Sungwon Lee

Influence of scandium and zirconium on grain stability and superplastic ductilities in ultrafine-grained Al-Mg alloys

Abstract Experiments were conducted to evaluate the grain refinement introduced by equal-channel angular pressing (ECAP) in three different Al–3% Mg alloys containing either 0.2% Sc, 0.2% Zr or a combination of 0.2% Sc and 0.12% Zr. The results show all three alloys exhibit significant grain refinement with as-pressed grain sizes in the range of ∼0.2–0.3 μm. Tensile testing after ECAP revealed superplastic ductilities in the Al–Mg–Sc–Zr and Al–Mg–Sc alloys at strain rates in the vicinity of ∼10−2 s−1 at temperatures of 573 and 673 K but superplasticity was not achieved in the Al–Mg–Zr alloy due to the onset of rapid grain growth at 573 K. At the higher temperature of 773 K, the Al–Mg–Sc–Zr alloy exhibited exceptional superplastic ductilities but superplasticity was no longer achieved in the Al–Mg–Sc alloy. This difference was due to extensive grain growth in the Al–Mg–Sc alloy at temperatures in the vicinity of 773 K whereas the ultrafine-grained microstructure in the Al–Mg–Sc–Zr alloy was stable at this high temperature due to the presence of stable Al3(ZrxSc1−x) precipitates.

Microhardness and microstructural evolution in pure nickel during high-pressure torsion

The microhardness and microstructural evolution during high pressure torsion testing in samples of pure nickel were investigated. Results were obtained on Ni samples subjected to annealing prior to high pressure torsion. A detailed TEM and X ray study were used. During torsion, the final microstructure was independent of the grain size. It was demonstrated that the processing parameters of strain and applied pressure were important for microhardness refinement and increased microhardness during high pressure torsion.

Developing superplastic properties in an aluminum alloy through severe plastic deformation

Abstract Equal-channel angular (ECA) pressing is a processing procedure which subjects a material to severe plastic deformation. Tests were conducted on a commercial cast aluminum alloy to evaluate the properties associated with samples subjected to three different ECA pressing procedures. The results show that all three procedures lead to an ultrafine microstructure and each procedure is capable of producing samples which exhibit high strain rate superplasticity. Optimum superplastic properties were achieved in samples subjected to ECA pressing to a strain of ∼12. Under these conditions, the measured elongations to failure at a temperature of 673 K were 1210 and 950% at strain rates of 10−1 and 1 s−1, respectively.

Spatially-Localized Compressed Sensing and Routing in Multi-hop Sensor Networks

We propose energy-efficient compressed sensing for wireless sensor networks using spatially-localized sparse projections. To keep the transmission cost for each measurement low, we obtain measurements from clusters of adjacent sensors. With localized projection, we show that joint reconstruction provides significantly better reconstruction than independent reconstruction. We also propose a metric of energy overlap between clusters and basis functions that allows us to characterize the gains of joint reconstruction for different basis functions. Compared with state of the art compressed sensing techniques for sensor network, our simulation results demonstrate significant gains in reconstruction accuracy and transmission cost.

Developing a superplastic forming capability in a commercial aluminum alloy without scandium or zirconium additions

Abstract Tests were undertaken to determine the feasibility of producing a superplastic forming capability in a commercial Al-2024 alloy through processing by equal-channel angular pressing (ECAP), where this alloy was selected because it contains no scandium or zirconium additions that are generally beneficial in retaining an array of small grains. Processing by ECAP produced grain sizes in the range from ∼0.3 to ∼0.5 μm and static annealing showed these ultrafine grains were reasonably stable at temperatures up to ∼700 K. Superplastic elongations were achieved after ECAP with a maximum elongation of ∼500% at 673 K when using a strain rate of 1.0×10−2 s−1. The strain rate sensitivity was measured as ∼0.3 suggesting that dislocation glide is the rate-controlling mechanism. These results demonstrate the potential for achieving high tensile ductilities in conventional commercial aluminum alloys through processing by ECAP.

Influence of equal-channel angular pressing on the superplastic properties of commercial aluminum alloys

Equal-channel angular (ECA) pressing was used to refine the microstructure in two commercial aluminum alloys, Al-2024 and the Supral-100 Al-2004 alloy. The ECA pressing was conducted at room temperature and at elevated temperatures for both alloys using several different processing routes. Tensile testing was carried out at elevated temperatures on both pressed and unpressed samples of each alloy in order to evaluate the effect of the pressing. This paper describes the influence of the ECA pressing on the subsequent mechanical properties of these two alloys. For both alloys, it is shown that the optimum superplastic conditions are influenced by the ECA pressing, and in practice there tends to be a decrease in the optimum temperature for superplasticity and a corresponding increase in the optimum strain rate. In addition, there was evidence for high strain rate superplasticity (HSR SP) in both alloys after the ECA pressing procedure.

Adaptive compressed sensing for depthmap compression using graph-based transform

In this paper we present an adaptive compressed sensing (CS) framework for depth map compression using a family of graph-based transforms (GBT). To improve overall performance, we propose a greedy algorithm that selects for each block a GBT minimizing a metric, based on average mutual coherence, that takes into consideration both the edge structure of the block and the characteristics of the CS measurement matrix. This algorithm uses a low-complexity estimate of the mutual coherence, so that explicit construction of the GBT at the encoder is not required in the iterative process. As compared to coding using H.264/AVC, the proposed approach applied to intra-frames shows an average of 39 % bitrate savings or 3.8 dB PSNR gain for views rendered using a depth image based rendering (DIBR) technique.

Efficient data-gathering using graph-based transform and compressed sensing for irregularly positioned sensors

In this work, we propose a decentralized approach for energy efficient data-gathering in a realistic scenario. We address a major limitation of compressed sensing (CS) approaches proposed to data for wireless sensor network (WSN), namely, that they work only on a regular grid tightly coupled to the sparsity basis. Instead, we assume that sensors are irregularly positioned in the field and do not assume that sparsifying basis is known a priori. Under the assumption that the sensor data is smooth in space, we propose to use a graph-based transform (GBT) to sparsify the sensor data measured at randomly positioned sensors. We first represent the random topology as a graph then construct the GBT as a sparsifying basis. With the GBT, we propose a heuristic design of the data-gathering where aggregations happen at the sensors with fewer neighbors in the graph. In our simulations, our proposed approach shows better performance in terms of total power consumption for a given reconstruction MSE, as compared to other CS approaches proposed for WSN.

Hardware-driven compressive sampling for fast target localization using single-chip UWB radar sensor

To design an energy-efficient UWB ranging system, we propose a compressive sampling (CS) technique tightly coupled to a recently proposed hardware. Our goal is to design a system that is robust to high noise and consumes less energy while providing reliable localization. In this work, we first introduce a representation of UWB signals as group sparse signals with the number of groups corresponding to the number of objects in the environment. Also, we design an efficient measurement system that is constructed using low-density parity-check (LDPC) matrix, in order to satisfy several constraints imposed by the hardware: non-negative integer entries in measurement (sensing) matrix, constant row-wise sum of non-zero entries in the matrix, and a unique structure characterized by Kronecker product. To enhance performance, we propose a window-based reweighted L1 minimization that outperforms other existing algorithms in our simulation. The result shows that our proposed method can achieve reliable target-localization, while using only 40% of the scanning (sampling) time required by the sequential scanning scheme, even in highly-noisy environments.

Node clustering for data collection in wireless sensor networks using graph-transform and compressive sampling

In this paper, we address the problem of node clustering for compressed sensing (CS) based data collection in wireless sensor networks (WSNs). With consideration of recovery accuracy, communication cost and residual energy, two clustering strategies are proposed. Both strategies utilize Lapacian eigenvectors corresponding to the topology graph as a sparsifying basis, termed eigenbasis. The first clustering strategy is a centralized one, for which we treat the energy concentration of eigenbasis as sparsity feature vector and use traditional pattern clustering method to divide the nodes into clusters. The second one is a distributed heuristic strategy simultaneously considering residual power, communication cost, and basis energy distribution over clusters. By utilizing eigenbasis, both strategies are independent of the data to be collected and applicable in irregularly placed WSNs. Simulation results from both synthetic and real data are included to demonstrate the proposed strategies.