Modelling and characterisation of a servo self-piercing riveting (SPR) system

SPR is a cold mechanical joining process in which multiple sheets of material are riveted together without the need for a predrilled hole. It works by pushing a typically semi-tubular rivet into a target stack of material, during which the plastic deformation of the material and rivet are such that a mechanical lock is formed within the material stack. The process is used extensively in the automotive industry in car body construction, and is a competing technology to more established joining techniques such as resistance spot welding. As part of the ongoing development of the technique, there is a strong need to understand and simulate the dynamics of the process. In this work, a lumped parameter model of the SPR system with a non-parametric model of the joint is presented. Simulated results are compared with experimental data for a given joint configuration. Furthermore, the model is used to highlight the significance of the compliances within the system. It is shown that during rivet insertion, the stiffness of the C-frame structure is an influential factor in determining the dynamic response of the system. The results provide the basis for a more comprehensive sensitivity analysis into the factors which affect the quality of the resulting joint

Thermal Mechanical Numerical Modeling of Friction Element Welding

With the objective of minimizing carbon footprint of vehicles, different organizations across the world are increasingly enforcing higher fuel efficiency targets for the automobile manufacturers. To improve the fuel economy while retaining or further improving the structural integrity, the automobile industry is vigorously shifting towards substituting conventional heavy materials like cast iron with new age materials such as aluminum alloys, steel alloys, etc. which are not only much lighter but also offer superior strength-to-weight ratio. Engineers use a mix of these new age materials with the aim of maximizing the benefits from each material. However, the utilization of such materials is currently limited in the industry as welding them using conventional methods such as resistance spot welding or fusion welding process, is plagued with inherent difficulties such as formation of brittle inter-metallic compounds, irreversible and adverse changes in the thermal and mechanical properties of the materials. Dissimilar material joining is of critical importance in aiding the manufacturers realize the crucial objective of a safer and more fuel efficient vehicle. Friction element welding (FEW), a friction based joining process, has been proposed for joining highly dissimilar materials in minimal time and with low input energy. FEW process can join a variety of materials which differ significantly in their mechanical, thermal, and metallurgical properties without inducing any of the defects associated with conventional welding methods. The fundamental governing mechanisms that characterize the FEW process needs to be investigated to help optimize the process for specific applications. Conducting experimental investigation is undesirable and infeasible due to the highly complex thermal-mechanical procedures occurring simultaneously in a very short period of time of about one second. As such, the utilization of a finite element model to simulate and analyze the FEW process is warranted which would help understand the underlying mechanisms of the process in detail and provide an efficient yet effective tool to observe the effect of different process parameters on the weld quality. A coupled thermal-mechanical finite element model (FEM) is developed in this work to simulate the FEW process and gain an understanding of the physical mechanisms involved in the process and help predict the influence of variation of process parameters on the evolution of temperature, material flow, and their effect on weld quality. The primary difficulty in simulating a highly transient process like FEW, wherein not only the workpiece is subjected to deformation but also the auxiliary joining element i.e. friction element undergoes extensive deformation, is that the mesh elements are prone to distortion failure while trying to capture such high amount of deformation. The presence and importance of temperature effect on material properties further complicate the FEM. To help eliminate the distortion issue while simultaneously achieving an accurate simulation of the FEW process, the coupled Eulerian-Lagrangian (CEL) approach is adopted. The novelty of the current approach employed lies in using a Eulerian definition for the tool as against the more traditional convention of adopting a purely Lagrangian definition. The Eulerian definition enables to simulate the extreme deformation of friction element and capture the material flow without any computational issues. To inspect for the accuracy of the FEM results, mechanical deformation for different parts observed in the FEM is compared against the experimental results. To further validate the FEM, experimental measurements of temperature at different locations at the interface of two layers of workpiece are compared against the FEM results at same locations in the model. With respect to, both, thermal and mechanical measurements comparisons good agreement is shown between the simulation results and the experimental data. The simulation results for sets with varying process parameters show that the rotational speed of the friction element has the highest influence on the amount of frictional heat generated followed by the time period for different steps. Higher amount of heat is generated and conducted into the top aluminum layer for longer Penetration time, whereas for more heat concentration into the friction element to achieve the required deformation, longer Welding step with higher rotational speed is desired

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Department of Mechanical EngineeringAlong with a worldwide increasing popularity of deep learning in computer science that began in the 2010s, it has been actively applied in the field of mechanical engineering as well, such as in computational fluid dynamics (CFD) simulation, topological design, and materials processing. Unlike the conventional numerical method of solving governing differential equations (physics-based), deep learning has presented a completely new perspective of analyzing modern technologies. Trained from a given dataset (experimental or simulation), the deep learning model can predict the future with very good accuracy, by spontaneously discovering intrinsic patterns contained in the data (thus, data-driven). In this dissertation, we present novel frameworks to make accurate predictions in three modern materials processing technologies (i.e., laser heat treatment, laser keyhole welding, and self-piercing riveting), by applying a state-of-the-art deep learning architecture. We anticipate that the proposed deep learning frameworks will be an important milestone for the future advanced manufacturing applications using an artificial intelligence (AI). In chapter 1, we introduced backgrounds of the three aforesaid materials processing technologies and deep learning, respectively. In the deep learning section, the focus was primarily placed on the algorithms of actively employed in computer vision, that is a convolutional neural network (CNN) for image recognition and a generative adversarial network (GAN) for image generation, which were the two main source frameworks adopted in this study. In chapter 2, we proposed a deep learning-based hardness predictive model in laser surface hardening (heat treatment) of AISI H13 tool steel, from an input of cross-sectional temperature distribution (the first deep learning model in laser hardening). The objective of laser hardening is to improve the metal surface by locally enhancing the surface hardness, and the employed deep learning model succeeded in accurately predicting the amount of hardening on entire cross-section. For the model input, finite element method (FEM)-simulated cross-sectional temperature profile was used when the surface temperature reaches the maximum, and the model was based on a conditional generative adversarial network (cGAN) with the CNN encoder-decoder, which is a specialized structure in image-to-image translation (temperature-to-hardness translation in our model). The presented deep learning architecture is expected to be useful in a development of highly accurate process predicting systems in laser heat treatment. In chapter 3, we studied a cross-section weld bead image prediction in laser keyhole welding of AISI 1020 steel, using state-of-the-art deep learning algorithms (the first deep learning model in laser weld bead image prediction). Predicting the bead shape has always been a challenging issue in laser keyhole welding, as the complex multi-physics phenomena come into play with high interfacial forces such as capillary and thermocapillary forces and recoil pressure. With our deep learning model, not only the geometrical bead shape, but also a high-resolution optical microscopic (OM) weld bead image can be produced including keyhole, heat affected zone, substrate, porosity, and microstructures, from the two input parameters of laser intensity and beam scanning speed. The proposed deep learning model consisted of two successive generators which both exhibit an encoder-decoder structure based on the CNN. Additionally, in the second generator, multi-scale cGAN architecture was employed with deep residual connections, considering size of the OM image (high-resolution). We expect the presented deep learning framework to play a leading role in the future advanced modeling of laser keyhole welding. In chapter 4, we presented a deep learning framework for predicting cross-sectional shape in self-piercing riveting (SPR) joining process (the first deep learning model in SPR). SPR process is getting popular in the automotive industry, as it can easily combine two or more sheets in a single step regardless of the material types (even can be applied to the dissimilar sheets such as steel???nonferrous metal and composite???metal). The quality of the SPR joint is determined by the cross-sectional shape, so its prediction is essential, which was conventionally carried out by the FEM simulation. Using our predictive model, without any concerns about the mesh and time step, highly accurate cross-sectional shape can be generated from a scalar input of punch force, within a few seconds. The proposed predictive model was a novel CNN-based deep residual generator in the cGAN architecture. The model presented in this dissertation opens up the possibilities of deep learning applications to the SPR process for the first time, and we anticipate that our model will play a central role in a development of future sophisticated AI models.clos