A. Arora

Neural network models of peak temperature, torque, traverse force, bending stress and maximum shear stress during friction stir welding

Abstract Tool and workpiece temperatures, torque, traverse force and stresses on the tools are affected by friction stir welding (FSW) variables such as plate thickness, welding speed, tool rotational speed, shoulder and pin diameters, pin length and tool material. Because of the large number of these welding variables, their effects cannot be realistically mapped by experiments. Here, we develop, test and make available a set of five neural networks to calculate the peak temperature, torque, traverse force and bending and equivalent stresses on the tool pin for the FSW of an aluminium alloy. The neural networks are trained and tested with the results from a well tested, comprehensive, three-dimensional heat and material flow model. The predictions of peak temperature and torque are also compared with appropriate experimental data for various values of shoulder radius and tool revolutions per minute. The models can be used even beyond the range of training with predictable levels of uncertainty.

Tool durability maps for friction stir welding of an aluminium alloy

Friction stir welding is not used for hard alloys because of premature tool failure. A scheme is created that exploits the physical three-dimensional heat and mass flow models, and implements them into a fast calculation algorithm, which, when combined with damage accumulation models, enables the plotting of tool durability maps that define the domains of satisfactory tool life. It is shown that fatigue is an unlikely mechanism for tool failure, particularly for the welding of thin plates. Plate thickness, welding speed, tool rotational speed, shoulder, and pin diameters and pin length all affect the stresses and temperatures experienced by the tool. The large number of these variables makes the experimental determination of their effects on stresses and temperatures intractable and the use of a well-tested, efficient friction stir welding model a realistic undertaking. An artificial neural network that is trained and tested with results from a phenomenological model is used to generate tool durability maps that show the ratio of the shear strength of the tool material to the maximum shear stress on the tool pin for various combinations of welding variables. These maps show how the thicker plates and faster welding speeds adversely affect tool durability and how that can be optimized.

Cooling rate in 800 to 500°C range from dimensional analysis

Abstract Structure and properties of steel welds are affected by their cooling rates in the 800 to 500°C range. The available cooling rate correlations are based mostly on heat conduction equation that ignores convective heat transfer and are inaccurate or empirical in nature and valid only for a limited range of welding conditions. Numerical heat and fluid flow models can accurately calculate cooling rates, but they are not widely available. In the present paper, the authors propose and test a generalised correlation for cooling rate developed through dimensional analysis, valid for a wide range of welding processes, operating parameters and material properties.

Wear-Induced Changes in FSW Tool Pin Profile: Effect of Process Parameters

Friction stir welding (FSW) of high melting point metallic (HMPM) materials has limited application due to tool wear and relatively short tool life. Tool wear changes the profile of the tool pin and adversely affects weld properties. A quantitative understanding of tool wear and tool pin profile is crucial to develop the process for joining of HMPM materials. Here we present a quantitative wear study of H13 steel tool pin profile for FSW of CuCrZr alloy. The tool pin profile is analyzed at multiple traverse distances for welding with various tool rotational and traverse speeds. The results indicate that measured wear depth is small near the pin root and significantly increases towards the tip. Near the pin tip, wear depth increases with increase in tool rotational speed. However, change in wear depth near the pin root is minimal. Wear depth also increases with decrease in tool traverse speeds. Tool pin wear from the bottom results in pin length reduction, which is greater for higher tool rotational speeds, and longer traverse distances. The pin profile changes due to wear and result in root defect for long traverse distance. This quantitative understanding of tool wear would be helpful to estimate tool wear, optimize process parameters, and tool pin shape during FSW of HMPM materials.

Towards attaining dissimilar lap joint of CuCrZr alloy and 316L stainless steel using friction stir welding

ABSTRACT CuCrZr alloy (Cu-0.8wt-%Cr-0.1wt-%Zr) and 316L stainless steel (Fe-0.03wt-%C-16wt-%Cr-10wt-%Ni) plates were successfully friction stir lap welded resulting in significant mechanical mixing of the two matrix elements, Cu and Fe, in the stir zone. The severe mixing not only led to improved load bearing response but also leads to form Cu-rich and Fe-rich regions in the weld nugget. The formation of these phases governs the failure mechanism of the joint. Tensile properties of the weld showed promising response when compared with joints made for the similar alloy pair by other welding techniques. This suggests strong feasibility of applying FSW for joining Cu and steel for nuclear applications.