269 research outputs found
Effect of creep ageing on the corrosion behaviour of an Al-Cu-Li alloy
No full textCreep ageing of Al-Cu-Li alloys induces precipitation of T1(Al2CuLi) and a high fraction of low-angle grain boundaries (LAGBs) and dislocations in grain interior, factors potentially contributing to corrosion. Qualitative/quantitative analysis of precipitates and quasi-in-situ EBSD observations of stress free and stress added alloy reveal that fine dense precipitation of T-1 in grain interior and suppressed precipitation along grain/subgrain-boundary induced by stress adding make the corrosion mode evolve from intergranular to intragranular, and grain orientation displays as the most relevant metallurgical parameter for the localised corrosion although the fraction of LAGBs and dislocations have been greatly improved by stress loading
Effect of non-isothermal creep aging on the microstructure, mechanical properties and stress corrosion cracking resistance of 7075 alloy
No full textThe effect of non-isothermal creep aging (NICA) on the microstructure, mechanical properties, and stress corrosion cracking (SCC) resistance of 7075 alloy was investigated. The results showed that the tensile strength of the alloy increased to 565 MPa when the alloy was heated to 210 °C (CH210) and reached 580 MPa when it was subsequently cooled to 120 °C (CC120). Simultaneously, the SCC susceptibility of rtf increased from 50.8 % to 98.4 %. As compared with traditional creep aging process [1], a large strength increment with excellent stress corrosion resistance have been obtained by NICA. The microstructure revealed that a lot of dislocations have been introduced by creep during the heating stage which could improve the precipitates volume fraction and accelerate the diffusion of solutes; while during the cooling stage, η′ was greatly refined, and GPI and GPⅡ were re-precipitated from the matrix due to the decreased solid solubility and increased critical radius R*; both of them are responsible for the continuous strength increase during NICA. Moreover, the width of the precipitate free zone (PFZ) was narrowed from 46.1 nm (CH210) to 28.6 nm (CC120). The microchemical analysis reveals that solutes were more homogenously distributed in grain boundary precipitates (GB-ppts), matrix precipitates, the PFZ, and the matrix with the help of creep. The narrower PFZ and homogeneous solute distribution are responsible for improving the SCC susceptibility in the CC120 alloy
Machine-learning-enabled optimization and online monitoring for efficient and high-quality smart drying
No full textDrying is an important process in the food industry that plays a critical role in both food production and preservation. Industrial scale drying processes and systems involve multiple interacting process parameters, conflicting production objectives, and highly uncertain sample characteristics, which make process control extremely challenging. Current industrial practice lacks the necessary decision-making tools to simultaneously achieve high energy efficiency and food quality. To address these challenges, this dissertation develops a suite of machine-learning-based process control tools to enable smart drying with improved process efficiency and product quality. The contributions of this dissertation are summarized as follows.
It is important to devise a drying strategy to optimize drying efficiency, energy consumption, and product quality, especially under intricate input-output relationships with process uncertainties. Chapter 2 develops an uncertainty-aware, machine-learning-based response surface methodology for apple drying. New drying experiments are designed to resemble industrial practice with variable slice thickness. Variable-response relationships are modeled using machine learning models; Monte Carlo simulations are applied to quantify process uncertainties; and a constrained optimization approach identifies feasible design spaces and optimal parameter combinations. The proposed method achieves a 17.9% energy savings and a 19.0% reduction in drying time.
Physical phenomena in drying can be measured by heterogeneous data modalities, with each carrying unique and complementary information. Effectively leveraging multi-modal data is essential for improving the performance of predictive modeling but remains challenging. Chapter 3 develops a multi-modal data fusion framework for accurately predicting final moisture content in apple drying. Tabular data and high-dimensional images are integrated through an encoder-decoder network to capture both process conditions and sample variability. Experimental results demonstrate predictive accuracy improvements of 19.3%, 24.2%, and 15.2% compared to tabular-only, image-only, and standard data fusion models, respectively. It is also shown that the proposed method is robust to varying modality ratios and can effectively capture process variabilities.
Accurate real-time forecasting of the drying readiness (the optimal drying endpoint) is crucial for minimizing energy consumption and ensuring product quality. Chapter 4 presents a multi-modal fusion framework for online cookie drying readiness prediction. The model integrates in-situ video data and tabular process parameters using modality-specific encoders and a transformer-based decoder. The proposed model achieves a 15-second average prediction error, outperforming the state-of-the-art method by 65.7%, while balancing accuracy, model size, and efficiency. The framework is extensible to various other modality fusion tasks for effective online monitoring.
Dynamic changes in food attributes during drying directly reflect product quality, and accurately predicting the trajectories of these attributes provides valuable insights into determining optimal drying conditions. Chapter 5 develops a data-driven approach for zero-shot prediction of surface color trajectories during food drying. The method learns component function parameters to represent color evolution under unseen conditions, with DCT preprocessing and enhanced by multi-modal data fusion and similarity- informed training selection. The method is validated on two case studies: cookie and apple drying, significantly outperforming baseline models by 93.2% and 87.30%, respectively.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo termsThe student, Shichen Li, accepted the attached license on 2025-08-18 at 14:01.The student, Shichen Li, submitted this Dissertation for approval on 2025-08-18 at 14:43.This Dissertation was approved for publication on 2025-08-18 at 16:11.DSpace SAF Submission Ingestion Package generated from Vireo submission #22763 on 2026-02-19 at 18:24:0
A new Al5Cu6(Li,Mg)2 cubic phase in an Al-Cu-Li-Mg-X alloy
No full textAmong cubic metals, the cubic phase has excellent heat-resistance potential owing to its cube-on-cube orientation relationship with the matrix. In this study, a new cubic phase, Al5Cu6(Li,Mg)2, was identified in an Al-CuLi-Mg-X alloy after ageing at 165 degrees C for 15 h. It has a Pm3 structure similar to that of both Al5Cu6Li2 and Al5Cu6Mg2. The density functional theory (DFT) calculations noted that the precipitation of Al5Cu6Li2 is more energetically favorable than that of Al5Cu6Mg2 in the Al-Cu-Li-Mg-X alloy. Moreover, the formation enthalpy may be further reduced when the Mg atoms replace some Li sites in Al5Cu6Li2, in favor of the formation of Al5Cu6(Li,Mg)2
Research of Jianping Li, Xia Liu, Wei Ji, Qinghua Tian, Shichen Li
No full textRaw data, statistical code, and Western Blot raw images.</p
Wireless Communication and Sensing System Design : A Learning-based Approach
No full textThesis (Ph.D.)--Michigan State University. Computer Science - Doctor of Philosophy, 2025With the rapid advancement of digital technologies, wireless communication and sensing systems have become increasingly integral to our daily lives. These systems utilize wireless signals not only as data carriers but also as a medium for radio sensing. Model-based approaches have traditionally been a popular choice for addressing existing challenges in communication and sensing. However, model-based approaches struggle to accurately characterize signal propagation, especially at higher frequencies, and optimizing them for communication is even more difficult. Moreover, extracting human motion-related information from these complex signals is often challenging with conventional methods. Recent progress in artificial intelligence (AI) has opened new avenues for addressing these challenges. This thesis explores learning-based approaches to uncover the hidden information embedded within wireless signals. By doing so, it aims to enhance the efficiency of wireless communication systems and enable fine-grained human motion sensing, thereby pushing the boundaries of wireless systems.The first part of this thesis explores the capability of various RF signals to sense different levels of human motion using learning-based approaches. We begin by proposing AuthIoT, a gesture-based wireless authentication scheme designed for IoT devices. AuthIoT leverages a convolutional neural network (CNN) to learn human gesture features from Wi-Fi channel state information (CSI) and maps them to specific letters for device authentication. To enhance robustness and enable gesture recognition across diverse environments, the system employs a feature fusion approach that integrates location-independent features, ensuring strong transferability. Next, we shift our focus to tiny motions and propose RadSee, a system capable of recognizing fine-grained handwriting. We develop a 6 GHz FMCW radar system along with a tailored deep neural network to identify handwritten letters through walls. The model combines a bidirectional long short-term memory (BiLSTM) network with an attention mechanism to leverage temporal dependencies and capture critical features\u2014such as turning points\u2014in radar phase sequences for accurate recognition. We push the limits of this system further with a novel learning framework and introduce RadEye, a system designed to recognize eye movements. Given the subtle nature of eye motion and the challenge of detecting it in RF signals, we adopt a transformer encoder as the feature extractor to more effectively exploit temporal dependencies in the phase sequences. To further enhance performance, we incorporate a state-of-the-art vision-based method to provide guidance and prior knowledge during the learning process. The second part of this thesis focuses on leveraging learning-based solutions to improve the efficiency of wireless communication systems, with particular emphasis on enhancing the throughput of mmWave communication systems. We begin by proposing an uplink multi-user MIMO (MU-MIMO) mmWave communication (UMMC) scheme for WLANs. MU-MIMO techniques are well-known for increasing network efficiency and throughput. A key innovation in this work is a learning-based Bayesian optimization (BayOpt) framework for joint beam search across multiple antennas. This approach eliminates the need for complex channel modeling and identifies optimal beamforming directions with only a few search iterations, significantly reducing beamforming overhead. We then further explore the beamforming problem in mmWave communications, shifting our focus to mobile mmWave networks. In such dynamic environments, beamforming overhead becomes more pronounced. To address this challenge, we leverage the temporal correlation of wireless channels to aid in beam selection. Specifically, we propose a Temporal Beam Prediction (TBP) scheme that enables a mobile mmWave device to predict its future beam direction based on its historical beam selection profile. At the core of this scheme is a modified LSTM architecture, complemented by an adversarial learning model to improve the robustness and generalizability of the beam steering process. This thesis presents efficient communication schemes and novel sensing applications based on learning-driven approaches, paving the way for the design of AI-enabled next-generation wireless communication and sensing systems. It provides detailed descriptions of system implementations, experimental setups, and performance evaluations of the proposed schemes in real-world environments. Furthermore, it offers an in-depth analysis of the limitations of these systems and discusses open challenges in developing future wireless communication and sensing systems using learning-based techniques.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
HIGH PERFORMANCE TRANSLUCENT SOFT PIEZOELECTRIC NANOCOMPOSITES
No full textSoft piezoelectric nanocomposites have attracted significant interest due to their flexibility and stretchability which are beneficial for a variety of applications. However, there is no soft and transparent piezoelectric material with good piezoelectric performance
The transparency of piezoelectric nanocomposite would be beneficial for the applications that need both good transmission of light and energy harvesting/sensing capability. To address this issue, a facile method to fabricate high performance soft piezoelectric nanocomposites with enhanced light transmission is developed by incorporating silver nanowires as conductive fillers.
During the fabrication process, ethanol was used to help the mixing of the composites and continuous magnetic stirring was applied during ethanol evaporation for good blending of the composite. After curing the mixture, the piezoelectric coefficient (d33) was measured to evaluate the piezoelectric performance by measuring the electric output as a function of a mechanical loading. The data analysis showed that the silver nanowire incorporated piezoelectric composite has a higher d33 coefficient (d33~100 pC/N at its peak) than that of the commercially available PVDF (d33: 30~40 pC/N).
We envision that the methods we developed and the findings from the study will contribute to applications of piezoelectric composites for a wider range of applications and provide guidelines for future research
Recommended from our members
High-Precision Robotic Assembly and Packing in the Presence of Uncertainties and Unknowns
No full textHigh-precision robotic manipulation tasks, such as robotic assembly tasks or dense packing tasks, are contact-rich and have to overcome the effects of various uncertainties stemming from inaccuracies in modeling, sensing, perception, and robotic motion control. Efficient and robust robotic assembly and dense packing of complexshaped parts are critical, open challenges in advanced manufacturing and logistics, since existing solutions are often limited to very specific objects or tasks. This dissertation presents a perception-based, online framework that enables autonomous robotic and human-robot collaborative (HRC) systems to perform tightclearance assembly under six-dimensional uncertainties. Additionally, it introduces a perception-driven, online approach for a robot manipulator to autonomously and densely pack unknown objects that arrive continually at a packing site into a container to maximize space utilization and overcome the effects of various uncertainties. The proposed methods are extensively validated through experimental evaluations on complex-shaped multi-peg-in-hole assembly tasks and dense packing problems involving unknown objects of varied shapes with tight clearances. The contributions of this research include designing, implementing, and testing the following frameworks and systems: - A robust and general autonomous robotic assembly approach for assembly involving arbitrary, complex-shaped parts, including multi-peg-in-hole tasks, under significant pose uncertainties exceeding ten times the task clearances. - A general, geometry-invariant representation of parts and novel optimization algorithm for online contact configuration estimation. - A flexible robotic assembly framework that enables arbitrary picking and insertion directions for assembly within the robot’s workspace. - A human-robot collaboration framework that combines intuitive human guidance with autonomous robotic precision for tight-clearance assembly, without requiring operator expertise in robotics. - A real-time, perception-driven framework for concurrent object detection, modeling, grasp planning, packing planning, and motion execution under uncertainty. - A novel online 4-DoF dense packing planning method that continuously optimizes object placement sequences and poses for high packing density. By addressing the challenges of uncertainty in high-precision robotic manipulation tasks of complex objects, this dissertation aims to push the envelope in robotics and automation towards broader applications of robotic systems in complex, real-world environments
China's People-to-people Diplomacy and Its Importance to China-EU Relations: A Historical Institutionalism Perspective
No full textPeople-to-people exchange has become a heated topic of the Chinese foreign policy. Since the beginning of the twenty-first century, China has established people-to-people dialogues with the United States, the European Union, the United Kingdom, France and Russia. In 2012, China and the EU established a high-level dialogue for people-to-people exchange, making people-to-people exchange the third pillar of China-EU relations. However, China is not a newcomer to people-to-people exchanges with Europe. Why does China launch the people-to-people diplomacy? Is it a plus or a must for China as well as for China-EU relations? The author reviews the history and current situation of China's people-to-people exchange and investigates China’s motivations behind the policy. Using the historical institutionalism as an approach, this paper argues that people-to-people diplomacy is a key component of the contemporary Chinese foreign policy towards Europe. China has long been an unequal counterpart to Europe since the 1840s. After the development of bilateral political and economic cooperation in the past four decades, people-to-people diplomacy is the last part that China needs to finish in order to regain equal status with Europe. In addition, it is also a step towards realising the "great rejuvenation of the Chinese nation"
- …
