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Wear behaviour of deep cryogenically treated coated tool during end milling of XH67MBTHO

Proceedings of the Institution of Mechanical Engineers, Part J: Journal of engineering Tribology 2025年

作者： Sasi, Jayaram C Mathew, Jose George, Allan Kuriachen, Basil Dhanish, P.B. Oommen, George Anoop, C.R. Advanced Manufacturing Centre Department of Mechanical Engineering National Institute of Technology Calicut Calicut India Materials and Manufacturing Entity LPSC Indian Space Research Organisation Trivandrum India Control System and Components Entity LPSC Indian Space Research Organisation Trivandrum India

XH67MBTHO is a Ni-based superalloy which finds usage in high temperature applications of aero engines. However, attributed to some of their inherent properties like strain hardening and poor thermal conductivity, tool life characteristics are adversely affected during the machining operations. The use of conventional TiAlN coated tools can address the tool wear issues faced during machining of similar Ni-based alloys. However, severe coating delamination, and significant adhesion wear mechanisms could negatively influence the effectiveness of the tools at dry lubricating conditions. The current study investigates the influence of a sustainable manufacturing technique through DCT (Deep Cryogenic Treatment) and its impact on coated tool inserts to address the tool wear issues under dry machining conditions. The DCT aids in the formation of η (eta) phase carbides, β-phase densification etc. Hence, the tool substrate develops significant wear resistance and fracture toughness in withstanding the contact stress at the machining interface. Notch wear, micro-crack formations, and built-up layer generations were observed to be major wear mechanism, and for treated tool (TT) it was less severe. Furthermore, the maximum reduction in average flank wear widths for treated tool was observed to be 26%, while the cutting force and surface roughness values were found to be reduced by 27%, and 24% respectively compared to untreated tool (UTT). © IMechE 2025.

关键词： Milling (machining)

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Point-Plane Better than Point-Point – for Embedding-based USAD 8

Point-Plane Better than Point-Point – for Embedding-based U...

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8th International Conference on Video and Image Processing, ICVIP 2024

作者： Zhao, Zhirong Zhang, Yuanhao Yu, Zujun Zhu, Liqiang Science and-Technology Department Guoneng Shuohuang Railway Development Co Ltd. Cangzhou Cangzhou062350 China Frontiers Science Center for Smart High-Speed Rail System Beijing Jiaotong University Beijing100044 China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China

ISBN: (数字)9781510689244

ISBN: (纸本)9781510689237

As deep neural networks (DNNs) have advanced, high-quality pre-trained models have provided excellent feature extraction methods for embedding-based unsupervised anomaly detection (EUSAD). However, when calculating anomaly scores, common EUSAD models often rely on point-to-point distance computations between test features and reference features. This approach not only fails to fully utilize the carefully integrated reference feature library but may also increase pixel-level false positives due to the incorrect selection of reference points. To address this issue, this paper adopts the least squares method, evaluating anomalies by calculating the distance between test feature points and the reference feature plane. Experimental results demonstrate that this method not only performs well in global image detection but also significantly reduces pixel-level false positives, showing strong performance across many anomaly detection datasets. © 2025 SPIE.

关键词： Pixels

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Enhancing Resource Allocation and Performance in Multi-Layer Industrial IoT Through Adjustable Strategy Integrating Cooperative Communication and Edge Computing

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IEEE Internet of Things Journal 2025年

作者： Sun, Mingyue Yuan, Yazhou Ma, Kai Chen, Cailian Zhang, Jianmin Luo, Xiaoyuan The Institute of electrical Engineering Yanshan University Qinhuangdao066004 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The Automation Department of the Baosteel Research Institute Shanghai200240 China

Reliable bidirectional communication between the control center (CC) and manufacturing devices (MDs) along with efficient resource allocation are critical for the Industrial Internet of Things (IIoT). However, due to the limited network resources of IIoT devices and the intertwined nature of communication and computation resources, achieving efficient optimization of these resources presents significant challenges. In this paper, we propose a multi-layer communication architecture for the IIoT based on edge computing and cooperative communication technologies, where resource-constrained MDs upload data to edge servers (ESs) for processing. To address the issues of resource scarcity and coupling, we establish a bandwidth release model (BRM) to analyze and quantify the relationship between computation and communication resources. This elucidates the interaction mechanisms between ‘transmission-computation’ performance. Furthermore, to prevent excessive data upload to ESs, which could lead to node overload and network congestion, we employ game theory to develop a pricing mechanism for resource allocation, where ESs charge for computation services. Specifically, we construct a Lagrangian framework to determine a resource allocation scheme, aiming to maximize the utility of the factory. Simulation results indicate that compared to common methods employed in existing works, our strategy enhances factory utility by 29.79%. © 2014 IEEE.

关键词： Edge computing

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Synthesis of Dynamic Virtual-Inertia for Microgrid system Applications

Synthesis of Dynamic Virtual-Inertia for Microgrid System Ap...

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Intelligent control, Computing and Communications (IC3), International Conference on

作者： Jackson J. Justo Galina Demidova Dmitry Lukichev Nikolai Poliakov Peter Makolo Ramesh C. Bansal Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia Department of Electrical Engineering University of Dar es Salaam Dar es Salaam Tanzania Depatment of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electric Electronic and Computer Engineering University of Pretoria Pretoria South Africa

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

During the last two decades, interest in grid integrated renewable energy sources (RERs) into the electrical power grids has significantly grown. The motive being a promising strategy to reduce greenhouse gas emissions generated by conventional power plants (CPPs) and hence environmental consequences, decrease losses in distribution lines to improve power system supply efficiency and reliability to consumers. As a result, several CPPs are being replaced by RERs such as solar PV system, wind energy, micro hydro-turbine, fuel cell, and small gas power plants. These are installed close to customers on low-voltage and medium-voltage sites to reduce losses in the network and improve power quality, overall efficiency and reliability. However, grid-connected RERs via inverter decreases system inertia due to decrease in CPPs with rotating mass serving as an immediate natural response to frequency deviations following network disturbances. Therefore, this paper presents the design of dynamic virtual-inertia control (DVIC) scheme. The developed DVIC incorporates the mechanism to adjust active and reactive demand in response to any frequency and voltage variations to ensure system stability. To validate the suitability of the presented technique, MATLAB/Simulink model is developed and simulated under various operating scenarios. Using the results obtained simulation study, it is evidently the proposed DVIC shows improved performance is achieved than when no inertia control system applied.

关键词： Time-frequency analysis Renewable energy sources Power system dynamics Microgrids Power system stability Inverters Power system reliability Reliability Frequency control Power generation

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Learning-based MPC of sampled-data systems with partially unknown dynamics

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ISA Transactions 2025年

作者： Han, Seungyong Guo, Xuyang Kommuri, Suneel Kumar Department of Mechanical System Engineering Jeonbuk National University 567 Baekje-daero Deokjin-gu Jeonju54896 Korea Republic of Department of Control Science and Engineering Tongji University Shanghai China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou215123 China

In this paper, a novel learning-based model predictive control (LMPC) method is proposed for sampled-data control systems with partially unknown dynamics. Many real-world processes are subject to time-varying parameters and irregular data sampling, making accurate modeling and stability guarantees extremely challenging. To address this, the proposed method uses a neural ordinary differential equation (NODE) to learn unknown time-varying parameter dynamics from irregularly observed data. This learned model is then integrated into the sampled-data MPC framework. In particular, the LMPC method guarantees the system's ultimate boundedness by deriving conditions based on the Gronwall–Bellman inequality. Finally, two practical examples illustrate the applicability of the LMPC method to real-world systems and demonstrate its quantitative stability analysis. © 2025

关键词： Sampled data control systems

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Distributed Fault-Tolerant Formation Game for Quadrotor UAVs With Hybrid Attacks

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Zhang, Rui-Ming Yao, Xiang-Yu Wang, Leimin Park, Ju H. Ge, Ming-Feng China University of Geosciences School of Mechanical Engineering and Electronic Information Wuhan430074 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China China University of Geosciences School of Automation Wuhan430074 China Yeungnam University Department of Electrical Engineering Gyeongsan38541 Korea Republic of

This manuscript studies the distributed faulttolerant formation for quadrotor unmanned aerial vehicles (QUAVs) subject to actuator failures and hybrid cyber attacks via a game-theoretical approach. The desired formation, which aligns with optimal self-interests, is achieved by seeking the Nash equilibrium (NE) of the game under a strongly connected directed graph. First, a secure fault-tolerant NE seeking algorithm is presented to handle actuator failures and false data injection (FDI) attacks, with the false data being measured through extended state observers (ESOs). The proposed algorithm is established on pseudo-gradient dynamics, in which a local estimate law is used to anticipate the actions of other QUAVs. Then, an ESOsbased algorithm is developed to mitigate the influence of FDI and denial-of-service (DoS) attacks, guaranteeing that all QUAVs exponentially converge to an adjustable small domain of NE by Lyapunov stability analysis. This work is distinct from existing studies by addressing the practical scenario, where physical failures and hybrid cyber attacks occur simultaneously in the game framework of QUAVs. Eventually, numerical experiments are conducted to demonstrate the efficacy of theoretical findings. © 1965-2011 IEEE.

关键词： Drones

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The staged optimal scheduling method for a multi-source industrial park with wind, solar, thermal, and energy storage 3

The staged optimal scheduling method for a multi-source indu...

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3rd International Conference on Smart Energy and Electrical engineering, SEEE 2024

作者： Hao, Ling Xu, Hui Tan, Zhenlong Zheng, Zhibao Xu, Fei Yang, Ruixuan Xu, Zhenyu Dai, Xuhui State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China Co. Ltd. Beijing101199 China School of Economics and Management Tsinghua University Beijing100084 China State Key Laboratory of Power Transmission Equipment Technology Chongqing University Chongqing400044 China

The increase in the proportion of renewable energy generation capacity has placed significant pressure on thermal power operations. Constructing energy storage facilities within the metering output of thermal power plants, allowing them to participate in peak load regulation with generating units jointly, is one effective way to alleviate the operational pressure on thermal power. To achieve comprehensive scheduling of thermal power plants with energy storage, this paper first establishes an overall output model for the coupling operation of thermal power and energy storage. Secondly, a staged optimal scheduling method for a multi-source industrial park with wind, solar, thermal, and energy storage is proposed to solve the optimization scheduling problem for the industrial park involving thermal power plants with energy storage participation. Finally, the simulation results based on a specific regional power grid in Inner Mongolia validate the feasibility of the proposed method. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Thermoelectric power plants

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Trajectory control and Fair Communications for Multi-UAV Networks: A Federated Multi-Agent Deep Reinforcement Learning Approach

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IEEE Transactions on Wireless Communications 2025年

作者： Tarekegn, Getaneh Berie Tesfaw, Belayneh Abebe Juang, Rong-Terng Saha, Dola Tarekegn, Robel Berie Lin, Hsin-Piao Tai, Li-Chia Nycu Department of Electrical and Computer Engineering 1001 University Road Hsinchu300 Taiwan Nycu Institute of Electrical and Control Engineering 1001 University Road Hsinchu300 Taiwan National Taipei University of Technology Department of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Institute of Aerospace and System Engineering Taipei10608 Taiwan University at Albany Suny Department of Electrical and Computer Engineering AlbanyNY12222 United States

Due to their highly flexible deployment and agility features, unmanned aerial vehicles (UAVs) serving as aerial base stations are increasingly being used in challenging environments, including emergency communication, traffic offloading, and failures of existing communications infrastructure. A reliable and effective communication service requires 3D deployment and autonomous UAV trajectory optimization in each time slot. While most prior approaches focus on trajectory design that maximizes communication coverage or network throughput without considering fairness as well as computation time. This paper presents a multi-UAV trajectory control and fair communication (TCFC) scheme that maximizes ground user data rate and communication coverage while ensuring service fairness in a UAV-aided wireless communications system. The proposed TCFC scheme employs a two-stage learning approach. Firstly, it develops a gated recurrent unit-based link quality estimation model to assess each user's link quality over time. Then, a federated multi-agent deep reinforcement learning (FedMADRL) algorithm is utilized to continuously adjust the trajectory of the UAVs, optimizing communication performance. We evaluated our proposed system using real channel measurement data, i.e., amplitude and phase signal information. The results show that the proposed TCFC scheme reduces computation time by 26.91% and provides comparable network performance with the baseline methods while improving DRL agents' privacy. © 2025 IEEE.

关键词： Deep reinforcement learning

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Reliable 2−∞ state estimation for delayed neural networks under weighted try-one-discard protocol

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Neurocomputing 2025年 635卷

作者： Luo, Yuqiang Guo, Siyu Zhao, Di Lin, Hong Informatization Office University of Shanghai for Science and Technology Shanghai200093 China Shanghai Key Lab of Modern Optical System Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China Business School University of Shanghai for Science and Technology Shanghai200093 China Institute of Intelligence Science and Engineering Shenzhen Polytechnic University Shenzhen518055 China

In this paper, the problem of reliable 2-∞ state estimation is addressed for discrete-time artificial neural networks with switched time-delays under weighted try-once-discard (WTOD) protocol. To mitigate data congestion and transmission burdens, the WTOD protocol is implemented in the transmission channels to optimize data communication, where the transmission priority is dynamically determined based on mission importance. A Bernoulli-distributed stochastic variable with known statistical properties is introduced to model the switching behavior between the presence and absence of time-delays and a failure matrix is constructed to characterize potential failures affecting the received measurement data. The primary objective of this paper is to develop a state estimator that effectively performs the desired estimation task by thoroughly accounting for the combined effects of switched time-delays and the WTOD protocol. Specifically, by utilizing Lyapunov theory and matrix inequality techniques, the estimator parameters are meticulously derived to ensure exponentially mean-square stability and 2-∞ performance. Finally, the efficacy and validity of the proposed algorithm are demonstrated through an illustrative example. © 2025 Elsevier B.V.

关键词： Stochastic systems

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Ultrafast-setting magnesium phosphate cement prepared from low-burned magnesium oxide for mixed stirring extrusion function integrated 3D printing applications

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Construction and Building Materials 2025年 479卷

作者： Deng, Yongjie Li, Nan Zhong, Jianjun Liang, Yun Lyu, Libo Yu, Qiuchun Li, Weihong College of Architectural Engineering Dalian University Dalian116622 China Key Laboratory for Prediction & Control on Complicated Structure System of the Education Department of Liaoning Province Dalian University Dalian116622 China Dalian Xueqing Mingfeng CNC Technology Co. Ltd Dalian116622 China

Regarding the issues of high cost, energy consumption and low activity in the existing preparation of magnesium phosphate cement (MPC) using heavy-burned magnesium oxide as raw material, this paper aims to prepare ultrafast-setting MPC using low-burned magnesium oxide. The objective was to explore the laws and mechanisms by which different magnesium oxide calcination temperatures affect the setting time, compressive and bond strengths, volumetric stability, and microstructure of MPC, thereby determining the optimal calcination temperature of magnesium oxide. Then, taking the setting time and compressive strength as performance indices, this paper determines the optimal baseline values of each component of MPC. Specifically, it ascertains the magnesium-phosphorus ratio, water-binder ratio, and borax doping amount. Subsequently, it integrates with the Mixed Stirring Extrusion Function Integrated 3D (MSEFI-3D) printing process to realize the application of MPC. The results reveal that, in comparison with MPC prepared from traditional heavy-burned magnesium oxide, appropriately reducing the calcination temperature of magnesium oxide can effectively enhance the mechanical properties of MPC and improve its volume stability. The compressive strength and interfacial bonding strength of MPC reach their optimal values when the calcination temperature of magnesium oxide is within the range of 850–950°C. Furthermore, the MPC prepared at this calcination temperature demonstrates excellent extrudability and constructability during the printing process. It can satisfy the requirements of 3D printing, thus providing a reference for the 3D-printing application of ultrafast - setting MPC prepared from low - burned magnesium oxide. © 2025 Elsevier Ltd

关键词： Printing properties

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