The memory behavior in liquid crystals(LCs)that is characterized by low cost,large area,high speed,and high-density memory has evolved from a mere scientific curiosity to a technology that is being applied in a variet...The memory behavior in liquid crystals(LCs)that is characterized by low cost,large area,high speed,and high-density memory has evolved from a mere scientific curiosity to a technology that is being applied in a variety of commodities.In this study,we utilized molybdenum disulfide(MoS_(2))nanoflakes as the guest in a homotropic LCs host to modulate the overall memory effect of the hybrid.It was found that the MoS₂nanoflakes within the LCs host formed agglomerates,which in turn resulted in an accelerated response of the hybrids to the external electric field.However,this process also resulted in a slight decrease in the threshold voltage.Additionally,it was observed that MoS₂nanoflakes in a LCs host tend to align homeotropically under an external electric field,thereby accelerating the refreshment of the memory behavior.The incorporation of a mass fraction of 0.1%2μm MoS₂nanoflakes into the LCs host was found to significantly reduce the refreshing memory behavior in the hybrid to 94.0 s under an external voltage of 5 V.These findings illustrate the efficacy of regulating the rate of memory behavior for a variety of potential applications.展开更多
Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.展开更多
[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-base...[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.展开更多
Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a vi...Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.展开更多
Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencie...Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies,an ABC variant named hybrid ABC(HABC) algorithm is proposed.Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.展开更多
Efficiency and linearity of the microwave power amplifier are critical elements for mobile communication systems. A memory polynomial baseband predistorter based on an indirect learning architecture is presented for i...Efficiency and linearity of the microwave power amplifier are critical elements for mobile communication systems. A memory polynomial baseband predistorter based on an indirect learning architecture is presented for improving the linearity of an envelope tracing （ET） amplifier with application to a wireless transmitter. To deal with large peak-to-average ratio （PAR） problem, a clipping procedure for the input signal is employed. Then the system performance is verified by simulation results. For a single carrier wideband code division multiple access （WCDMA） signal of 16-quadrature amplitude modulation （16-QAM）, about 2% improvement of the error vector magnitude （EVM） is achieved at an average output power of 45.5 dBm and gain of 10.6 dB, with adjacent channel leakage ratio （ACLR） of -64.55 dBc at offset frequency of 5 MHz. Moreover, a three-carrier WCDMA signal and a third-generation （3G） long term evolution （LTE） signal are used as test signals to demonstrate the performance of the proposed linearization scheme under different bandwidth signals.展开更多
Constitutive behavior of nickel-titanium shape memory alloy （Ni-Ti SMA） under hot deformation was investigated by means of the compression tests and the linear fitting method. Based on the true stres-strain curves o...Constitutive behavior of nickel-titanium shape memory alloy （Ni-Ti SMA） under hot deformation was investigated by means of the compression tests and the linear fitting method. Based on the true stres-strain curves of Ni-Ti SMA under compression at the strain rates of 0.001-1 s land at the temperatures ranging from 600 to 1 000 ℃, the constitutive equation of Ni-Ti SMA with respect to the Zener-Hollomon parameter was established according to the high stress level and the low stress level at various temperatures so as to more accurately describe the deformation behavior of Ni-Ti SMA during hot working. Dynamic recovery and dynamic recrystallization of Ni-Ti SMA occur under hot compression, which lays the theoretical foundation for understanding the constitutive behavior of Ni-Ti SMA.展开更多
In order to propel the development of metal magnetic memory （MMM） technique in fatigue damage detection, the Jiles-Atherton model （J-A model） was modified to describe MMM mechanism in elastic stress stage. A serie...In order to propel the development of metal magnetic memory （MMM） technique in fatigue damage detection, the Jiles-Atherton model （J-A model） was modified to describe MMM mechanism in elastic stress stage. A series of rotating bending fatigue experiments were conducted to study the stress-magnetization relationship and verify the correctness of modified J-A model. In MMM detection, the magnetization of material irreversibly approaches to the local equilibrium state Mo instead of global equilibrium state M^n under cyclic stress, and the M0-a curves are loops around the Mar,-a curve. The modified J-A model is constructed by replacing M~ in J-A model with M0, and it can describe the magnetomechanical effect well at low external magnetic field. In the rotating bending fatigue experiments, the MMM field distribution in normal direction around cylinder specimen is similar to the stress distribution, and the calculation result of model coincides with experiment result after some necessary modifications. The MMM field variation with time at a certain point in fatigue process is divided into three stages with the variation of stable stress-stain hysteresis loop, and the calculation results of model can explain not only the three stages of MMM field changes, but also the different change laws when the applied magnetic field and initial magnetic field are different. The MMM field distribution in normal direction along specimen axis reflects stress concentration effect at artificial defect, and the magnetic signal fluctuates around the defect at late fatigue stage. The calculation results coincide with the initial MMM principle and can explain signal fluctuates around the defect. The modified J-A model can explain experiment results well, and it is fit for MMM field characterization.展开更多
An innovative approach to increase structural survivability of concrete and maintain structural durability of concrete was developed in case of earthquakes and typhoons. This approach takes advantage of the superelast...An innovative approach to increase structural survivability of concrete and maintain structural durability of concrete was developed in case of earthquakes and typhoons. This approach takes advantage of the superelastic effect of shape memory alloy(SMA) and the cohering characteristic of repairing adhesive. These SMA wires and brittle fibers containing adhesives were embedded into concrete beams during concrete casting to form smart reinforced concrete beams. The self-repairing capacity of smart concrete beams was investigated by three-point bending tests. The experimental results show that SMA wires add self-restoration capacity,the concrete beams recover almost completely after incurring an extremely large deflection and the cracks are closed almost completely by the recovery forces of SMA wires. The number or areas of SMA wires has no influence on the tendency of deformation during loading and the tendency of reversion by the superelasticity. The adhesives released from the broken-open fibers fill voids and cracks. The repaired damage enables continued function and prevents further degradation.展开更多
Tension-compression fatigue test was performed on 0.45% C steel specimens.Normal and tangential components of magnetic memory testing signals,Hp(y) and Hp(x) signals,with their characteristics,K of Hp(y) and Hp(x)M of...Tension-compression fatigue test was performed on 0.45% C steel specimens.Normal and tangential components of magnetic memory testing signals,Hp(y) and Hp(x) signals,with their characteristics,K of Hp(y) and Hp(x)M of Hp(x),throughout the fatigue process were presented and analyzed.Abnormal peaks of Hp(y) and peak of Hp(x) reversed after loading; Hp(y) curves rotated clockwise and Hp(x) curves elevated significantly with the increase of fatigue cycle number at the first a few fatigue cycles,both Hp(y) and Hp(x) curves were stable after that,the amplitude of abnormal peaks of Hp(y) and peak value of Hp(x) increased more quickly after fatigue crack initiation.Abnormal peaks of Hp(y) and peak of Hp(x) at the notch reversed again after failure.The characteristics were found to exhibit consistent tendency in the whole fatigue life and behave differently in different stages of fatigue.In initial and crack developing stages,the characteristics increased significantly due to dislocations increase and crack propagation,respectively.In stable stage,the characteristics remained constant as a result of dislocation blocking,K value ranged from 20 to 30 A/(m·mm)-1,and Hp(x)M ranged from 270 to 300 A/m under the test parameters in this work.After failure,both abnormal peaks of Hp(y) and peak of Hp(x) reversed,K value was 133 A/(m·mm)-1 and Hp(x)M was-640 A/m.The results indicate that the characteristics of Hp(y) and Hp(x) signals were related to the accumulation of fatigue,so it is feasible and applicable to monitor fatigue damage of ferromagnetic components using metal magnetic memory testing(MMMT).展开更多
In order to investigate the physical mechanism of metal magnetic memory testing, both the influences of earth magnetic field and applied stress on magnetic domain structure were discussed. Static tension and fatigue t...In order to investigate the physical mechanism of metal magnetic memory testing, both the influences of earth magnetic field and applied stress on magnetic domain structure were discussed. Static tension and fatigue tests for low carbon steel plate specimens were carried out on hydraulic servo testing machine of MTS810 type and magnetic signals were measured during the processes by the type of EMS-2003 instrument. The results indicate that the initial magnetic signals of specimens are different before loading. The magnetic signals curves are transformed from initial random to regular pattern due to the effect of two types of loads. However, the shape and distribution of magnetic signal curves in the elastic region are different from that of plastic region in tension test. While in fatigue test those magnetic signals curves corresponding to different cycles are similar. The H_p(y) value of magnetic signals on the fracture zone increases dramatically at the breaking transient time and positive-negative magnetic poles occur on the two parts of fracture zone.展开更多
文摘The memory behavior in liquid crystals(LCs)that is characterized by low cost,large area,high speed,and high-density memory has evolved from a mere scientific curiosity to a technology that is being applied in a variety of commodities.In this study,we utilized molybdenum disulfide(MoS_(2))nanoflakes as the guest in a homotropic LCs host to modulate the overall memory effect of the hybrid.It was found that the MoS₂nanoflakes within the LCs host formed agglomerates,which in turn resulted in an accelerated response of the hybrids to the external electric field.However,this process also resulted in a slight decrease in the threshold voltage.Additionally,it was observed that MoS₂nanoflakes in a LCs host tend to align homeotropically under an external electric field,thereby accelerating the refreshment of the memory behavior.The incorporation of a mass fraction of 0.1%2μm MoS₂nanoflakes into the LCs host was found to significantly reduce the refreshing memory behavior in the hybrid to 94.0 s under an external voltage of 5 V.These findings illustrate the efficacy of regulating the rate of memory behavior for a variety of potential applications.
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
文摘[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.
基金National Natural Science Foundation of China(71690233,71971213,71901214)。
文摘Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.
基金supported by the National Natural Science Foundation of China(7177121671701209)
文摘Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies,an ABC variant named hybrid ABC(HABC) algorithm is proposed.Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.
基金supported by the National High Technology Researchand Development Program of China (863 Program) (YJCB2008023WL)
文摘Efficiency and linearity of the microwave power amplifier are critical elements for mobile communication systems. A memory polynomial baseband predistorter based on an indirect learning architecture is presented for improving the linearity of an envelope tracing （ET） amplifier with application to a wireless transmitter. To deal with large peak-to-average ratio （PAR） problem, a clipping procedure for the input signal is employed. Then the system performance is verified by simulation results. For a single carrier wideband code division multiple access （WCDMA） signal of 16-quadrature amplitude modulation （16-QAM）, about 2% improvement of the error vector magnitude （EVM） is achieved at an average output power of 45.5 dBm and gain of 10.6 dB, with adjacent channel leakage ratio （ACLR） of -64.55 dBc at offset frequency of 5 MHz. Moreover, a three-carrier WCDMA signal and a third-generation （3G） long term evolution （LTE） signal are used as test signals to demonstrate the performance of the proposed linearization scheme under different bandwidth signals.
基金Project(51071056) supported by the National Natural Science Foundation of ChinaProjects(HEUCFR1132, HEUCF121712)supported by the Fundamental Research Funds for the Central Universities of China
文摘Constitutive behavior of nickel-titanium shape memory alloy （Ni-Ti SMA） under hot deformation was investigated by means of the compression tests and the linear fitting method. Based on the true stres-strain curves of Ni-Ti SMA under compression at the strain rates of 0.001-1 s land at the temperatures ranging from 600 to 1 000 ℃, the constitutive equation of Ni-Ti SMA with respect to the Zener-Hollomon parameter was established according to the high stress level and the low stress level at various temperatures so as to more accurately describe the deformation behavior of Ni-Ti SMA during hot working. Dynamic recovery and dynamic recrystallization of Ni-Ti SMA occur under hot compression, which lays the theoretical foundation for understanding the constitutive behavior of Ni-Ti SMA.
基金Projects(11072056, 10772061) supported by the National Natural Science Foundation of ChinaProject(A200907) supported by the Natural Science Foundation of Heilongjiang Province,ChinaProject(20092322120001) supported by the PhD Programs Foundations of Ministry of Education of China
文摘In order to propel the development of metal magnetic memory （MMM） technique in fatigue damage detection, the Jiles-Atherton model （J-A model） was modified to describe MMM mechanism in elastic stress stage. A series of rotating bending fatigue experiments were conducted to study the stress-magnetization relationship and verify the correctness of modified J-A model. In MMM detection, the magnetization of material irreversibly approaches to the local equilibrium state Mo instead of global equilibrium state M^n under cyclic stress, and the M0-a curves are loops around the Mar,-a curve. The modified J-A model is constructed by replacing M~ in J-A model with M0, and it can describe the magnetomechanical effect well at low external magnetic field. In the rotating bending fatigue experiments, the MMM field distribution in normal direction around cylinder specimen is similar to the stress distribution, and the calculation result of model coincides with experiment result after some necessary modifications. The MMM field variation with time at a certain point in fatigue process is divided into three stages with the variation of stable stress-stain hysteresis loop, and the calculation results of model can explain not only the three stages of MMM field changes, but also the different change laws when the applied magnetic field and initial magnetic field are different. The MMM field distribution in normal direction along specimen axis reflects stress concentration effect at artificial defect, and the magnetic signal fluctuates around the defect at late fatigue stage. The calculation results coincide with the initial MMM principle and can explain signal fluctuates around the defect. The modified J-A model can explain experiment results well, and it is fit for MMM field characterization.
基金Project(50538020) supported by the National Natural Science Foundation of ChinaProject(20070421050) supported by China Postdoctoral Science Foundation
文摘An innovative approach to increase structural survivability of concrete and maintain structural durability of concrete was developed in case of earthquakes and typhoons. This approach takes advantage of the superelastic effect of shape memory alloy(SMA) and the cohering characteristic of repairing adhesive. These SMA wires and brittle fibers containing adhesives were embedded into concrete beams during concrete casting to form smart reinforced concrete beams. The self-repairing capacity of smart concrete beams was investigated by three-point bending tests. The experimental results show that SMA wires add self-restoration capacity,the concrete beams recover almost completely after incurring an extremely large deflection and the cracks are closed almost completely by the recovery forces of SMA wires. The number or areas of SMA wires has no influence on the tendency of deformation during loading and the tendency of reversion by the superelasticity. The adhesives released from the broken-open fibers fill voids and cracks. The repaired damage enables continued function and prevents further degradation.
基金Projects(50975283,50975287)supported by the National Natural Science Foundation of ChinaProject(2011CB013401)supported by the National Basic Research Program,China
文摘Tension-compression fatigue test was performed on 0.45% C steel specimens.Normal and tangential components of magnetic memory testing signals,Hp(y) and Hp(x) signals,with their characteristics,K of Hp(y) and Hp(x)M of Hp(x),throughout the fatigue process were presented and analyzed.Abnormal peaks of Hp(y) and peak of Hp(x) reversed after loading; Hp(y) curves rotated clockwise and Hp(x) curves elevated significantly with the increase of fatigue cycle number at the first a few fatigue cycles,both Hp(y) and Hp(x) curves were stable after that,the amplitude of abnormal peaks of Hp(y) and peak value of Hp(x) increased more quickly after fatigue crack initiation.Abnormal peaks of Hp(y) and peak of Hp(x) at the notch reversed again after failure.The characteristics were found to exhibit consistent tendency in the whole fatigue life and behave differently in different stages of fatigue.In initial and crack developing stages,the characteristics increased significantly due to dislocations increase and crack propagation,respectively.In stable stage,the characteristics remained constant as a result of dislocation blocking,K value ranged from 20 to 30 A/(m·mm)-1,and Hp(x)M ranged from 270 to 300 A/m under the test parameters in this work.After failure,both abnormal peaks of Hp(y) and peak of Hp(x) reversed,K value was 133 A/(m·mm)-1 and Hp(x)M was-640 A/m.The results indicate that the characteristics of Hp(y) and Hp(x) signals were related to the accumulation of fatigue,so it is feasible and applicable to monitor fatigue damage of ferromagnetic components using metal magnetic memory testing(MMMT).
文摘In order to investigate the physical mechanism of metal magnetic memory testing, both the influences of earth magnetic field and applied stress on magnetic domain structure were discussed. Static tension and fatigue tests for low carbon steel plate specimens were carried out on hydraulic servo testing machine of MTS810 type and magnetic signals were measured during the processes by the type of EMS-2003 instrument. The results indicate that the initial magnetic signals of specimens are different before loading. The magnetic signals curves are transformed from initial random to regular pattern due to the effect of two types of loads. However, the shape and distribution of magnetic signal curves in the elastic region are different from that of plastic region in tension test. While in fatigue test those magnetic signals curves corresponding to different cycles are similar. The H_p(y) value of magnetic signals on the fracture zone increases dramatically at the breaking transient time and positive-negative magnetic poles occur on the two parts of fracture zone.
