Showing papers by "Yung C. Shin published in 2020"

Mechanical breathing in organic electrochromics

Abstract: The repetitive size change of the electrode over cycles, termed as mechanical breathing, is a crucial issue limiting the quality and lifetime of organic electrochromic devices. The mechanical deformation originates from the electron transport and ion intercalation in the redox active material. The dynamics of the state of charge induces drastic changes of the microstructure and properties of the host, and ultimately leads to structural disintegration at the interfaces. We quantify the breathing strain and the evolution of the mechanical properties of poly(3,4-propylenedioxythiophene) thin films in-situ using customized environmental nanoindentation. Upon oxidation, the film expands nearly 30% in volume, and the elastic modulus and hardness decrease by a factor of two. We perform theoretical modeling to understand thin film delamination from an indium tin oxide (ITO) current collector under cyclic load. We show that toughening the interface with roughened or silica-nanoparticle coated ITO surface significantly improves the cyclic performance. Though organic electrochromic devices are promising for various applications, their performance is limited by a cyclic change in volume of the material over time. Here, the authors study mechanical breathing in a model polymer and provide rules for the design of new mechanically-reliable materials.

Deep-learning-based porosity monitoring of laser welding process

Abstract: A porosity monitoring scheme for laser welding process was developed based on a deep learning approach. The in-process weld-pool data were sensed with a coaxial high-speed camera and labelled with the porosity attributes measured from welded specimens. A convolutional neural network (CNN) model with compact architecture was designed to learn weld-pool patterns to predict porosity. In laser welding experiments of 6061 Aluminum alloy, the CNN-based monitoring model achieved a classification accuracy of 96.1% for porosity occurrence detection, though the prediction of micro (less than 100 µm) and deep subsurface pores still remains challenging.

Overview of Laser Applications in Manufacturing and Materials Processing in Recent Years

Abstract: This article is to capture some of the important developments in the rapidly growing areas of laser-based manufacturing and materials processing and also to describe important technological issues pertaining to various laser-based manufacturing processes. The topics to be covered in this paper include more popularly used processes in industry such as laser additive manufacturing, laser-assisted machining, laser micromachining, laser forming, laser surface texturing, laser welding, and laser shock peening, although there are several additional areas of laser applications. In each section, a brief overview of the process is provided, followed by critical issues in implementing the process, such as properties, predictive modeling, and process monitoring, and finally some remarks on future issues that can guide researchers and practitioners.

Analysis of defect formation mechanisms and their effects on weld strength during friction stir welding of Al 6061-T6 via experiments and finite element modeling

Abstract: The formation of tunnel defects and cavities during friction stir welding of Al 6061 T6 alloy is symbiotically studied experimentally and numerically. A coupled Eulerian-Lagrangian finite element model is established to analyze the process, where the workpiece is modeled as an Eulerian body, and the tool as Lagrangian. The model was first validated by conducting experiments and correlating the force measured by a three-axis dynamometer and the temperature measured by a pyrometer with those predicted by the simulation model. The experimentally validated simulation model was used to find an optimum parameter set for the sound weld case. The material flow and peak temperatures reached are shown to be different in the formation of various subsurface defects. It is also shown that the maximum temperature reached during the formation of tunnel defects is below that of the solidus melting point and is higher in the case of cavity defect formation. The pin drives the material to the advancing side of the weld during cavity defect formation while it fails to do so during the formation of tunnel defects. At lower rotational speeds, tunnel defects are observed and at higher welding speeds, cavity defects are observed. A featured tooltip is used in the simulation, which facilitates the stirring action and leads to the formation of a sound weld. The model is validated by conducting experiments at the same welding parameters.

Prediction of 3D microstructure and phase distributions of Ti6Al4V built by the directed energy deposition process via combined multi-physics models

Abstract: In this paper, a multiphysics and multiscale integrated simulation framework is established to link the thermal history with the microstructural evolution and resulting properties of Ti6Al4V in additive manufacturing processes by combining: (1) a three-dimensional (3D) multiphysics modeling of quasi-steady-state deposition geometry and thermal history in the directed energy deposition (DED) process, (2) a 3D cellular automata modeling of the solidification grain structure, and (3) a diffusion/diffusionless kinetic modeling of solid-state phase transformation and microhardness prediction based on the simulated phase volume fractions. By applying to Ti6Al4V, this integrated simulation framework demonstrates its feasibility in modeling complex microstructural evolution and phase transformation during the multi-track DED process. The simulated track geometry and thermal history agree well with experimental results. Coupled with the extracted temperature profiles and heating/cooling rates, the competitive growth of β grains upon solidification of the molten pool is successfully predicted. The solid-state β→α/α´ transformation in the fusion zone and heat-affected zone is then captured by the kinetic solid-state phase prediction model. With the predicted volume fractions of α and α´ in the final microstructure, the microhardness is assessed, matching the experimental measurements.

Robust Wheel Wear Monitoring System for Cylindrical Traverse Grinding

Abstract: Although many advanced signal processing techniques and novel machine learning algorithms have been applied to the monitoring of grinding processes in the literature, most of these techniques and algorithms are only effective under specific conditions and are unusable under other grinding conditions, such as varying wheel types or workpiece materials. This article proposes a robust grinding wheel wear monitoring system to eliminate these restrictions. Physical information generated during the grinding process is collected by a power sensor, accelerometers, and acoustic emission sensors. After the signals are preprocessed, features are extracted via different signal processing techniques, and a novel normalization scheme is applied to make these features independent of the wheel type, workpiece material, and grinding parameters. The features that are most related to wheel wear are selected according to the statistical criterion. An interval type-2 fuzzy basis function network is adopted to develop a wheel wear monitoring model, which is capable of predicting wheel wear under various grinding conditions and generating upper and lower prediction bounds according to the fluctuation of features. Based on the wheel wear model, a robust monitoring scheme to schedule timely wheel dressing and ensure workpiece surface finish could be established.

Optimization of Chessboard Scanning Strategy Using Genetic Algorithm in Multi-Laser Additive Manufacturing Process

Abstract: Residual stress and manufacturing time are two serious challenges that hinder the widespread industry adoption and implementation of the powder-bed fusion (PBF) process. Commercial Multi-Laser PBF (ML-PBF) systems have been developed by several vendors in recent years, which dramatically increase the production rate by employing more heat sources (up to 4 laser beams). Although numerous research works conducted toward mitigation of the effects of residual stress on printed parts in the Single Laser PBF (SL-PBF) process, no research work on this topic has been reported for the ML-PBF process to date. One of the most efficient real-time approaches to mitigate the influence of residual stress and as such the process lead time effectively is to improve the scanning strategy. This approach can be also implemented effectively in the ML-PBF process. In this work, we extend the previously developed GAMP (Genetic Algorithm Maximum Path) strategy for optimizing the scanning path in ML-PBF. The E-GAMP (the Extended GAMP) strategy manipulates the printing topology of the islands and generates more thermally efficient scanning patterns for the chessboard scanning strategy in ML-PBF. This strategy extends the single thermal heat source to multiple ones (2 as well as 3 lasers). To validate the effectiveness of the proposed strategy, we simulate the thermal distribution throughout a simple rectangular layer by ABAQUS for both the traditional successive scanning strategy and the E-GAMP strategy. The results demonstrate that the E-GAMP strategy considerably decreases the manufacturing time while it reduces the maximum temperature gradient, or in other words, generates a more uniform temperature distribution throughout the exposure layer.

A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems

Abstract: A novel approach to build a Takagi-Sugeno (T-S) fuzzy model of an unknown nonlinear system from experimental data is presented in the paper. The neuro-fuzzy models or, more specifically, fuzzy basis function networks (FBFNs) are trained from input–output data to approximate the nonlinear systems for which analytical mathematical models are not available. Then, the T-S fuzzy models are derived from the direct linearization of the neuro-fuzzy models. The operating points for linearization are chosen using the evolutionary strategy to minimize the global approximation error so that the T-S fuzzy models can closely approximate the original unknown nonlinear system with a reduced number of linearizations. Based on T-S fuzzy models, optimal controllers are designed and implemented for a nonlinear two-link flexible joint robot, which demonstrates the possibility of implementing the well-established model-based optimal control method onto unknown nonlinear dynamic systems.