Investigation on the effect of process parameters on mechanical and microstructural properties of AA8011 similar FSW weld joints

Chakravarthi, G.; Giridharan, K.; Stalin, B.; Padmanabhan, S.; Sekar, Subramani; Nagaprasad, N.; Jule, Leta Tesfaye; Krishnaraj, Ramaswamy

doi:10.1177/16878132221112146

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…Studying the theoretical analysis of welding heat generation and the impact of different welding process parameters on weld quality is of great significance. [18][19][20] By establishing a welding heat generation model and analyzing the overall heat generation mathematical model during the welding process, a theoretical basis is provided for the optimization of welding process parameters.…”

Section: Heat Generation Model Of Static Shoulder Friction Stir Weldingmentioning

confidence: 99%

Intelligent optimization of non-rigidly supported stationary shoulder friction stir welding parameters

Jin,

Miao,

Cui

et al. 2024

Advances in Mechanical Engineering

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During the online welding of railway aluminum alloy trucks using fixed shoulder stirring friction welding technology, defects such as flying edges, cracks, and tunnels may occasionally occur due to poor coordination of the welding process parameters. This inconsistency can compromise the success rate of the welding. To enhance the quality and efficiency of the welding repairs, an optimization of the welding process parameters of the developed equipment was undertaken. An intelligent prediction model for welding parameters was established based on the BSO-BP neural network. The model was trained with 17 sets of authentic welding data, and its accuracy was validated through a comparison of predictive outputs with actual welding outcomes. The BSO-BP neural network demonstrated a maximum error of 1.85% and a relative error of 1.27%, showing a marked improvement over traditional BP neural network predictions. Utilize the optimized welding process parameters obtained from a trained predictive model to conduct welding experiments under actual working conditions. During the experiment, the deformation of the test plates was minimal. The resulting welds were smooth, free of defects such as flash and grooves, and the overall welding quality was good. The tensile strength of the welds, at 262.95 MPa, was close to the predicted results and reached 78.49% of the base material’s tensile strength. In the microstructure of the weld, sporadically distributed dimples of varying sizes can be observed, indicating the presence of second phase particles which contribute to enhancing the tensile strength of the weld, thereby meeting the strength requirements of the aluminum alloy car body. The parameter optimization method used has certain reference value and can provide guidance for similar research.

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Section: Heat Generation Model Of Static Shoulder Friction Stir Weldingmentioning

confidence: 99%

Intelligent optimization of non-rigidly supported stationary shoulder friction stir welding parameters

Jin,

Miao,

Cui

et al. 2024

Advances in Mechanical Engineering

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“…The high heat input during welding can cause a significant amount of material to be displaced into the weld area. To achieve high-quality welding and a favorable weld surface, it is necessary to modify the speed ratios (ω/v) within the range of 11 to 32 according to the literature to provide the suitable generated heat in the stirred zone [2,5]. Various welding parameters were employed to examine the cross-sectional characteristics of welded joints.…”

Section: Microstructure Observationmentioning

confidence: 99%

“…It is essential to recognize and comprehend the potential interactions that can take place between the welding parameters to maximize the performance of the FSW joint that is produced as a result of the welding process [2]. Considering that the FSW results can be sensitive to variations in some of the welding parameters, it is essential to do so, as mentioned by many authors [3][4][5]. Welding factors, tool geometry, and joint design significantly impact heat distribution, material flow pattern, formed structure, and material connection quality [6].…”

Section: Introductionmentioning

confidence: 99%

Effect of Friction Stir Welding Processing Parameters on the Mechanical Properties of AA1050 Aluminum Alloy

Saad,

abdelwanis,

Gaafer

et al. 2024

Engineering Research Journal (Shoubra)

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Friction stir welding (FSW) is gaining popularity in aluminum alloy joining due to its numerous advantages, such as creating high-quality joints with minimal heat input. This study examined the impact of different processing parameters, such as rotational and traverse speeds, on joints' quality and mechanical properties created through the FSW of AA1050 aluminum alloy. Various experiments were conducted to weld AA1050 aluminum alloy to investigate this, varying the rotational speeds (450-900 rpm) and traverse speeds (14-40 mm/min). The findings indicated that higher rotational speeds were associated with diminished joint quality, as indicated by heightened levels of porosity and the formation of intermetallic compounds. Moreover, an augmentation in traverse speed led to elevated tensile strength and hardness levels. The study determined that the processing parameters substantially impact the quality and mechanical properties of joints created through friction stir welding of AA1050 aluminum alloy. The study suggests that superior joints with favorable mechanical properties can be achieved by utilizing an intermediate rotational speed of 900 rpm and a traverse speed of 20 mm/min.

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Examining and optimizing the weld area and mechanical performance of thermoplastic parts manufactured by additive manufacturing and welded by friction stir welding

Güden,

Motorcu,

Yazıcı

2024

FME Transactions

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This study presents an experimental investigation into the weldability of ABS M30 (acrylonitrile butadiene styrene) plates produced by Additive Manufacturing (AM) using Friction Stir Welding (FSW). The effects of FSW process parameters on the yield stress and their optimal levels were determined using the Taguchi method. The optimal welding parameters were found to be a 16 mm tool shoulder diameter, 800 rpm tool rotation speed, and 10 mm/min traverse speed. The weld area of each sample welded using FSW was examined at a macroscopic level. The direction of tool rotation significantly affects the quality and strength of the FSW. When the FSW was performed with a clockwise rotation of the welding tool, a perfect weld could not be achieved. The tunnel effect resulted in gaps in the weld area of the samples at high rotation speeds. Differences were observed in the density between the weld area of the samples and the main parts.

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Investigation on the effect of process parameters on mechanical and microstructural properties of AA8011 similar FSW weld joints

Cited by 3 publications

References 31 publications

Intelligent optimization of non-rigidly supported stationary shoulder friction stir welding parameters

Intelligent optimization of non-rigidly supported stationary shoulder friction stir welding parameters

Effect of Friction Stir Welding Processing Parameters on the Mechanical Properties of AA1050 Aluminum Alloy

Examining and optimizing the weld area and mechanical performance of thermoplastic parts manufactured by additive manufacturing and welded by friction stir welding

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