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11th International Symposium on Precision Mechanical Measurements

作者： Zhao, Jiang-Yu Wang, Yang Li, Mi Tang, Qi-Fu Engineering Research Center of Mechanical Testing Technology and Equipment Ministry of Education Chongqing University of Technology Chongqing400054 China Chongqing Key Laboratory of Time-grating Sensing and Advanced Testing Technology Chongqing University of Technology China

ISBN: (纸本)9781510680296

Error analysis on a PCB based inductive linear displacement sensor is introduced in this paper, and a self-correcting method independent of external reference for the sensor is proposed in this paper. This method is mainly aimed at the linear displacement error of short period caused by manufacturing and assembly of sensor prototype. In this paper, only the 1-order, 2-order and 4-order errors common in the measurement process are studied. Therefore, studying from the output signal, the generation mechanism of three kinds of errors has been traced theoretically, and the functional relationship between the output signal and error has been calculated, thus establishing the short-period error function model of the relevant sensor signal. In the experimental verification stage, four comparative tests were carried out, which effectively verified the feasibility of the self-correcting method. The results of prototype experiments show that the measurement accuracy of the sensor prototype has been effectively improved. The 1-order error is reduced by about 64.5%, the 2-order error is reduced by about 3.8%, the 4-order error is reduced by about 83%, and the total error is reduced from 51.6μm to 36.2μm. © 2024 SPIE.

关键词： Velocity measurement

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An Absolute Linear Displacement Sensor Based on Eddy Current Effect 11

An Absolute Linear Displacement Sensor Based on Eddy Current...

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11th International Symposium on Precision Mechanical Measurements

作者： Li, Peilin Wu, Liang Zhou, Fengyu Chen, Liming Engineering Research Center of Mechanical Testing Technology and Equipment Ministry of Education Chongqing University of Technology Chongqing400054 China Chongqing Key Laboratory of Time Grating Sensing and Advanced Detection Technology Chongqing University of Technology Chongqing400054 China

ISBN: (纸本)9781510680296

To meet the requirements of modern industry to absolute measurement, an absolute linear displacement sensor based on eddy current effect is proposed in this paper. The sensor is composed of a stationary ruler with metal reflection conductors and a movable ruler with field coils and inductive coils and includes a Coarse Measurement Track (CMT) and a Fine Measurement Track (FMT). The metal reflection conductors below the movable ruler will generate eddy current and a radiative magnetic field will be produced when the high-frequency alternating current is applied to field coils. The variation of displacement depends on the inductive Electromotive Forces (EMFs) of inductive coils. The CMT is used for positioning the pitch number while the FMT is used for high-precision displacement calculation. The absolute displacement is obtained by the combination of the CMT and the FMT. In this paper, the working principle and structure of the sensor is firstly introduced. Then, the electromagnetic field simulation is conducted according to the sensor model to verify feasibility and rationality. Finally, the sensor prototype is fabricated by PCB technology for experimental validation. The experimental results manifest that the peak-to-peak value of measurement error is 13 μm in 4 mm pitch and the peak-to-peak value of measurement error is 49 μm in the full measurement range of 500 mm. © 2024 SPIE.

关键词： Displacement measurement

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Structural Design and Simulation Analysis of a Worm Gear Pair Dynamic Inspection Instrument Based on FMT System 11

Structural Design and Simulation Analysis of a Worm Gear Pai...

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11th International Symposium on Precision Mechanical Measurements

作者： Zheng, Yong Xie, Wei Chen, Yan Peng, Donglin Duan, Yuancong Engineering Research Center of Mechanical Testing Technology and Equipment of Ministry of Education Chongqing University of Technology Chongqing400054 China Chongqing Key Laboratory of Time-Grating Sensing and Advanced Testing Technology Chongqing University of Technology Chongqing400054 China School of Electrical and Electronic Engineering Chongqing University of Technology Chongqing400054 China

ISBN: (纸本)9781510680296

In this paper, an instrument is designed for the detection of transmission error of medium and large-sized worm gear pair based on the FMT system, which has the advantages of large size and dynamic measurement. The working principle of the instrument is introduced, and the concept of integrated processing of the time-grid sensor and related parts of the instrument is proposed. The key components are designed using SolidWorks, and the structural layout and virtual assembly are completed;the finite element analysis of the beam assembly is carried out and the structure optimization is carried out through Ansys Workbench, and the 1st-order intrinsic frequency is increased by 24.71% after the optimization and the amplitudes corresponding to the intrinsic frequencies are significantly reduced in each order. After optimization, the 1st order intrinsic frequency is increased by 24.71%, and the amplitude corresponding to each order of intrinsic frequency is significantly reduced. © 2024 SPIE.

关键词： Worm gears

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An online monitoring method for embedded time-gate displacement sensor operation state base on CNN and LSTM 11

An online monitoring method for embedded time-gate displacem...

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11th International Symposium on Precision Mechanical Measurements

作者： Gou, Li Wang, Minxuan Peng, Donglin Fan, Wanlin Engineering Research Center of Mechanical Testing Technology and Equipment Ministry of Education Chongqing University of Technology Chongqing40054 China Chongqing Innovation Center of Industrial Big-Data Co. Ltd Chongqing400700 China Chongqing Gearbox Co. Ltd. Chongqing402263 China Chongqing Key Laboratory of Time-grating Sensing and Advanced Testing Technology Chongqing University of Technology Chongqing400054 China

ISBN: (纸本)9781510680296

In view of the problems that embedded time-gate displacement sensor (hereinafter referred to as time-gate), which has severe working conditions, high-precision state is difficult to maintain for a long time, and the operating state is difficult to monitor online. this paper proposes an online monitoring method of time-gate operating state, based on a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM). Firstly, feature information was obtained from the geometric, operating, electromagnetic simulation and environmental characteristics of the time-gate displacement sensor. Then, A time-gate operating state prediction model was constructed by feeding feature information into CNN-LSTM model and verified by test set, The CNN layers Perform feature learning and extraction and the LSTM layers Process timing data. Finally, the relevant real-time data are fed into the above operating state monitoring system, which achieve the real-time online Monitoring of the time-gate operation state. The experimental results show that, the extreme prediction errors such as abnormal and no alarm were avoided by this method in the test set, and the recognition accuracy of the state is up to 91.1% by this prediction model. Effectively guarantee the safe and stable operation of the time-gate and the equipment in which it is located. © 2024 SPIE.

关键词： Convolutional neural networks

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Burrs at exit position in slot micro-milling considering tool runout: Feature extraction, characterization, and modeling

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Journal of Manufacturing Processes 2025年 149卷 754-769页

作者： Da Qu Zhipeng Xu Yang Song Xiaoyu Ma Jianwei Ji Engineering Research Center of Mechanical Testing Technology and Equipment (Ministry of Education) Chongqing University of Technology Chongqing China College of Mechanical Engineering Chongqing University of Technology Chongqing China Chongqing Key Laboratory of Time Grating Sensing and Advanced Testing Technology Chongqing University of Technology Chongqing China Institute of Advanced Technology Chongqing University of Technology Chongqing China

Micro-milling is a significant application in manufacturing components for various crucial fields such that inertial navigation. Strict requirements for machining quality, such as surface finish and dimensional accuracy, are proposed to ensure adequate functionality of inertial navigation components. However, it is severely limited by burr formation, especially at the work position of the component. To this end, this paper first presents an improved Canny-based algorithm for extracting continuous edges of exit burrs. The characterizations of the extracted burrs are then carried out for various machining processes. Based on orthogonal experiments of the machining process, the influence and the interaction influence of the feed per tooth and depth of cut on crucial burrs, including exit-burrs and exit side burrs, are revealed by analysis of mean. It is observed that the depth of cut has more significant impacts on formations of exit-burrs and exit side burrs. An analytical model, considering tool runout, is then established to predict the burr height of exit side burrs, and the applicability of the model is comparatively analyzed, showing that tool runout should not be ignored in predicting burr characters. The mean height of exit-burrs is predicted by modeling an analytical model based on the experimental results. The root-mean-square error (RMSE) of the predicted exit-burrs is 4.363 μm, noting that the prediction of burr characters in micro-milling is extremely challenging due to the complex process of material removal and micro-structural effects encountered during the micro-scaled machining process.

关键词： Milling (machining)

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Lightweight CNN for HRRP Recognition Based on Attention Mechanism Structured Pruning

Lightweight CNN for HRRP Recognition Based on Attention Mech...

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2023 IEEE International Radar Conference, RADAR 2023

作者： Wang, Yanhua Zhang, Zhilong Yuan, Mingchen Liao, Jiandong Zhang, Liang Beijing Institute of Technology Electromagnetic Sensing Research Center of Cemee State Key Lab Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Beijing Institute of Technology Advanced Technology Research Institute Shandong250300 China

ISBN: (纸本)9781665482783

Deep convolutional neural network (CNN) has been widely investigated for radar target high resolution range profile (HRRP) recognition. However, the deep structures of CNN require high storage and computational capabilities, thus restricting its applications with limited resources. In this paper, we design a lightweight CNN model with structure pruning based on the channel-wise attention mechanism. Specifically, the attention value is used to represent the importance of the filters in CNN. Furthermore, Greedy strategy and fine-tuning are adopted in the pruning process to minimize the loss of model performance. The results of public dataset show that the recognition accuracy of the proposed method decreases by less than 0.13% when the pruning ratio is 80%. © 2023 IEEE.

关键词： Convolutional neural networks

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Radar range Extended target detection using Prototypical Network 2

Radar range Extended target detection using Prototypical Net...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Ma, Wenhao Yuan, Mingchen Wang, Xinyang Zhang, Liang Wang, Yanhua Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Zhengzhou Research Institute Beijing Institute of Technology Zhengzhou450000 China Electromagnetic Sensing Research Center of Cemee State Key Laboratory Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China

ISBN: (纸本)9798331515669

In radar ground stationary or slow-moving target detection, the target detection algorithm based on High-Resolution Range Profile (HRRP) is an important technical approach. Traditional target detection algorithms based on HRRP are difficult to apply in target detection tasks under multiple terrains. To this end, this paper proposes a feature detection algorithm based on deep learning for target polarized HRRP. This paper studies an end-to-end target detection algorithm based on prototypical networks. This algorithm utilizes prototypical networks to extract deep features from input HRRP and constructs the feature distribution of clutter. Then, it calculates the distance between the features of the input test sample and the clutter feature center, and compares it with the threshold calculated based on the false alarm probability to complete the detection. Under the backgrounds of bare soil and highway, the algorithm improves by 22% compared to the traditional energy detection algorithm for two types of vehicle targets at a signal-to-clutter ratio (SCR) of 0dB. © 2024 IEEE.

关键词： Clutter (information theory)

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HRRP SCATTERING CENTER ESTIMATION BASED ON MODELLED NEURAL NETWORK

HRRP SCATTERING CENTER ESTIMATION BASED ON MODELLED NEURAL N...

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IET International Radar Conference 2023, IRC 2023

作者： Zhang, Yu-Ang Zhou, Na Wang, Yanhua Zhang, Liang School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Electromagnetic Sensing Research Center of CEMEE State Key Laboratory Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Advanced Technology Research Institute Beijing Institute of Technology Shandong250300 China

ISBN: (纸本)9781839539954

Scattering center estimation of HRRP is crucial for radar automatic target recognition (RATR). Traditional estimation algorithms either necessitate prior information of the signal or suffer from mismatch issues or possess excessive computational costs. In this paper, we propose an HRRP-modelled neural network (HNN), which addresses the mismatch problem and exhibits better accuracy. HNN combines the HRRP signal model with its layered structure, including input, output and activation function. It is initialized via orthogonal matching pursuit and optimized using back propagation algorithm with dual learning rate. The estimated position and amplitude can be obtained by the weights between layers. Through simulations and experiments, we demonstrate the superiority of HNN over traditional methods. © The Institution of Engineering & technology 2023.

关键词： Backpropagation

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DUAL BAND HRRP SCATTERING CENTER ESTIMATION VIA ATOMIC NORM MINIMIZATION

DUAL BAND HRRP SCATTERING CENTER ESTIMATION VIA ATOMIC NORM ...

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IET International Radar Conference 2023, IRC 2023

作者： Zhou, Na Zhang, Yu-Ang Wang, Yanhua Zhang, Liang School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Electromagnetic Sensing Research Center of CEMEE State Key Laboratory Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Advanced Technology Research Institute Beijing Institute of Technology Shandong250300 China

ISBN: (纸本)9781839539954

High resolution range profile (HRRP) obtained through air-to-ground radar provides information on the target structure, which is widely used in military and aviation fields. On the other side, dual band radar is able to improve the performance of target detection and recognition by fusing the information of different band. In this paper, we propose a dual band HRRP scattering center estimation method based on atomic norm minimization (ANM). Due to the fact that dual band information reflects the characteristics of the same target, it is modelled as the multiple measurement vector (MMV) and incorporated into the modelling process to effectively utilize the correlation of dual band information and sparse reconstruction. This method estimates in a continuous domain, avoiding the problem of base mismatch. According to the experimental results, this method exhibits outstanding advantages in accuracy and robustness. © The Institution of Engineering & technology 2023.

关键词： Radar target recognition

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RADAR TARGET HRRP SEGMENTATION USING DIFFUSION MODEL

RADAR TARGET HRRP SEGMENTATION USING DIFFUSION MODEL

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IET International Radar Conference 2023, IRC 2023

作者： Liu, Jiaqi Yuan, Mingchen Zhang, Liang Wang, Yanhua School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Electromagnetic Sensing Research Center of CEMEE State Key Laboratory Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Advanced Technology Research Institute Beijing Institute of Technology Shandong250300 China

ISBN: (纸本)9781839539954

High-resolution range profile (HRRP) formed by the extended target under high-resolution radar can reflect the detailed information of the target, which is one of the important means of target detection for air-to-ground radar. Accurately segmenting the target HRRP from the entire echo is of great significance for studying the target characteristics, target feature extraction and recognition. In this paper, a radar target HRRP segmentation algorithm using diffusion model is proposed. The disturbed position parameters of the target HRRP are generated according to the idea of adding noise by the diffusion model, and then the predicted segmentation results are obtained through the trained model to modify the disturbed parameters. The performance of the algorithm under different signal-to-noise ratios and polarization modes is evaluated in a measured dataset. The experimental results show that this method can effectively obtain target HRRPs and can be applied to the extended target segmentation of high-resolution radar. © The Institution of Engineering & technology 2023.

关键词： Signal to noise ratio

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