Cross-Section Bead Image Prediction in Laser Keyhole Welding of AISI 1020 Steel Using Deep Learning Architectures

A deep learning model was applied for predicting a cross-sectional bead image from laser welding process parameters. The proposed model consists of two successive generators. The first generator produces a weld bead segmentation map from laser intensity and interaction time, which is subsequently translated into an optical microscopic (OM) image by the second generator. Both generators exhibit an encoder & x2013;decoder structure based on a convolutional neural network (CNN). In the second generator, a conditional generative adversarial network (cGAN) was additionally employed with multiscale discriminators and residual blocks, considering the size of the OM image. For a training dataset, laser welding experiments with AISI 1020 steel were conducted on a large process window using a 2 KW fiber laser, and a total of 39 process conditions were used for the training. High-resolution OM images were successfully generated, and the predicted bead shapes were reasonably accurate (R-Squared: 89.0 & x0025; for penetration depth, 93.6 & x0025; for weld bead area)

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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

Deep-Learning Approach to the Self-Piercing Riveting of Various Combinations of Steel and Aluminum Sheets

Deep-learning architectures were employed to simulate the self-piercing riveting process of steel and aluminum sheets and predict the cross-sectional joint shape with a zero head height. Four steels (SPRC440, SPFC590DP, GI780DP, SGAFC980Y) and three aluminum alloys (Al5052, Al5754, Al5083) were considered as the materials for the top and bottom sheets, respectively. The key objective was to consider the material properties of these metal sheets (Young's modulus, Poisson's ratio, and ultimate tensile strength) in a deep-learning framework. Two deep-learning models were considered: In the first model, the properties of the top and bottom sheets were adopted as the scalar inputs, and in the second model, the three properties were graphically assigned to the three channels of the input image. Both the models generated a segmentation image of the cross-section. To assess the accuracy of the predictions, the generated images were compared with ground truth images, and three key geometrical factors (interlock, bottom thickness, and effective length) were measured. The first and second models achieved prediction accuracies of 91.95% and 92.22%, respectively

Prediction of hardness and deformation using a 3-D thermal analysis in laser hardening of AISI H13 tool steel

In this study, a 3-D thermal analysis assisted by a systematic experimental study using a 2 kW multi-mode fiber laser was employed to develop predictive models for hardness and thermal deformation in laser transformation hardening of AISI H13 tool steel. Using the thermal model, temperature histories were obtained, which then were processed to compute the effective carbon diffusion time (ECDT) and the effective cooling time (ECT), the two concepts recently proposed by Ki and So [1]. Carefully observing the extensive hardness and deformation measurement data, it was revealed that ECDT is a parameter that correlates well with hardness, and ECT, when extended to the plastic deformation region, shows a strong correlation with deflection angle for both plastic deformation and solid-state phase transformation regions. By finding the minimum specimen thickness that preserves the hardening characteristics of thick steel plates, we were able to increase the deflection angle to ??? 0.8??, and, after laser hardening, hardness values of up to ??? 800 Hv were obtained. In this study, ECDT and ECT maps for AISI H13 tool steel were computed along with the hardening process window (or heat treatable region), and using the thermal model and experimental data, the minimum specimen thickness that preserves the hardening characteristics of thick plates was determined. This study shows that hardness distributions and thermal deformation behavior can be effectively predicted from a 3-D thermal analysis.clos

Investigation of Keyhole Behavior in Fiber Laser Welding of Zinc-Coated Steel Sheets

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Effect of keyhole geometry and dynamics in zero-gap laser welding of zinc-coated steel sheets

Both zinc-coated and uncoated DP 590 steel sheets were used for laser welding experiment with a 2 kW multi-mode fiber laser under the same experimental conditions. Using systematically obtained keyhole data, how the keyhole dynamically responds to an incident laser beam was studied by defining several key factors, such as keyhole expansion factor, keyhole motion range factor, average aperture diameters, mass loss fraction and melt pool volume size. The relative configuration of the keyhole and the laser beam was found to be the most influential factor for obtaining good welds, and when the beam was located away from the front keyhole wall a good weld was obtained. When the beam irradiates on the front keyhole wall, the zinc evaporation region between the two sheets can be directly heated by the beam, and the zinc vapor pressure can become extremely large, and therefore the melt pool is severely disturbed. The keyhole bottom aperture diameter and the keyhole expansion factor for the bottom aperture were both largely proportional to the mass loss for both zinc-coated and uncoated steels. Zero-gap laser welding of zinc-coated steels can be indeed successful and a theoretical basis is presented.close

A study of keyhole geometry in laser welding of zinc-coated and uncoated steels using a coaxial observation method

Experiments were conducted over a large process parameter space using a 2 kW multi-mode fiber laser, and the effect of zinc-coating on the keyhole geometry was investigated using a high-speed coaxial video camera. From the top and bottom coaxial surface images of erratic keyhole motions that were obtained from separately conducted experiments, time-averaged keyhole structures were calculated using a physics-based semi-statistical frame analysis. For uncoated steel, the keyhole bottom aperture is located mostly within the laser beam area. In this case, because there is no zinc evaporation, the bottom aperture tends to be closed to absorb enough laser energy for sustaining a keyhole. For zinc-coated steel, the keyhole is mostly open at the bottom, and the front keyhole wall is tilted so that the laser beam is located on the front keyhole wall. The keyhole tilting angle is more important for zinc-coated steel and the beam interaction area is more relevant to uncoated steel.close0