.1007/s12008-023-01529-9

In this study, the focus is on the joining of dissimilar Al alloys specifically AA6082 and AA5456 plates using friction stir welding (FSW). Several welding input variables, namely tool rotational speed (RS), welding speed (WS), tool tilt angle (TA), pin depth (PD), and cylindrical pin type (CP) are investigated to their impact on the tensile strength (UTS), hardness (BHN), and Specific Wear Rate (SWR) of the welds. To optimize the welding variables and enhance the mechanical properties (UTS, BHN, and SWR). This study aimed to optimize the variables of the friction stir welding (FSW) process using a combination of Taguchi and GRA methods and an understanding of the relationships between process variables by utilizing the two methods to optimize the FSW variables for AA6082 and AA5456 alloys. The Taguchi approach is used to create an L27 orthogonal array for FSW experiments. Subsequently, the multi-objective optimization problem is transformed into a single-objective optimization problem using the grey relational degree. The significance and influence of the different variables are determined using ANOVA. Through confirmation experiments and predictive analysis, the optimal variables are validated and compared with the obtained results. The findings indicate that the tool rotational speed is the most influential parameter significantly affecting the UTS, BHN, and SWR of the welds compared to the other investigated variables. The optimal factor combination identified using the GRA multi-criteria optimization technique is RS1-WS3-TA1-PD2-CP1, resulting in an approximate improvement in the grey relational grade (GRG) of 0.187.