Ultrasonic Spot Welding of Dissimilar Metal Sheets: An Experimental, Numerical and Metallurgical Investigation

Satpathy, Mantra Prasad (2017) Ultrasonic Spot Welding of Dissimilar Metal Sheets: An Experimental, Numerical and Metallurgical Investigation. PhD thesis.

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Abstract

Ultrasonic metal welding (USMW) is a new and emerging concept used in the industries over the past twenty years and serving to the manufacturing sectors like aviation, medical, microelectronics, automotive and much more due to various hurdles faced by conventional fusion welding process. USMW is a clean and reliable technique in which the welding takes place with a high energy, no flux or filler metal needed, longer tool life and it takes very short time (less than one second) to weld materials in a perfect controllable environment with greater efficiency.To acquire high vibration amplitude in USMW, there is a necessity to design a welding system that consists of components like a booster and horn. The principal purpose of these parts is to amplify the input amplitude of vibration so that the energy transferred to the welding spot should be sufficient for creating a joint. In the present study, new type of booster and horn are proposed and modelled with adequate precision not only to produce high-quality welds but also to solve a lot of issues faced while designing these types of ultrasonic tools. The modal analysis module of finite element method (FEM) is used to
analyze the effects of different step lengths and fillet radius on its natural frequency of 20 kHz, ensuring that these components will be in a resonating condition with other parts of the system. It is found that there were 1.11 % and 2.52 % errors in the length calculation of both parts. Similarly, 0.61 % error is obtained for both while calculating the magnification ratio. However, such low levels of errors may be considered to be insignificant. The dynamic analysis has also been performed to find out the stress distribution in both parts under cyclic loading conditions. Due to these cyclic loading conditions, the nodal regions (hot areas) are under highly stressed, and the relevant temperature field is consequently determined. The results obtained from the simulation, and experimental results were found to be close to each other and an error of 2% was noticed. Other welding components are also fabricated such as anvil, specimen-holder and backing plate for producing a satisfactory weld. Meanwhile, the complex mechanism behind the USMW has been addressed and modelled analytically. This model can predict the forces as well as temperatures those occur during the welding process and also explains the effects of various material properties and surface conditions on the weld behaviour. The experiments have been performed on the aluminium, copper, brass and stainless steel metal sheets with a number of different configurations, anvil designs, and surface conditions. The fundamental aspect of this study is to control the process parameters like vibration amplitude, weld pressure and weld time so that, an appreciable weld strength can be obtained. Thus, tensile shear and T-peel failure load studies suggest that increase in vibration amplitude means the increase of scrubbing action between the faying surfaces, resulting a better bonding strength. Similarly, increase in weld pressure also increases these weld failure loads and reach a peak value at a particular pressure. But, subsequently, these failure loads decrease due to suppression of relative motion between sheets and initiation of cracks. Excessive weld time also causes cracks around the weld spot. Likewise, if the thickness of the sheets increased, weld strengths are also increased
due to absorption of more amount of ultrasonic energy. Moreover, the highest weld interface temperatures and weld areas are observed at the end of weld time because of the larger plastic deformation at the mating surfaces. For all the experiments, first anvil design shows maximum failure loads due to its non-cutting width and angle of knurls. Likewise, on the increase of surface roughness, the tensile shear, and T-peel failure loads decrease. It is found that, in lubricating condition, the highest failure loads are obtained. Furthermore, the polynomial regression, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) methods are developed and compared for each performance measure so that the whole welding process can be accurately described by a best predictive model. A welding mechanics based numerical model has been developed which can predict the temperatures during USMW process for various surface conditions. For all the experimental investigations, the predictive results show good agreement with the experimental values. In addition to it, acoustic softening during ultrasonic welding is found to very significant for the reduction in yield strength of the weld material up to 95 %. It is seen that the quality of welding depends on the material properties, process parameters, and thickness of the workpiece. The present investigation also explains in details the effect of process parameters on the responses through metallurgical analysis. A quality lobe of welding like “under weld”, “good weld” and “over weld” is proposed after observing the fractured samples in optical microscopy and scanning electron microscopy
(SEM). Meantime, energy dispersive spectroscopy (EDS) and X- ray diffraction (XRD) analyses are also used to reveal the thickness of interatomic diffusion and IMCs along the weld interface.

Item Type:Thesis (PhD)
Uncontrolled Keywords:USMW; Tensile shear failure load; T-peel failure load; ANN; ANFIS; Finite element method; Microscopy
Subjects:Engineering and Technology > Mechanical Engineering > Production Engineering
Engineering and Technology > Mechanical Engineering > Finite Element Analysis
Engineering and Technology > Mechanical Engineering > Machine Design
Divisions: Engineering and Technology > Department of Mechanical Engineering
ID Code:8622
Deposited By:Mr. Sanat Kumar Behera
Deposited On:08 Jun 2017 19:06
Last Modified:08 Jun 2017 19:06
Supervisor(s):Sahoo, Susanta Kumar and Datta , Saurav

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