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For FSW to produce sound joints, appropriate process parameters have to be used. As Mishra et al. [20] identified, the welding parameters, tool geometry, and design of the joint have a significant effect on the material flow pattern and temperature distribution. These, as a result, affect the final microstructure of the material.
Five different welding tools: designs (a,c,e,g,i) and products (b,d,f,h,j).
Welding was performed under various combinations of rotational and welding speed, to investigate the effect of tool design on mechanical strength. The tensile test was performed with an Imada MX2 (Northbrook, IL, USA) tension test machine, using grips of 40 mm at a 10 mm/min crosshead speed. Rectangular specimens were prepared from the welded aluminum sheets (Figure 4b). The tensile test specimens were not produced to a technical standard due to their size. For each setting of tool design and welding speed, five tensile tests were performed. The experimental setup for the tensile tests and a snapshot of a tensile test experiment on a sample after the failure of the sample are depicted in Figure 5a,b, respectively. An identical geometry to the welded samples was also prepared from the base material, the tensile strength was measured, and for comparison purposes, the weld efficiency, i.e., the ratio of the tensile strength of the weld to that of the base material, was calculated.
It was also found that, at the times when the tool or welding speed was altered, the initial welding run was rarely successful, and most of the time did not produce a uniform weld along its length while it failed. This was associated with the low initial temperature of the tool. When the tool was used again, welds were sound as the tool had been heated from the initial run. However, if welding was to be repeated on the same sample for a second time, a small exit crack would form before lifting the tool. These issues relating to the first run can be resolved by pre-heating the tool. The development of exit cracks requires further study to address them properly.
The third tool, T3P3R2, shows improved mechanical strength measurements at welding speeds of 110 mm/min, 80 mm/min, and 150 mm/min. All mechanical strength measurements are consistent, with no large range of measurements in comparison to the other tool designs. It appears that the lowest mechanical strength achieved is at a welding speed of 300 mm/min, as shown in Figure 5e.
The fifth tool, T5P3OC, shows improved mechanical strength at a welding speed of 250 mm/min, as well as at a welding speed of 110 mm/min, while the strength at 200 mm/min is lower. All these settings showed an appreciable range in mechanical strength measurements in comparison to the other tools. Finally, as shown in Figure 5g, the lowest mechanical strength measured is at a welding speed of 80 mm/min. The combination of this welding speed together with the off-center pin did not produce favorable conditions for a strong weld. It should be stated that this tool design produces the lowest mechanical strengths measured compared to others.
When comparing the various tool designs, results in Figure 6a show that, at a welding speed of 80 mm/min, the highest strength is achieved with welds produced with tool T4P3G0.5, while tools T1P3 and T3P3R2 produce similar results, with the latter having a larger range of measurements. The tool design which failed, as it produced very poor results, was T5P3OC, possibly due to the extensive plastic material mixing that resulted, given the thickness of the sheets.
For welds performed at a welding speed of 110 mm/min (Figure 6b), the highest mechanical strength was achieved with tool T2P4, although close mechanical strengths were achieved with tools T3P3R2 and T4P3G0.5. The largest range of measurements in mechanical strength was measured with tool T1P3 compared to the others. Of all the tool designs, tool design T5P3OC again exhibited the lowest mechanical strength, as shown in Figure 6b.
For a welding speed of 150 mm/min (Figure 6c), the highest mechanical strength achieved was with tool T2P4, and close to that was with tools T3P3R2 and T4P3G0.5. None of the tool designs used had a large range in measurements. Again, tool T5P3OC showed the lowest mechanical strength, as depicted in Figure 6c.
For a welding speed of 250 mm/min (Figure 6e), the highest mechanical strength achieved was with tool T2P4. The mechanical strength achieved was much larger compared to the other tool designs, and tool design T5P3OC demonstrated the lowest mechanical strength and largest range of values measured, as shown in Figure 6e.
Finally, for a welding speed of 300 mm/min (Figure 6f), the largest mechanical strength achieved was for welds produced with tool T1P3, and similar to that was tool design T2P4, which showed a larger range in mechanical strengths measured. In addition, the mechanical strength of the welds produced with tool T4P3G0.5 had a large range in measurements. As before, tool T5P3OC showed the lowest mechanical strength, as depicted in Figure 6f.
The overall comparison shows that tool T2P4 produces welds with the highest mechanical strength under all welding settings studied. This tool design produced the best results at welding speeds of 250 mm/min and 150 mm/min. In addition, tool designs T3P3R2 and T4P3G0.5 show that at a lower welding speed, their tensile strength was high, but as the speed was increased, their tensile strength was affected negatively in a significant manner. The tool design T1P3 produced welds of inconsistent mechanical strength at various welding conditions, even though it was the best tool design at 300 mm/min welding speed. The tool design T5P3OC showed consistently poor mechanical strength at all welding conditions, with fracture stresses never exceeding 65 MPa. A 3D plot was made to compare the tensile strengths of the five welding tool designs at the welding speeds studied, as shown in Figure 9. These differences in the results between the different weld tools reflect the pronounced effect of tool geometry on mechanical strength. Figure 9 combines all these measurements. So, tool T2P4 produces poor welds at 80 mm/min, but in all other settings produces sound welds of great mechanical strength compared to the other tool designs.
Experimental Prediction of Potential Fatigue Crack Path on Concrete Surface Guo Li-ping 1 , Sun Wei 1 , He Xiao-yuan 2 ( 1 College of Materials Science and Engineering, Southeast University, Nanjing 210096, CHINA. E-mail address: guoliping691@sohu.com, sunwei@seu.edu.cn 2 College of Civil Engineering, Southeast University, Nanjing 210096, CHINA) ABSTRACT. Before the 21 th century, it was difficult to predict the potential fatigue crack path of heterogeneous materials by experimental approaches. The crack path was usually analyzed by numerical simulation programs in the world during that period. However, numerical programs were not always consistent with the experimental results because of the variety of materials used in structures. Along with the development of Digital Speckle Correlation Method (DSCM) in recent years, this new non-destructive testing technique has presented its advantages in on-line prediction and inspection of the potential crack paths on specimen surfaces. To testify the feasibility and accuracy of DSCM system, two different concrete specimens under flexural fatigue loading and a matched software UU © were employed in this paper. By use of global and local strain fields on target surfaces, the start of potential fatigue crack path predicted by DSCM system is coincident with the real one observed from experiments. It is testified that DSCM system is accurate and effective in on-line prediction of potential fatigue crack path of heterogeneous specimen under flexural cyclic loading. Especially, the experimental results show that it is beneficial to the safety evaluation and structural design of critical components or structures in practice. In addition, fatigue testing circumstances should be still and clean to assure more precise analysis results of DSCM system. Keywords: flexural fatigue, concrete, crack path, strain field, DSCM INTRODUCTION Prediction of potential fatigue crack path is vital for safety evaluation and structural design of critical components or structures, e.g. bridges, seashore structures and runway, et al [1]. Because of the complex stress distribution on structure surface, it is therefore a challenge for engineers and research scientists to predict the start of crack path,
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