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Proceedings of the 60th Annual ACM/IEEE design Automation Conference

作者： Qinghai Liu Qinfei Tang Jiarui Chen Chuandong Chen Ziran Zhu Huan He Jianli Chen Yao-Wen Chang Center for Discrete Mathematics Fuzhou University Fuzhou China and Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou China Center for Discrete Mathematics Fuzhou University Fuzhou China College of Computer and Date Science Fuzhou University Fuzhou China Department of Microelectronics Fuzhou University Fuzhou China Department of Microelectronics Southeast University Nanjing China Hangzhou Huawei Enterprises Telecommunication Technologies Co. Ltd Hangzhou China State Key Lab of ASIC & System Fudan University Shanghai China Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan and Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (纸本)9798350323481

PCB routing becomes time-consuming as the complexity of PCB design increases. Unlike traditional schemes that treat the two essential PCB routing processes separately, namely, escape and bus routing, we consider the continuity between them and present a golden-pin-based routing scheme to find the desired solution with angle and topology constraints. Further, conventional rip-up and reroute methods are often ineffective and inefficient for congestion alleviation and routability optimization. We construct a component graph by modeling components as vertices and applying the minimum weight vertex covering method to improve the routability. A self-adaptable ordering method is presented for escape routing to arrange the pin order on the component boundary, guaranteeing successful bus routing. In addition, escape routing is performed based on a disjoint path method. We construct a dynamic Hanan grid in bus routing and utilize a novel congestion adjustment technique to improve solution quality. Compared with FreeRouting and Allegro, the experiment results show that our algorithm achieves high routability and a significant 90% runtime reduction.

关键词： escape routing

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Tool wear parameters identification in precision milling using a hybrid model combining cutting forces analytical model and Gaussian process regression method*

Tool wear parameters identification in precision milling usi...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Shengqiang Zhao Lin Zhou Hao Sun Fangyu Peng Rong Yan Teng Zhang National NC System Engineering Research Center School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China Wuhan Digital Design and Manufacturing Innovation Center Co. Ltd China Wuhan China State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665431545

In precision milling, tool wear has a significant impact on tool life prediction, machining accuracy and surface quality evaluation of workpiece. Tool wear pattern generally includes the increase of the tool flank wear width, as well as the expansion of the edge radius in precision process. Therefore, two tool wear parameters in precision milling have been generalized in this paper. The milling cutting force analytical model considering tool wear is set up to generate the data related to cutting force and tool wear parameters, which can be used as the dataset to construct and train the Gaussian process regression model. Finally, the trained regression model can output the prediction of tool flank wear width and edge radius as measured cutting forces treated as input values, which is helpful to realize the online monitoring of tool wear in the processing site environment. Consequently, the average relative prediction errors of tool flank wear width and edge radius are controlled within 2%, with the prediction time within 100ms.

关键词： Training Analytical models Parameter estimation Machine learning algorithms Force Milling Gaussian processes

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A hybrid mechanism-based and data-driven approach for the calibration of physical properties of Ni-based superalloy GH3128*

A hybrid mechanism-based and data-driven approach for the ca...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Hao Sun Lin Zhou Shengqiang Zhao Teng Zhang Fangyu Peng Rong Yan National NC System Engineering Research Center School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China Wuhan Digital Design and Manufacturing Innovation Center Co. Ltd China Wuhan China State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665431545

Within the finite element simulation model of metal manufacturing processes, the setting of physical properties of the materials will significantly affect the final simulation results. In this paper, a hybrid approach to integrate mechanical models and experimental data is proposed to identify the constitutive parameters and tool-chip friction coefficient of superalloy GH3128. Firstly, mechanical model is established and the relation between the constitutive parameters and the measured shear flow stress is determined. Combined with Bayesian inference, the fast inverse identification of the above parameters is realized. Then, a finite element surrogate model and Bayesian framework are fused for calibrating tool-chip friction coefficient. Finally, the effectiveness of the proposed approach is verified through milling experiments. The prediction accuracy of the prediction model are significantly improved by considering the uncertainties of physical properties. The relative error of the model in direction and direction are 11.4681% and 4.2989%, respectively.

关键词： Uncertainty Friction Simulation Metals Milling Predictive models Finite element analysis

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Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey

arXiv

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arXiv 2024年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Fang, Yuguang Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Computer Science City University of Hong Kong Kowloon Hong Kong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions. In addition, the mixture of experts (MoE) can enable the distributed and collaborative execution of AI models without performance degradation between connected vehicles. In this survey, we explore the integration of MoE and GAI to enable Artificial General Intelligence in IoV, which can enable the realization of full autonomy for IoV with minimal human supervision and applicability in a wide range of mobility scenarios, including environment monitoring, traffic management, and autonomous driving. In particular, we present the fundamentals of GAI, MoE, and their interplay applications in IoV. Furthermore, we discuss the potential integration of MoE and GAI in IoV, including distributed perception and monitoring, collaborative decision-making and planning, and generative modeling and simulation. Finally, we present several potential research directions for facilitating the integration. © 2024, CC BY.

关键词： Integration

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Time-dependent fatigue reliability estimation and optimal design of scraper chains under corrosive wear effect

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Mechanics Based design of Structures and Machines 2024年第10期52卷

作者： Shuai Li Zhencai Zhu Hao Lu Yujun Xue Gang Shen a School of Mechatronics Engineering Henan University of Science and Technology Luoyang Chinab Henan Key Laboratory for Machinery Design and Transmission System Henan University of Science and Technology Luoyang Chinac Longmen Laboratory Luoyang China d School of Mechatronic Engineering Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment China University of Mining and Technology Xuzhou China

Fatigue damage is one of the main failures of the scraper chain. Moreover, corrosive wear has a detrimental effect on fatigue crack propagation. It is so essential to study the fatigue reliability of the scraper chain considering the interaction of corrosive wear and fatigue. This paper is intended to propose a reliability-based estimation and optimal design of the scraper chain considering the performance degradation of the scraper chain under the interaction of corrosive wear and fatigue. First, the dynamic model of the scraper conveyor is established based on the finite element method. Second, Paris Low is used to empirically describe the occurrence of cracking with corrosion effects. Furthermore, the time-dependent reliability of scraper chains under the interaction of the corrosive wear and fatigue crack propagation is addressed by the fourth-moment saddlepoint approximation (FMSA) method, and the reliability-based design optimization (RBDO) of the scraper chain is then carried out. Finally, the applicability of the proposed method is verified by the SGZ1000/1400 scraper conveyor.

关键词： Fatigue reliability scraper chain saddlepoint approximation reliability based design optimization

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Research on in-plane fluid-elastic instability of heat transfer tube in steam generator 48

Research on in-plane fluid-elastic instability of heat trans...

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48th International Congress and Exhibition on Noise Control Engineering, INTER-NOISE 2019 MADRID

作者： Qi, Huan-Huan Jiang, Nai-Bin Huang, Xuan Feng, Zhi-Peng Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China 328 Changshun Avenue Chengdu China

ISBN: (纸本)9788487985317

Due to a small gap between the anti-vibration bars (AVBs) and the heat transfer tubes, and according to the position of AVBs and the number of continuous failures of the in-plane support, the in-plane restraint failure analysis of AVB is divided into 13 cases. The influence of the restraint failure on the in-plane mode of the heat transfer tube under different cases is analysed. The mode damping ratio was calculated by performing a weighted average method based on the mode shape function, and then the effects of different restraints by AVBs on fluid-elastic instability of steam generator tubes are studied. The analysis results show that with the increase of the continuous failure position of the in-plane support, the first-order modal frequency in the elbow zone decreases continuously, and the mode shape appearing in the elbow zone becomes more obvious. The first-order mode in the elbow zone is not necessarily the mode in which the largest fluidelastic instability ratio occurs. The vibration mode with the largest ratio of the fluid-elastic instability appears almost in the elbow zone. With the increase of the continuous failure position of the in-plane support, the fluid-elastic instability ratio increases continuously. When three or more continuous AVBs fail in-plane, fluidelastic instability will occur. © INTER-NOISE 2019 MADRID - 48th International Congress and Exhibition on Noise Control Engineering. All Rights Reserved.

关键词： Tubes (components)

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A Matching Based Escape Routing Algorithm with Variable design Rules and Constraints

A Matching Based Escape Routing Algorithm with Variable Desi...

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design Automation Conference

作者： Qinghai Liu Disi Lin Chuandong Chen Huan He Jianli Chen Yao-Wen Chang Center for Discrete Mathematics Fuzhou University Fuzhou China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou China Department of Microelectronics Fuzhou University Fuzhou China Hangzhou Huawei Enterprises Telecommunication Technologies Co. Ltd Hangzhou China State Key Lab of ASIC & System Fudan University Shanghai China Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

Escape routing is a critical problem in PCB routing, and its quality greatly affects the PCB design cost. Unlike the traditional escape routing that works mainly for the BGA package with unique line width and space, this paper presents a high-performance escape routing algorithm to handle problems with variable design rules and manual constraints, including variable line widths/spaces, the neck mode of wires, and the pad entry for differential pairs. We first propose a novel obstacle-avoiding method to project pins to the boundary and construct a channel projection graph. We then construct a bi-projection graph and propose a matching-based hierarchical sequencing algorithm to consider manual constraints. We perform global routing for each pin/differential pair by congestion-avoiding path initialization and rip-up and reroute path optimization. Finally, we complete detailed routing in every face, ensuring the wire angle and pad entry constraints. Experimental results show that our algorithm can achieve 100% routability without any design rule violation for all given industrial PCB instances, while two state-of-the-art routers cannot complete routing.

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An Efficient Digital Twin Based on Machine Learning Svd Autoencoder and Generalised Latent Assimilation for Nuclear reactor Physics

SSRN

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SSRN 2022年

作者： Gong, Helin Cheng, Sibo Chen, Zhang Li, Qing Quilodrán-Casas, César Xiao, Dunhui Arcucci, Rossella SJTU-ParisTech Elite Institute of Technology Shanghai 200240 China Data Science Institute Imperial College London LondonSW7 2AZ United Kingdom Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610041 China Faculty of Sciences and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom

This paper proposes an approach that combines reduced-order models with machine learning in order to create an digital twin to predict the power distribution over the core during the operation stage. The operational digital twin is designed to solve forward problems given input operation parameters, as well as to solve inverse problems given some observations of the power field. The forward model is realised using SVD autoencoder reduced order model and machine learning methods. The inverse model is based on a generalised latent assimilation method. The effectiveness in the sense of accuracy and real-time solver of the digital twin is illustrated through a real engineering problem in nuclear reactor physics - reactor core simulation in the life cycle of HPR1000 affected by control rod inserting step, burnup, power level and inlet temperature of the coolant, which shows potential applications for on-line monitoring purpose. © 2022, The Authors. All rights reserved.

关键词： Inverse problems

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Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural networks

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Physical Review C 2022年第3期105卷 034611-034611页

作者： X. Zhang (张鑫) X. Liu (刘星泉) Y. Huang (黄宇) W. Lin (林炜平) H. Zheng (郑华) R. Wada A. Bonasera Z. Chen (陈志强) L. Chen (陈蕾) J. Han (韩纪锋) R. Han (韩瑞) M. Huang (黄美容) Q. Hu (胡强) Q. Leng (冷强钟) C. W. Ma (马春旺) G. Qu (曲国峰) P. Ren (任培培) G. Tian (田国玉) Z. Xu (徐子虚) Z. Yang (杨振雷) L. Zhang (张丽娜) Key Laboratory of Radiation Physics and Technology of the Ministry of Education Sichuan University Chengdu 610064 China School of Physics and Information Technology Shaanxi Normal University Xi'an 710119 China Cyclotron Institute Texas A&M University College Station Texas 77843 USA Laboratori Nazionali del Sud INFN via Santa Sofia 62 95123 Catania Italy Institute of Modern Physics Chinese Academy of Sciences Lanzhou 730000 China College of Physics and Electronics information Inner Mongolia University for Nationalities Tongliao 028000 China School of Physics Henan Normal University Xinxiang 453007 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu 610213 China School of Information Sciences and Engineering Lanzhou University Lanzhou 730000 China

A deep learning based method with the convolutional neural network (CNN) algorithm for determining the impact parameters is developed by using constrained molecular-dynamics model simulations, focusing on the heavy-ion collisions at the low-intermediate incident energies from several tens to one hundred MeV/nucleon in which the emissions of heavy fragments with the charge numbers larger than three become crucial. To make the CNN applicable in the task of the impact-parameter determination at the present energy range, specific improvements are made in the input selection, the CNN construction, and the CNN training. It is demonstrated from the comparisons of the deep CNN method and the conventional methods with the impact-parameter-sensitive observables that the deep CNN method shows better performance for determining the impact parameters, especially leading to the capability of providing better recognition of the central collision events. With a proper consideration of the experimental filter effect in both training and testing processes to keep consistency with the actual experiments, the good performance of the deep CNN method holds and performs significantly better in terms of predicting the impact parameters and recognizing the central collision events compared with conventional methods, demonstrating the superiority of the present deep CNN method. The deep CNN method with the consideration of the filter effect is applied in the deduction of nuclear stopping power. Higher accuracy for the stopping power deduction is achieved benefitting from the better impact-parameter determination using the deep CNN method, compared with using conventional methods. This result reveals the importance of selecting a reliable impact-parameter-determination method in the experimental deduction of the nuclear stopping power as well as other observables.

关键词： Low & intermediate energy heavy-ion reactions Convolutional neural networks Machine learning Nuclear data analysis & compilation

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Emergent grain boundary phases in stressed polycrystalline thin films

arXiv

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arXiv 2022年

作者： Wang, Mengyuan Yan, Ruilin Han, Xiao Wang, Hailong Upmanyu, Moneesh CAS Key Laboratory of Mechanical Behavior and Design of Materials Department of Modern Mechanics CAS Center for Excellence in Complex System Mechanics University of Science and Technology of China Anhui Hefei230027 China Department of Mechanical and Industrial Engineering Northeastern University BostonMA02115 United States

The grain boundary (GB) microstructure influences and is influenced by the development of residual stresses during synthesis of polycrystalline thin films. Recent studies have shown that the frustration between the preferred growth direction and rotations of abutting crystals to local cusps in GB energies leads to internal stresses localized within nanoscopic surface layers around the valleys and ridges that form at emergent boundaries (eGBs). Using a combination of continuum frameworks, numerical analyses and all-atom simulations of bicrystal h111i copper films, we show that eGBs tune their surface morphology and rotation extent in response to external strains. Compression favors rotation to and growth of low energy GB phases (complexions) at eGB valleys while tension favors the transitions at eGB ridges, a reflection of the stress-induced mass efflux/influx that changes the energetic balance between interfacial and deformation energies. Molecular dynamics simulations of strained and growing bicrystal films reveal that the eGB phase transition is coupled to island formation at the surface triple junctions, providing a direct link between eGB phases and surface step flow. The interplay between eGB structure, morphology and mechanics emerges as a crucial ingredient for predictive understanding of stress and morphological evolution during film growth, with broad implications for multifunctional response of polycrystalline surfaces in a diverse range of surface phenomena such as surface mediated deformation, interfacial embrittlement, thermal grooving, stress corrosion, surface catalysis and topological conduction. © 2022, CC BY.

关键词： Residual stresses

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