Table of Content

10 October 2022, Volume 33 Issue 19

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Development Status and Prospect of Key Rail Grinding Equipment and Technology of Grinding Stone

ZHANG Wulin, FANXiaoqiang, ZHU Minhao, DUAN Haitao

2022, 33(19):  2269-2287.  DOI: 10.3969/j.issn.1004-132X.2022.19.001

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 The rail grinding strategies and techniques were reviewed， and the key equipment of the grinding methods using grinding wheels（active grinding and high-speed passive grinding）and the milling and grinding compound process were summarized. Meanwhile， the research status of rail grinding stone（grinding wheel， equipped with rail grinding train and used for cutting tools）were reviewed from ingredients， forming processes， structural design and grinding performance evaluation criterions， etc. It was concluded that the fine formula design, scientific evaluation criterions, etc. were the main challenges for the development of high-performance grinding stones. Finally， it was pointed out that the greenization， standardization， and intelligence were the important directions for future development of grinding stone technology. 

Study on Crack Repair Performance of Si3N4/TiC/ZrSi2 Ceramic Tool Materials

ZHANG Shuai, CHEN Zhaoqiang, XIAO Guangchun, YI Mingdong, ZHANG Jingjie, ZHOU Tingting, XU Chonghai

2022, 33(19):  2288-2297.  DOI: 10.3969/j.issn.1004-132X.2022.19.002

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To solve the problems that ceramic tools were sensitive to cracks， ZrSi2 crack repairing agent was added into Si3N4/TiC ceramic tool material matrix， which made ceramic tools have crack repairing ability. When ZrSi2 content was as 10% （volumn fraction）， Si3N4/TiC/ZrSi2 ceramic tool materials had better comprehensive properties， with flexural strength of 815 MPa， fracture toughness of 8.06 MPa·m1/2， Vickers hardness of 15.91 GPa and relative density of 99.72%. Cracks were introduced into the surface of ceramic tool materials by Vickers hardness tester， and the optimal repair parameters and repair mechanism of cracks in Si3N4/TiC-based ceramic materials with ZrSi2 as repair agent were studied. The research shows that the flexural strength of Si3N4/TiC/ZrSi2 prefabricated crack specimen with 10% ZrSi2 added may be restored to 92.02% of that of smooth specimen after heat treatment at 800 ℃ for 60 min in air atmosphere， and the crack with the surface length of 300～350 μm is completely repaired， and an oxide layer mainly composed of ZrO2 and SiO2 is formed on the surface of the specimen. The mechanism of crack repair of ceramic cutting tool materials is analyzed as follows：in the air heat treatment processes， ZrSi2， a healing agent at the crack and the surface of the material， preferentially reacts with oxygen to generate ZrO2 and SiO2， which heal the crack on the surface of the material and restore the strength of the crack sample. At the same time， during cutting， ZrO2 and SiO2， oxidation products of ZrSi2 at the cracks and material surfaces， may repair the crack in time and improve the wear resistance of the tool. 

Metal Defect Image Recognition Method Based on Shallow CNN Fusion Transformer

TANG Donglin, YANG Zhou, CHENG Heng, LIU Mingxuan, ZHOU Li, DING Chao

2022, 33(19):  2298-2305，2316.  DOI: 10.3969/j.issn.1004-132X.2022.19.003

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Aiming at the problems of large amount of parameters and calculation in the field of metal defect recognition， a metal defect recognition method was proposed based on shallow CNN and Transformer model. The shallow CNN was used to learn the local information and position information of the images， and the Transformer was used to learn the global information of the images. At the same time， the channel attention module SE was introduced to pay attention to the important feature channels to realize the defect image recognition. The effectiveness of this method was verified by introducing the open defect data set， and the universality of this method was verified by using the self-built defect ultrasonic data set. The experimental results show that the proposed method has strong universality and may effectively identify metal defect images on small and medium-sized data sets. 

Study on Leakage Characteristics and Dynamic Characteristics of New Type of Vortex Groove Seals

JIANG Jinyu, SUN Dan, ZHAO Huan, ZHANG Guochen, LI Yu, WEN Shuaifang

2022, 33(19):  2306-2316.  DOI: 10.3969/j.issn.1004-132X.2022.19.004

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A new seal structure with vortex groove in the circumferential direction of the sealing inlet was proposed. The theoretical models of traditional labyrinth seal and the new type of vortex groove seal leakage characteristics and dynamic characteristics were established. Based on the model accuracy verification by means of experiments， the leakage characteristics and dynamic characteristics of the traditional labyrinth seal and the new type of vortex groove seal  were compared and analyzed under different inlet/outlet pressure ratios and pre-whirl ratios. The influence mechanism of the new vertex groove structure on the seal leakage and dynamic characteristics was studied. The results show that with the increase of the number of vortex groove， the leakage of vortex groove  seal decreases gradually. Under the same pressure ratio， the difference between the leakage of the new seal with different number of vortex grooves increases with the increase of the pressure ratio. When the pressure ratio is as 6， compared with the traditional labyrinth seal， the leakage of the 64 vortex grooves seal is reduced by 3.37%. Under the conditions of high pre-whirl ratio， the tangential airflow force of the seal with different number of vortex grooves is opposite to the direction of rotor vortex， which plays a role in inhibiting rotor vortex， the tangential airflow force increases with the increase of the number of vortex grooves. With the increase of rotor vortex frequency， the cross stiffness of seals with three different number of vortex grooves first decreases to negative value and then increases to positive value. The effective damping of the vortex groove seals is higher than that of the traditional labyrinth seals. The new vortex groove seal may improve the stability of the rotor systems. 

Research on Accelerated Life Test Method of Harmonic Reducers

WANG Qiao, DU Xuesong, SONG Chaosheng, ZHU Caichao, SUN Jianquan, LIAO Delin

2022, 33(19):  2317-2324.  DOI: 10.3969/j.issn.1004-132X.2022.19.005

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At present， there was an urgent need for an accelerated life test method of harmonic reducer in engineering to replace the full life test in order to reduce the test cycles and costs. Based on the principles of accelerated life test， a constant stress accelerated life test program  was proposed based on the failure characteristics of the harmonic reducer. The flexible wheel that was most likely to fail in the harmonic reducers was taken as the object， and the definite failure judgment， acceleration stress， acceleration factor， method of accelerating model were pointed out. In the small sample test data processing， a more accurate Weibull distribution parameter estimation was obtained by using the maximum likelihood method and Markov Monte Carlo method. The experimental results of the prototype show that the scheme may effectively describe the life of the harmonic reducers. The reliability evaluation standard of the basic rated life is 62% higher than that of the evaluation standard of the median life. The life index of the harmonic reducers was effectively evaluated. The research results have certain engineering application values. 

Based on Machine Learning Intelligent Design and Properties Research of Large Sectional High Strength Martensite Steels for Petroleum Equipment

LI Fangpo, LU Caihong, ZHAO Jingxiao, LI Xiucheng, SHANG Chengjia

2022, 33(19):  2325-2330.  DOI: 10.3969/j.issn.1004-132X.2022.19.006

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In order to develop new materials and meet the requirements of ultra-deep petroleum and gas development, four prediction models for composition-yield strength and composition-hardness of large cross-sectional martensitic steel with the highest strength grade for equipment components were established based on machine learning and composition performance big data herein. The results show that the artificial neural network model with 4 layers of neurons and 64 layers depth has the best fitting degree for property predicting, and two optimized martensitic steels chemical composition design with yield strength greater than 1100 MPa, hardness greater than 42HRC and carbon content less than 0.22% are formed based on genetic algorithm. The experimental results show that the hardening distribution curve of designed materials is basically consistent with the predicted values, and the maximum error is less than 3HRC. According to the optimized composition, 35 batches of products were manufactured and testing results show that the materials may meet the performance requirements of 150 mm cross-sectional drilling rig components. More than 95% uniform fine acicular martensite may be obtained in the full cross-sections, yield strength is greater than 1100MPa and impact absorbed energy is greater than 45 J which meets the service requirements of petroleum equipment. The results of prediction performance are consistent with that of experiments. Material big data is combined with machine learning, which provides a new way for developing high performance petroleum equipment materials.

Scale Optimization of Humanoid Robotic Arms Based on Kinematic Performance Analysis

SUN Peng, LI Yanbiao, ZHANG Cong, SHAN Xiaohang, YUE Yi, WEI Baochen

2022, 33(19):  2331-2340.  DOI: 10.3969/j.issn.1004-132X.2022.19.007

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 Aiming at the problems that the coupling effect of multi-joint series mechanisms on the kinematic performance of the end-effector， the kinematic characteristics of series-parallel hybrid mechanisms were analyzed， and a multi-parameter plane model was proposed to realize visualized design. Based on screw theory， the kinematics transfer matrices of serial and parallel joints were established respectively， and the corresponding performance indexes were set up. According to the multi-parameter plane model， the coupling relationship among parameter scale， orientation workspace， and performance indexes were further revealed through a three-dimensional visualization graphics. Finally， the values of parameters， the range of high-quality orientation workspace， and the distribution of indexes were confirmed. The results show that the global performance indexes of the shoulder and wrist joints are as 0.78 and 0.67， respectively， and the global condition number of the elbow joint is as 2.38. The hybrid configuration has functions similar to that of the human arm joints， and gives full play to the comprehensive advantages of series and parallel mechanisms. 

Multi-dimensional Dynamic Bayesian Network and Its Importance Measure Analysis Method

CHEN Dongning, HU Yanlong, YAO Chengyu, WANG Kuantong, MA Lei,

2022, 33(19):  2340-2346.  DOI: 10.3969/j.issn.1004-132X.2022.19.008

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The Bayesian network analysis method was an important method of reliability analysis, but the traditional Bayesian network analysis method was limited to analyze the influences of single factors, and there was a large analysis deviation when the system reliability was affected by multiple factors. Therefore, a multi-dimensional dynamic Bayesian network analysis method was proposed, which used unit step function and impulse function to construct Bayesian network time continuity, and established the failure probability distribution function when the root node was affected by multiple factors. Then, a multi-dimensional dynamic Bayesian network importance measure analysis method was proposed by expanding the traditional importance measure analysis method. Through the engineering example analyses of the hydraulic system of the bucket wheel machine tensioning mechanisms, and compared with the analysis results of the discrete-time Bayesian network analysis method, the feasibility and superiority of the multi-dimensional dynamic Bayesian network and its importance measure analysis method were verified, which provides a more accurate quantitative basis for system improvement and weak link identification.

Intelligent Fault Diagnosis of Bearings with Few Samples Based on an Improved Convolutional Generative Adversarial Network

GUO Wei, XING Xiaosong

2022, 33(19):  2347-2355.  DOI: 10.3969/j.issn.1004-132X.2022.19.009

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 Few bearing samples usually led to inadequate learning and low diagnosis accuracy. To solve this problem， an improved convolutional generative adversarial network was constructed. It made full use of data generation ability of the GAN and learning ability of deep CNN， so that the intelligent fault diagnosis might be conducted for the bearings with few samples under varying working conditions. First， a deep convolutional GAN was constructed. The deep features in few real data were mined through adversarial learning between the generator and discriminator of GAN， and then the generator might generate simulated data exactly like real one to make up for the lack of very few samples. Then， the dense block and dilated convolutions were combined with the CNN， named as DDCNN， to improve the learning ability by extending the network depth and perception range. As a result， the DDCNN may identify tiny differences in multi-class datasets and enhance feature extraction. Finally， the proposed method was verified by using bearing datasets with few samples under fixed conditions and varying rotating speeds， and was compared with other frameworks. The experimental results indicate that DDCNN has higher diagnosis accuracy for bearings with few samples and noisy conditions. 

Blind Deconvolution Based on Reweighted-kurtosis Maximization for Wind Turbine Fault Diagnosis

WU Lei, WANG Jiaxu, ZHANG Xin, LIU Zhiwen

2022, 33(19):  2356-2363.  DOI: 10.3969/j.issn.1004-132X.2022.19.010

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Due to influences of noise and complex transmission paths， the wind turbine gear fault signatures were generally weak. To effectively diagnose the gear faults， a new blind deconvolution method  was proposed based on reweighted-kurtosis maximization. The reweighted-kurtosis possessed great robustness to single or few strong impulse interferences and did not require any prior knowledge of the fault impulse train to be restored. The proposed deconvolution method may effectively solve the problems that the classical kurtosis maximization-based methods tend to restore a single dominant impulse rather than the gear fault impulse train. At the same time， the proposed method has stronger applicability in gear fault diagnosis for industrial equipment in comparison with the common non-fully “blind” methods（relying on the prior knowledge of the fault characteristic frequency）. The analysis results of the simulated signals show that the proposed method is effective in restoring fault impulse trains. The applications in wind turbine fault diagnosis demonstrate the effectiveness of the proposed method for gear fault diagnosis. 

Study on Mechanics Properties and Energy Absorption of Gradient Porous Scaffolds Fabricated by SLM

ZENG Shoujin, LI Chuansheng, LIU Guang, XU Mingsan, LI Dichen

2022, 33(19):  2364-2371.  DOI: 10.3969/j.issn.1004-132X.2022.19.011

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Gradient Ti6Al4V porous scaffolds were designed based on triply periodic minimal surfaces（TPMS） and were fabricated by SLM technology. The effects of edge porosity Pout， center porosity Pin and average porosity P- on mechanics properties and energy absorption were analyzed. The results show that elastic modulus， compressive strength and total absorbed energy per unit volume WV of gradient porous scaffolds with the same P- all decrease with the increase of Pout and increase with the increase of Pin. The elastic modulus， compressive strength and WV of gradient porous scaffolds with the different P- all decrease with the increase of P-. P- has greater influence on elastic modulus， compressive strength and WV than Pin and Pout. The printed gradient porous scaffolds meet the requirements of elastic modulus and compressive strength of femur and tibia. The mechanics property model established may provide a reference for the applications of porous scaffolds in orthopedics. 

Strength-constrainted Topology Optimization Based on Additive Manufacturing Anisotropy

HE Zhicheng, YANG Dingding, JIANG Chao, WU Yi, JIANG Hexin

2022, 33(19):  2372-2380，2393.  DOI: 10.3969/j.issn.1004-132X.2022.19.012

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The particularity of additive manufacturing processes leds to the fact that the manufacturing structure exhibited anisotropic mechanics properties. In order to meet the more stringent structural strength design requirements， atopology optimization strategy considering anisotropic strength constraints was proposed based on bi-directional evolutionary structural optimization. The anisotropic Tsai-Hill failure coefficient evaluating additive structural strength was derived， and an objective function containing the constraint of failure coefficient was established by the scale factor. The sensitivity numbers were analyzed in detail， moreover， numerical methods such as sensitivity normalization were used to stabilize the optimization processes. It shows that the proposed method effectively suppresses the high failure risk area， thus， ensures the structural strength， and may obtain better results than that of von-Mesis stress-dependent design under material anisotropic strength assumption. In addition， the optimization results are deeply rely on the variation of anisotropy and the material stacking angle parameters， therefore reasonable tuning will help to optimize structural properties. 

Vehicle High-precision Positioning Technique Based on Multi-sensors Information Fusion

SHI Xiaobo, ZHAO Dingxuan, KONG Zhifei, NI Tao, ZHAO Xiaolong, GUO Qinghe

2022, 33(19):  2381-2387.  DOI: 10.3969/j.issn.1004-132X.2022.19.013

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 In order to solve the positioning error problems of multi-axle emergency rescue vehicles driving in complex environments， a combined positioning system was designed based on multi-information fusion. The positioning system was mainly composed of a front-end odometer and a back-end optimization model. The three-axis vehicle kinematics modeling was completed， the inertial measurement unit-based kinematics prediction equation and global positional system-based kinematics observation equation were derived， the state was updated through the Kalman filter， and the construction of the front-end odometer was completed. The external reference calibration tool was designed based on iterative closest point matching algorithm， and a feature point cloud matching model  was constructed to complete the back-end model optimization. Finally， after simulation and experiments were carried out， the results show that the positioning errors of the system are less than 3.45 cm， the angle errors are less than 0.10 degrees， the accuracy and stability are greatly improved. 

Numerical Simulation on Mounted Bolt Failures in Vehicle Subframe 

LI Wei, ZHENG Hao, LIU Yanmei, FAN Song

2022, 33(19):  2388-2393.  DOI: 10.3969/j.issn.1004-132X.2022.19.014

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A high-strength mounted bolt（10.9， M14）was taken as the research object， 3 bolt strength tests were designed for different loading status. The fracture strain under different stresses triaxiality was calibrated using finite element simulation and parameter reverse methods and CrachFEM shear failure model was constructed for bolt material. The shear fracture strength was studied for variable diameters of mounted bolt during tests and simulation for subframe， and it provides the stable simulation method for mounted bolt design for subframe to satisfy the crash requirements. The correlation work also demonstrates that CrachFEM model may better predict the fracture failure behavior of high-strength mounted bolts.