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Preparation of tungsten-particle-reinforced Zr-based bulk metallic glass composites by two-step spark plasma sintering:microstructure evolution,densification mechanism and mechanical properties

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Rare Metals 2024年第4期43卷 1793-1808页

作者： yun-Fei Ma Pan Gong Mao Zhang Hui-EHu Zhen Peng Xiao Xu xin Wang Mehdi Malekan Xue-Feng Tang Lei deng Jun-Song Jin xin-yun Wang State Key Laboratory of Materials Processing and Die and Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan430074China Research Institute of Huazhong University of Science and Technology in Shenzhen Shenzhen518057China Department of Chemistry and Materials Naval University of EngineeringWuhan430032China School of Materials Science and Engineering Jiangsu UniversityZhenjiang212013China College of Machine Shanghai Dianji UniversityShanghai201306China School of Metallurgy and Materials Engineering College of EngineeringUniversity of TehranTehran 11155-4563Iran

A new two-step spark plasma sintering(TSS)process with low-temperature pre-sintering and high-temperature final sintering has been successfully applied to prepare the tungsten-particle(Wp)-reinforced bulk metallic glass composites(Wp/BMGCs).Compared to normal spark plasma sintering(NS),the densification rate and relative density of Wp/BMGCs can be improved by selecting TSS with appropriate sintering pressure in the low temperature pre-sintering ***,the compressive strength and plastic strain of 30%Wp/BMGCs prepared by TSS are both higher than those of the samples prepared by *** TSS process can significantly enhance the compressive strength of 30%Wp/BMGCs by 12%and remarkably increase the plastic strain by 50%,while the trend is completely opposite for 50%Wp/***-in situ experiments and finite element simulations reveal that uneven temperature distribution among particles during low-temperature pre-sintering causes local overheating at contact points between particles,accelerating formation of sintering neck between particles and plastic deformation of *** the volume fraction of Wp is low,TSS can improve the interface bonding between particles by increasing the number of sintering *** makes the fracture mode of Wp/BMGCs being predominantly transgranular ***,as the volume fraction of Wp increases,the adverse effects of Wp plastic deformation are becoming more and more *** aggregated Wp tends to form a solid"cage structure"that hinders the bonding between particles at the interface;correspondingly,the fracture behavior of Wp/BMGCs is mainly dominated by intergranular ***,reducing the sintering pressure during the low-temperature pre-sintering stage of TSS has been shown to effectively decrease plastic deformation in Wp,resulting in a higher degree of densification and better mechanical properties.

关键词： Bulk metallic glass composites Tungsten particle Two-step spark plasma sintering Densification mechanism Mechanical properties

Microstructure and mechanical properties of Co-28Cr-6Mo-0.22C investment castings by current solution treatment

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China Foundry 2024年第4期21卷 369-378页

作者： Ze-yu Dan Jun Liu Jian-lei Zhang Yan-hua Li Yuan-xin deng yun-hu Zhang Chang-jiang Song Center for Advanced Solidification Technology School of Materials Science and EngineeringShanghai UniversityShanghai 200444China Wuxi CastPro Precision Co. Ltd.Wuxi 214105JiangsuChina

This study examined the impact of current solution treatment on the microstructure and mechanical properties of the Co-28Cr-6Mo-0.22C alloy investment *** findings reveal that the current solution treatment significantly promotes the dissolution of carbides at a lower *** optimal conditions for solution treatment are determined as a solution temperature of 1,125°C and a holding time of 5.0 *** these parameters,the size and volume fraction of precipitated phases in the investment castings are measured as6.2μm and 1.1vol.%.The yield strength,ultimate tensile strength,and total elongation of the Co-28Cr-6Mo-0.22C investment castings are 535 MPa,760 MPa,and 12.6%,*** values exceed those obtained with the conventional solution treatment at 1,200°C for 4.0 *** findings suggest a phase transformation of M_(23)C_(6)→σ+C following the current solution treatment at 1,125°C for 5.0 *** comparison,the traditional solution treatment at 1,200°C for 4.0 h leads to the formation of M_(23)C_(6)and M_(6)C *** is noteworthy that the non-thermal effect of the current during the solution treatment modifies the free energy of both the matrix and precipitation *** modification lowers the phase transition temperature of the M_(23)C_(6)→σ+C reaction,thereby facilitating the dissolution of *** a result,the current solution treatment approach achieves carbide dissolution at a lower temperature and within a significantly shorter time when compared to the traditional solution treatment methods.

关键词： CoCrMo alloy investment castings current solution treatment microstructure mechanical property carbide

Probabilistic seismic inversion based on physics-guided deep mixture density network

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Petroleum Science 2024年第3期21卷 1611-1631页

作者： Qian-Hao Sun Zhao-yun Zong xin Li National Key Laboratory of Deep Oil and Gas China University of Petroleum(East China)Qingdao266580ShandongChina Laoshan Laboratory Qingdao266580ShandongChina CNOOC Research Institute Ltd. Beijing100028China

Deterministic inversion based on deep learning has been widely utilized in model parameters *** by logging data,seismic data,wavelet and modeling operator,deterministic inversion based on deep learning can establish nonlinear relationships between seismic data and model ***,seismic data lacks low-frequency and contains noise,which increases the non-uniqueness of the *** conventional inversion method based on deep learning can only establish the deterministic relationship between seismic data and parameters,and cannot quantify the uncertainty of *** order to quickly quantify the uncertainty,a physics-guided deep mixture density network(PG-DMDN)is established by combining the mixture density network(MDN)with the deep neural network(DNN).Compared with Bayesian neural network(BNN)and network dropout,PG-DMDN has lower computing cost and shorter training time.A low-frequency model is introduced in the training process of the network to help the network learn the nonlinear relationship between narrowband seismic data and low-frequency *** addition,the block constraints are added to the PG-DMDN framework to improve the horizontal continuity of the inversion *** illustrate the benefits of proposed method,the PG-DMDN is compared with existing semi-supervised inversion *** synthetic data examples of Marmousi II model are utilized to quantify the influence of forward modeling part,low-frequency model,noise and the pseudo-wells number on inversion results,and prove the feasibility and stability of the proposed *** addition,the robustness and generality of the proposed method are verified by the field seismic data.

关键词： Deep learning Probabilistic inversion Physics-guided Deep mixture density network

Adaptive 5G-and-beyond network-enabled interpretable federated learning enhanced by neuroevolution

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Science China(Information Sciences) 2024年第7期67卷 107-132页

作者： Bin CAO Jianwei ZHAO xin LIU yun LI State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology School of Artificial Intelligence Hebei University of Technology School of Economics and Management Hebei University of Technology Industrial Artificial Intelligence Centre Shenzhen Institute for Advanced StudyUniversity of Electronic Science and Technology of China

Mobile telemedicine systems based on the next-generation communication will significantly enhance deep fusion of network automation and federated learning(FL), but data privacy is a paramount issue in sectors like healthcare. This work hence considers FL augments 5G-and-beyond networks by training deep learning(DL) models without the need to exchange raw data. The substantial communication loads imposed on by extensive parameters involved in DL models are managed through adaptive scheduling mechanisms effectively. To address the opaque nature of DL models and to improve the interpretability of FL models, we introduce a convolutional fuzzy rough neural network specifically designed for medical image processing. We also develop a multiobjective memetic evolutionary algorithm to streamline and optimize the neural network architectures. Our comprehensive FL framework integrates smart scheduling, interpretable fuzzy rough logic,and neuroevolution. This framework is shown to improve communication efficiency, increase interpretability of diagnosis with protected privacy, and generate low-complexity neural architectures.

关键词： 5G-and-beyond network interpretable federated learning mobile telemedicine system fuzzy rough set theory neuroevolution

(Benzimidazolium)2GeI4:A layered two-dimensional perovskite with dielectric switching and broadband near-infrared photoluminescence

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结构化学 2024年第6期43卷 45-49页

作者： Xuan Zhu Lin Zhou Xiao-yun Huang Yan-Ling Luo xin deng xin Yan Yan-Juan Wang Yan Qin Yuan-Yuan Tang Ordered Matter Science Research Center Nanchang UniversityNanchang330031People's Republic of China

Two-dimensional(2D)lead halide perovskites have attracted tremendous attention due to their outstanding physical ***,the presence of toxic lead is a major problem for large-scale ***,we report the synthesis,phase transition,dielectric and photoluminescence(PL)properties of a lead-free 2D Ge-based perovskite(BIM)2GeI4(BIM=benzimidazolium)which experiences a phase transition at 405 K accompanied by an order-disorder change of organic ***,the step-like dielectric transition near the phase transition temperature makes it suitable for dielectric ***,(BIM)2GeI4 has a direct bandgap of 2.23 eV and broadband emission from 550 to 1000 nm,implying its unique optical property for potential near-infrared lighting and *** work provides a fresh example for the development of lead-free 2D Ge-based perovskite with potential applications in the fields of optoelectronics and high temperature dielectric switching.

关键词： Dielectric switching Photoluminescence Two-dimensional perovskite Lead-free perovskite

SDCC: software-defined collective communication for distributed training

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Science China(Information Sciences) 2024年第9期67卷 105-125页

作者： xin JIN Zhen ZHANG yunshan JIA yun MA Xuanzhe LIU School of Computer Science Peking University Department of Computer Science and Technology Johns Hopkins University

Communication is crucial to the performance of distributed training. Today's solutions tightly couple the control and data planes and lack flexibility, generality, and performance. In this study, we present SDCC, a software-defined collective communication framework for distributed training. SDCC is based on the principle of modern systems design to effectively decouple the control plane from the data *** abstracts the operations for collective communication in distributed training with dataflow operations and unifies computing and communication with a single dataflow graph. The abstraction, together with the unification, is powerful: it enables users to easily express new and existing collective communication algorithms and optimizations, simplifies the integration with different computing engines(e.g., Py Torch and Tensor Flow) and network transports(e.g., Linux TCP and kernel bypass), and allows the system to improve performance by exploiting parallelism exposed by the dataflow graph. We further demonstrate the benefits of SDCC in four use cases.

关键词： machine learning systems distributed training deep learning collective communication software-defined networking

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Effect of thermo-mechanical treatment on microstructure and mechanical properties of wire-arc additively manufactured Al-Cu alloy

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Journal of Central South University 2024年第7期31卷 2181-2193页

作者： ZHANG Tao QIN Zhen-yang GONG Hai WU yun-xin CHEN xin Light Alloy Research Institute Central South UniversityChangsha 410083China State Key Laboratory of Precision Manufacturing for Extreme Service Performance Central South UniversityChangsha 410083China Welding and Additive Manufacturing Centre Cranfield UniversityBedfordMK430ALUK

Wire-arc additive manufacture(WAAM)has great potential for manufacturing of Al-Cu ***,inferior mechanical properties of WAAM deposited material restrict its industrial ***-layer cold rolling and thermo-mechanical heat treatment(T8)with pre-stretching deformation between solution and aging treatment were adopted in this *** effects on hardness,mechanical properties and microstructure were analyzed and compared to the conventional heat treatment(T6).The results show that cold rolling increases the hardness and strengths,which further increase with T8 *** ultimate tensile strength(UTS)of 513 MPa and yield stress(YS)of 413 MPa can be obtained in the inter-layer cold-rolled sample with T8 treatment,which is much higher than that in the as-deposited *** cold-rolled samples show higher elongation than that of as-deposited ones due to significant elimination of porosity in cold rolling;while both the T6 and T8 treatments decrease the *** cold rolling and pre-stretching deformation both contribute to the formation of dense and dispersive precipitatedθ′phases,which inhibits the dislocation movement and enhances the strengths;as a result,T8 treatment shows better strengthening effect than the T6 *** strengthening mechanism was analyzed and it was mainly related to work hardening and precipitation strengthening.

关键词： wire-arc additive manufacture inter-layer cold rolling thermal-mechanical treatment microstructure mechanical properties strengthening mechanism

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

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Petroleum Science 2024年第2期21卷 885-902页

作者： xin Zhang yun-Hu Lu Yan Jin Mian Chen Bo Zhou State Key Laboratory of Petroleum Resources and Prospecting China University of Petroleum(Beijing)Beijing102249China College of Petroleum Engineering China University of Petroleum(Beijing)Beijing102249China College of Artificial Intelligence China University of Petroleum(Beijing)Beijing102249China PetroChina Tarim Oilfield Company KorlaXinjiang841000China

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum *** deep learning technique has been receiving more interest due to the great potential to deal with pore pressure ***,most of the traditional deep learning models are less efficient to address generalization *** fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic ***,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss *** machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure *** CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few *** validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization *** adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

关键词： Pore pressure prediction Seismic data 1D convolution pyramid pooling Adaptive physics-informed loss function High generalization capability

Controllable Ring-opening Polymerization ofδ-Valerolactone Catalyzed by Quinolinyl-Urea/MTBD Systems

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Chinese Journal of Polymer Science 2024年第8期42卷 1103-1110页

作者： xin-Yu Liu Liang Zhu Su-yun Jie Bo-Geng Li State Key Laboratory of Chemical Engineering College of Chemical and Biological EngineeringZhejiang UniversityHangzhou 310058China

Due to their excellent biocompatibility and biodegradability,aliphatic polyesters are widely used in the biomedical,packaging and agricultural fields,which are usually accessed by the ring-opening polymerization(ROP)of lactones and the catalysts particularly play an important *** a series of quinolinyl-urea catalysts have been synthesized via the reaction between isocyanate and aminoquinoline with an amino group at different substitution positions and *** combination with 7-methyl-1,5,7-triazabicyclo[4,4,0]dec-5-ene(MTBD)as a cocatalyst and benzyl alcohol(BnOH)as an initiator,1-(3,5-bis(trifluoromethyl)phenyl)-3-(quinolin-3-yl)urea(3-QU)was observed to be most active for the ROP ofδ-valerolactone(δ-VL).The polymerization conditions were optimized by varying the type of organic base,catalyst concentration and reaction *** changing the ratio of[M]_0/[I],linear polyvalerolactones(PVLs)with different molecular weights and narrow molecular weight distribution were *** kinetic and chain extension experiments were carried out to prove the"living"/controllable *** the NMR experiments were used to support the proposal of possible mechanism.

关键词： Quinolinyl-urea Ring-opening polymerization PVL