Authored by Ibrahim Sabry*
Abstract
This paper addresses the development of a fuzzy model for weld quality prediction. Significant underwater friction stir welding parameters affecting the weld quality are rotational speed, traverse speed, and tool shoulder diameter . Welding experiment is performed on AA 1050 aluminium pipes by central composite design to attain maximum tensile strength and hardness of the weld joint. Quality of weld measured in terms of tensile strength and hardness is predicted using fuzzy logic and the results are compared with statistical analysis. Confirmatory experimental results show that the fuzzy model can predict an adequate output with less error than statistical analysis.
Keywords: Friction stir welding; Fuzzy logic; Tensile strength; Hardness test
Introduction
A Patented in 1991 by researchers at The Welding Institute of Cambridge, England, Friction Stir Welding (FSW) is a novel solid state joining process used in applications worldwide [1]. The process, which occurs below the melting temperature of the joint material, represents a departure from traditional fusion welding
methods. In conventional FSWoP, a rotating tool is plunged into the surface of adjoining metal pipes. The rotation of the tool generates heat at the interface, resulting in local plasticization of the material due to shear stress. As the tool traverses along the joint line, the material behind the tool consolidates, forming a welded region with a width roughly corresponding to the diameter of the tool in contact with the surface [2].
In Figure 1 observation, the mechanism is clarified that the advancing and retreating sides of the weld are established relative to the direction of tool rotation. The advancing side is the weld side where the rotation of the tool is in the same direction as the traverse; the retreating side is the opposite side of the rotation of the tool and the direction of welding. Material is swept from the forward side during a weld and deposited on the retreating side. As a relatively new technology, a lot of research has devoted to the influences of process parameters on the quality of the finished joint.
There are four key process parameters which can be differ in FSW: rotation speed, traverse speed, tilt angle, and shoulder diameter. Rotation speed (N) is in units of rotations per minute (rpm) and designates the rate at which the tool rotates. Traverse speed (F) indicates the speed at which the tool traverses the material. ν is usually specified in units of inches per minute (mm/ min) [3,4].
The essential parameters influencing the two processes are typically as follows: tool rotational speed of the tool [5,6], The tool’s travel speed in the FSW process of the plates [7] while it is referred to the rotational speed of pipes in FSWoP process [8], tool pin profile [9,10], joint thickness [11]. It was evident from all these studies that the optimum levels of these parameters for optimizing the FSW of plates are not the same for the FSWoP process under the same conditions. As an example, the FSW of plates has been studied in [12] and the FSWoP in [13] under the same range of rotational speed. The effect of rotational speed for the plate on the joint’s tensile strength is entirely different from that for the pipes [14]. The Process parameters such as tool rotation speed (N) and travel speed (F) determine the strength and efficiency of the joints generated by the FSWoP process [15]. Research has been applied out in the improvement of the FSWoP process as a distinguished solid-state join the ng process ever since its invention [16]. According to the results of this paper, Al 7075-T7351 is a quench prone alloy due to faster natural aging response and enhanced mechanical properties. The natural response to aging has been assessed through the testing of transverse tensile properties and micro-hardness. Most notably, the conditions of water cooling increased the properties of tensile by about 10 percent above normal FSW. Fu RD, et al. [17] too states that tensile strength of Underwater FSW method reached 75% of base metal and the elongation is comparably better than the normal FSW joint. Moreover, the hardness and tensile strength of Underwater FSW joint were improved compared to normal FSW joint properties. Darras B, et al. [18] studied Underwater FSW for Al AA2219-T6 to clarify the enhanced value in tensile strength compared to normal FSW joint. This study also proved that the influences of water cooling is the essential cause for the underwater FSW joint to increase the strength.
Upadhyay P, et al. [19] again, focused their research on underwater FSW of Al 2219 to moreover advance the mechanical properties of the joint with varying welding temperature history. This work is able to discover that external water-cooling action in underwater FSW developed the normal FSW joint tensile value from 324 to 341 MPa. Liu H, et al. [20] conducted experimental study to different welding conditions in Underwater FSW of 6061 aluminium alloy. The experimental outcome proved that underwater joint generates less peak temperature compared to normal FSW joint [21]. The grain sizes were predicted using boundary migration model. In addition, Transmission Electron Microscopy (TEM) was used to characterize the microstructures. Kishta EE, et al. [22] conducted experimental study to Underwater FSW of AL AA2219-T6 performed an experimental analysis for improving mechanical properties in the heat-affected area (HAZ). The experimental observations during microstructural analysis uncovered that the hardness of the HAZ can be enhanced with UFSW method due to the narrowing of precipitate free zone.
Cheng, et al. [23] explain the fuzzy control of the feed rate in the final milling process. different in the cutting parameters and nonlinearities in the processes are the drawbacks in the development of suitable mathematical models. Fuzzy logic technology offers an alternative approach and models a system as a black box with variables of input and output [24,25]. The fundamental frequencies of electromagnetic radiation emitted during the FSW welds tensile failure, generated at different process parameters, were analysed using fuzzy modelling [26]. It was estimated that the fundamental frequency of weld failures from the fuzzy model was closer to the experimental results. Dewan MW, et al. [27] found that the number of the membership functions (MF) and their locations on the universe of discourse influenced the fuzzy algorithm, compared to the shape variations of MFs. Zhang Q, et al. [28] developed a systematic datadriven fuzzy modelling approach to AA5083 Aluminum alloyrelated FSW behaviour with microstructural features, mechanical properties, and overall weld quality. The extracted models have proven to be accurate, interpretable, and resilient, and can be applied to facilitate the optimal design of process parameters to achieve the desired welding properties. Hence, the FSW process parameters, rotation speed, travels speed, and shoulder diameter, effect the tensile strength and hardness underwater FSWoP joints. There is a gap between predicting tensile strength and hardness for different parameters and for various materials. This gap could be filled by developing a fuzzy logic-based model for the underwater FSWoP process. In this work, an attempt was made to develop a straightforward fuzzy model for the prediction of weld strength and hardness, within the range of the process parameters, which could provide a perfect joint through utilize underwater FSWoP.
To read more about this article...Open access Journal of Modern Concepts in Material Science
To know more about our Journals....Iris Publishers
No comments:
Post a Comment