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Tool condition monitoring plays a significant role in tool pre-diagnosis and health management system. In this paper, wear experiments collected multi-sensor signals during the processing of milling tools, including: vibration, cutting force and acoustic emission signals, which performed multi-domain feature extraction in time, frequency and time-frequency domains. Correlation analysis was utilized to select the optimal features. Principal component analysis was used for feature dimensionality reduction. The reduced-dimensional features were input into backpropagation neural network, support vector regression and radial basis function neural network models, which were proposed to establish the prediction model between multi-sensor fusion features and tool wear. Through comparative analysis with the prediction model based on single sensor signal, the experimental results presented that the proposed method based on multi-sensor feature fusion had comprehensive advantages in prediction accuracy and stability. Multi-sensor fusion technology has certain guiding significance and reference value for the online monitoring research of tool wear.
Zhaopeng He; Tielin Shi. Multi-sensor Fusion Technology and Machine Learning Methods for Milling Tool Wear Prediction. Advances in Intelligent Automation and Soft Computing 2021, 602 -610.
AMA StyleZhaopeng He, Tielin Shi. Multi-sensor Fusion Technology and Machine Learning Methods for Milling Tool Wear Prediction. Advances in Intelligent Automation and Soft Computing. 2021; ():602-610.
Chicago/Turabian StyleZhaopeng He; Tielin Shi. 2021. "Multi-sensor Fusion Technology and Machine Learning Methods for Milling Tool Wear Prediction." Advances in Intelligent Automation and Soft Computing , no. : 602-610.
Tool condition monitoring is an important part of tool prediagnosis and health management systems. Accurate prediction of tool wear is greatly important for making full use of tool life, improving the production efficiency and product quality, and reducing tool costs. In this paper, an intelligent cutting tool embedded with a thin-film thermocouple collected the temperature signals during the tool life cycle. As a deep learning method, stacked sparse autoencoders model with a backpropagation neural network for regression was proposed to predict tool wear based on raw temperature signals. An improved loss function with sparse and weight penalty terms was used to enhance the robustness and generalizability of the stacked sparse autoencoders model. To confirm the superiority of the proposed model, the predictive performance was compared with that of traditional machine learning methods, such as backpropagation neural network and support vector regression with manual features extraction. The root mean square error and coefficient of determination were calculated to evaluate the prediction model. The experimental results showed that the proposed model outperformed the traditional methods with a higher prediction accuracy and better prediction stability. The feasibility and accuracy of temperature signals for tool wear prediction were also confirmed.
Zhaopeng He; Tielin Shi; Jianping Xuan; Tianxiang Li. Research on tool wear prediction based on temperature signals and deep learning. Wear 2021, 478-479, 203902 .
AMA StyleZhaopeng He, Tielin Shi, Jianping Xuan, Tianxiang Li. Research on tool wear prediction based on temperature signals and deep learning. Wear. 2021; 478-479 ():203902.
Chicago/Turabian StyleZhaopeng He; Tielin Shi; Jianping Xuan; Tianxiang Li. 2021. "Research on tool wear prediction based on temperature signals and deep learning." Wear 478-479, no. : 203902.
Carbon-coated silicon nanotube ([email protected]) anodes show tremendous potential in high-performance lithium ion batteries (LIBs). Unfortunately, to realize the commercial application, it is still required to further optimize the structural design for better durability and safety. Here, the electrochemical and mechanical evolution in lithiated [email protected] nanohybrids are investigated using large-scale atomistic simulations. More importantly, the lithiation responses of [email protected] nanohybrids are also investigated in the same simulation conditions as references. The simulations quantitatively reveal that the inner hole of the SiNT alleviates the compressive stress concentration between a-Li x Si and C phases, resulting in the [email protected] having a higher Li capacity and faster lithiation rate than [email protected] The contact mode significantly regulates the stress distribution at the inner hole surface, further affecting the morphological evolution and structural stability. The inner hole of bare SiNT shows good structural stability due to no stress concentration, while that of concentric [email protected] undergoes dramatic shrinkage due to compressive stress concentration, and that of eccentric [email protected] is deformed due to the mismatch of stress distribution. These findings not only enrich the atomic understanding of the electrochemical–mechanical coupled mechanism in lithiated [email protected] nanohybrids but also provide feasible solutions to optimize the charging strategy and tune the nanostructure of SiNT-based electrode materials.
Chen Feng; Shiyuan Liu; Junjie Li; Maoyuan Li; Siyi Cheng; Chen Chen; Tielin Shi; Zirong Tang. Molecular Understanding of Electrochemical–Mechanical Responses in Carbon-Coated Silicon Nanotubes during Lithiation. Nanomaterials 2021, 11, 564 .
AMA StyleChen Feng, Shiyuan Liu, Junjie Li, Maoyuan Li, Siyi Cheng, Chen Chen, Tielin Shi, Zirong Tang. Molecular Understanding of Electrochemical–Mechanical Responses in Carbon-Coated Silicon Nanotubes during Lithiation. Nanomaterials. 2021; 11 (3):564.
Chicago/Turabian StyleChen Feng; Shiyuan Liu; Junjie Li; Maoyuan Li; Siyi Cheng; Chen Chen; Tielin Shi; Zirong Tang. 2021. "Molecular Understanding of Electrochemical–Mechanical Responses in Carbon-Coated Silicon Nanotubes during Lithiation." Nanomaterials 11, no. 3: 564.
Deep residual network (DRN) is one of the state-of-the-art deep learning models in data-driven fault diagnosis field. Their especially deep architectures give them sufficient capacity to deal with very complex diagnosis issues. However, a neural network with excellent performance usually requires hundreds of thousands parameters, which is unaffordable for current industrial machines due to their limited computational resources. To enable the practical application of fault diagnosis, developing deep learning methods that have both powerful performance and economical computation burden is necessary. This study proposes a novel bearing fault diagnosis method based on the wavelet packet transform (WPT) and a lightweight variant of DRN called multi branch deep residual network (MB-DRN) to resolve the above issues. WPT is utilized to map raw signals into time-frequency domain, from which the MB-DRN can extract a set of robust features more easily. And MB-DRN builds several small-sized convolutional layer branches in each building block to increase the network non-linearity, the construction of layer branches can be achieved freely and this design strategy largely saves the parameter usage while approaches a stronger model's capacity. Two rolling bearing datasets with variable operating conditions were conducted on the proposed method to validate performance. The results verify the necessity of WPT based data processing method and show that MB-DRN can outperform the accuracies of standard DRN with only one fourths of parameter amount, revealing significant potential of the proposed method for realistic industrial fault diagnosis applications.
Shoucong Xiong; Hongdi Zhou; Shuai He; Leilei Zhang; Tielin Shi. Fault diagnosis of a rolling bearing based on the wavelet packet transform and a deep residual network with lightweight multi-branch structure. Measurement Science and Technology 2021, 32, 085106 .
AMA StyleShoucong Xiong, Hongdi Zhou, Shuai He, Leilei Zhang, Tielin Shi. Fault diagnosis of a rolling bearing based on the wavelet packet transform and a deep residual network with lightweight multi-branch structure. Measurement Science and Technology. 2021; 32 (8):085106.
Chicago/Turabian StyleShoucong Xiong; Hongdi Zhou; Shuai He; Leilei Zhang; Tielin Shi. 2021. "Fault diagnosis of a rolling bearing based on the wavelet packet transform and a deep residual network with lightweight multi-branch structure." Measurement Science and Technology 32, no. 8: 085106.
Spiral tool path generation methods for the ultra-precision diamond turning of freeform surfaces can significantly affect machining efficiency and surface quality. However, effective methods for freeform surfaces with a high slope, especially nonrotation symmetric surfaces and those determined by the recursive formula, are unavailable. Therefore, a new spiral tool path generation method based on a nonuniform rational B-spline (NURBS) parameter space is proposed for the ultra-precision diamond turning of freeform surfaces. The discrete points are used for NURBS global interpolation by discretizing the surface in a radial Cartesian coordinate system, thereby forming a NURBS surface. A more uniform spiral tool path of freeform surfaces with a higher calculation efficiency can be obtained by directly utilizing the parameters on the NURBS surface than by applying existing methods. In addition, a solution is provided for freeform surfaces determined by the recursive formula. Simulation and experiments are conducted to show the effectiveness and adaptability of our method.
Shuai He; Jianping Xuan; Wenhao Du; Qi Xia; Shoucong Xiong; Leilei Zhang; Yinfeng Wang; Jinzhou Wu; Hongfei Tao; Tielin Shi. Spiral tool path generation method in a NURBS parameter space for the ultra-precision diamond turning of freeform surfaces. Journal of Manufacturing Processes 2020, 60, 340 -355.
AMA StyleShuai He, Jianping Xuan, Wenhao Du, Qi Xia, Shoucong Xiong, Leilei Zhang, Yinfeng Wang, Jinzhou Wu, Hongfei Tao, Tielin Shi. Spiral tool path generation method in a NURBS parameter space for the ultra-precision diamond turning of freeform surfaces. Journal of Manufacturing Processes. 2020; 60 ():340-355.
Chicago/Turabian StyleShuai He; Jianping Xuan; Wenhao Du; Qi Xia; Shoucong Xiong; Leilei Zhang; Yinfeng Wang; Jinzhou Wu; Hongfei Tao; Tielin Shi. 2020. "Spiral tool path generation method in a NURBS parameter space for the ultra-precision diamond turning of freeform surfaces." Journal of Manufacturing Processes 60, no. : 340-355.
Slippery liquid-infused porous surfaces (SLIPSs) have drawn tremendous attentions due to their excellent self-cleaning and anti-icing properties, where durability becomes one of most concerned issues. Here, we demonstrate a robust lubricant-infused surface with enhanced durability by introducing micro pyramidal holes and porous nanostructures. The porous nanostructures serve to retain the lubricant whilst the robust micro pyramidal holes provide protection for the nanostructures. SLIPSs with micro pyramidal holes (P-SLIPSs) are achieved via anisotropic etching and spraying superhydrophobic coatings. The sliding angle of P-SLIPS is as low as 1.5°, enabling liquid droplets including ink, milk and glycerol to slide easily on the surface. The ice adhesion strength of the P-SLIPS is about 11.5 kPa, similar to that of the nanostructured SLIPS without micro pyramidal holes but much lower than that of glass, Al alloy, silicon and superhydrophobic surfaces. After 20 icing/deicing cycles, the ice adhesion strength of the P-SLIPS is significantly lower than that of the nanostructured SLIPS. This is mainly attributed to the introduction of the robust micro pyramidal holes, which can protect the nanostructures from damaging during the icing/deicing process. Our work provides a feasible approach to fabricate robust lubricant-infused surfaces with excellent durability for anti-icing and self-cleaning.
Xianhua Tan; Yuzhou Zhang; Xingyue Liu; Shuang Xi; Zhenyu Yan; Zhiyong Liu; Tielin Shi; Guanglan Liao. Employing micro pyramidal holes and porous nanostructures for enhancing the durability of lubricant-infused surfaces in anti-icing. Surface and Coatings Technology 2020, 405, 126568 .
AMA StyleXianhua Tan, Yuzhou Zhang, Xingyue Liu, Shuang Xi, Zhenyu Yan, Zhiyong Liu, Tielin Shi, Guanglan Liao. Employing micro pyramidal holes and porous nanostructures for enhancing the durability of lubricant-infused surfaces in anti-icing. Surface and Coatings Technology. 2020; 405 ():126568.
Chicago/Turabian StyleXianhua Tan; Yuzhou Zhang; Xingyue Liu; Shuang Xi; Zhenyu Yan; Zhiyong Liu; Tielin Shi; Guanglan Liao. 2020. "Employing micro pyramidal holes and porous nanostructures for enhancing the durability of lubricant-infused surfaces in anti-icing." Surface and Coatings Technology 405, no. : 126568.
Over the past few years, deep learning–based techniques have been extensively and successfully adopted in the field of fault diagnosis. As the diagnosis tasks become more complicated, the structure of the traditional convolutional neural network (CNN) has to become deeper to deal with them, while the gradient of fault features may vanish within the deep network. In addition, all the features are treated equally in the traditional CNN, which cannot make the most of the representation power of CNN. Here, we proposed a method named dual attention dense convolutional network to handle these issues, which is constructed by the dense network and the dual attention block. On one hand, the dense connections and concatenation layers can reinforce the propagation of fault features among layers and mitigate the vanishing gradient phenomenon in the deep network. On the other hand, as the features flow through the channel attention and spatial attention within the dual attention block, this attention mechanism can learn which feature to emphasize or suppress and then obtain the cross-channel and cross-spatial weights of the features. These weights can make the most of the abundant information, elevating the expressive power of network. After passing through these dense and attention blocks, the generated high-level features are then fed into the final classification layer to obtain diagnosis results. The effectiveness of the dual attention dense convolutional network is validated by eight datasets of spindle bearings under various machinery conditions. Compared with eight other approaches including support vector machines, random forest, and six existing deep learning models, the results indicate that the proposed dual attention dense convolutional network possesses higher accuracy, fewer parameters and computations, and faster convergence under complex operational conditions.
Su Jiang; Jianping Xuan; Jian Duan; Jianbin Lin; Hongfei Tao; Qi Xia; Ruizhen Jing; Shoucong Xiong; Tielin Shi. Dual attention dense convolutional network for intelligent fault diagnosis of spindle-rolling bearings. Journal of Vibration and Control 2020, 1 .
AMA StyleSu Jiang, Jianping Xuan, Jian Duan, Jianbin Lin, Hongfei Tao, Qi Xia, Ruizhen Jing, Shoucong Xiong, Tielin Shi. Dual attention dense convolutional network for intelligent fault diagnosis of spindle-rolling bearings. Journal of Vibration and Control. 2020; ():1.
Chicago/Turabian StyleSu Jiang; Jianping Xuan; Jian Duan; Jianbin Lin; Hongfei Tao; Qi Xia; Ruizhen Jing; Shoucong Xiong; Tielin Shi. 2020. "Dual attention dense convolutional network for intelligent fault diagnosis of spindle-rolling bearings." Journal of Vibration and Control , no. : 1.
Reliable data-driven tool condition monitoring (TCM) system is more and more promising for the cutdown on the machine downtime and economic losses. However, traditional methods aren't able to address machining big data because of low model generalization ability and labored hand-crafted features extraction operation. In this paper, a novel deep learning model, named multi-frequency-bands deep convolution neural network (MFB-DCNN), is proposed to handle machining big data and monitor tool condition. Firstly, samples are enlarged and three layer wavelet package decomposition is applied to obtain wavelet coefficients at different frequency bands. Then, multi-frequency-bands features extraction structure based on deep CNN structure is introduced and utilized for sensitive features extraction from these coefficients. The extracted features are fed into full connection layers to predict tool wear conditions. After that, milling experiments are conducted for signals acquisition and model construction. A series of hyperparameters selection experiments are designed for the proposed MFB-DCNN model optimization. Finally, prediction performance of typical models is evaluated and compared with the proposed model. The results show that the proposed model owns outstanding generalization ability and keeps higher prediction performance, and the well-designed structure can remedy the absence of complicated feature engineering.
Jian Duan; Jie Duan; Hongdi Zhou; Xiaobin Zhan; Tianxiang Li; Tielin Shi. Multi-frequency-band deep CNN model for tool wear prediction. Measurement Science and Technology 2020, 32, 065009 .
AMA StyleJian Duan, Jie Duan, Hongdi Zhou, Xiaobin Zhan, Tianxiang Li, Tielin Shi. Multi-frequency-band deep CNN model for tool wear prediction. Measurement Science and Technology. 2020; 32 (6):065009.
Chicago/Turabian StyleJian Duan; Jie Duan; Hongdi Zhou; Xiaobin Zhan; Tianxiang Li; Tielin Shi. 2020. "Multi-frequency-band deep CNN model for tool wear prediction." Measurement Science and Technology 32, no. 6: 065009.
Due to harsh environment in the cutting area, access to the cutting region is still limited for the existing temperature sensors. As such, measuring temperature signals remains a challenge for real-time tool wear monitoring. In this paper, a polycrystalline cubic boron nitride (PCBN) cutting tool embedded with thin-film thermocouple (TFTC) was devised and used for tool wear monitoring during the cutting of hardened AISI O2 tool steel. A detailed manufacturing process of the smart tool was introduced. Performance tests for the smart insert were evaluated and results showed that the embedded TFTC had a sensitivity of 14.4 μV/℃, good dynamic response, and functional validity. Wear experiments were implemented under two sets of cutting parameters to explore the relationship between cutting temperature and tool wear. The results showed that the increased maximum cutting temperature could well reflect the progression of flank wear. Obvious fluctuation in temperature signal could also well reflect the abnormal condition of the tool. The devised smart tool will be useful for determining optimum cutting parameters and extending tool life.
Tianxiang Li; Tielin Shi; Zirong Tang; Guanglan Liao; Jian Duan; Jinghui Han; Zhaopeng He. Real-time tool wear monitoring using thin-film thermocouple. Journal of Materials Processing Technology 2020, 288, 116901 .
AMA StyleTianxiang Li, Tielin Shi, Zirong Tang, Guanglan Liao, Jian Duan, Jinghui Han, Zhaopeng He. Real-time tool wear monitoring using thin-film thermocouple. Journal of Materials Processing Technology. 2020; 288 ():116901.
Chicago/Turabian StyleTianxiang Li; Tielin Shi; Zirong Tang; Guanglan Liao; Jian Duan; Jinghui Han; Zhaopeng He. 2020. "Real-time tool wear monitoring using thin-film thermocouple." Journal of Materials Processing Technology 288, no. : 116901.
Accidental failures of rotating machinery components such as rolling bearings may trigger the sudden breakdown of the whole manufacturing system, thus, fault diagnosis is vital in industry to avoid these massive economical costs and casualties. Since convolutional neural networks (CNN) are poor in extracting reliable features from original signal data, the time-frequency analysis method is usually called for to transform 1D signal into a 2D time-frequency coefficient matrix in which richer information could be exposed more easily. However, realistic fault diagnosis applications face a dilemma in that signal time-frequency analysis and fault classification cannot be implemented together, which means manual signal conversion work is also needed, which reduces the integrity and robustness of the fault diagnosis method. In this paper, a novel network named WPT-CNN is proposed for end-to-end intelligent fault diagnosis of rolling bearings. WPT-CNN creatively uses the standard deep neural network structure to realize the wavelet packet transform (WPT) time-frequency analysis function, which seamlessly integrates fault diagnosis domain knowledge into deep learning algorithms. The overall network architecture can be trained with gradient descent backpropagation algorithms, indicating that the time-frequency analysis module of WPT-CNN is also able to learn the dataset characteristics, adaptively representing signal information in the most suitable way. Two experimental rolling bearing fault datasets were used to validate the proposed method. Testing results showed that WPT-CNN obtained the testing accuracies of 99.73% and 99.89%, respectively, in two datasets, which exhibited a better and more reliable diagnosis performance than any other existing deep learning and machine learning methods.
Shoucong Xiong; Hongdi Zhou; Shuai He; Leilei Zhang; Qi Xia; Jianping Xuan; Tielin Shi. A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures. Sensors 2020, 20, 4965 .
AMA StyleShoucong Xiong, Hongdi Zhou, Shuai He, Leilei Zhang, Qi Xia, Jianping Xuan, Tielin Shi. A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures. Sensors. 2020; 20 (17):4965.
Chicago/Turabian StyleShoucong Xiong; Hongdi Zhou; Shuai He; Leilei Zhang; Qi Xia; Jianping Xuan; Tielin Shi. 2020. "A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures." Sensors 20, no. 17: 4965.
7075 aluminum alloy has been widely applied in the field of aerospace and marine sheet metal because of its protruding mechanical and corrosion resistance. In this paper, the problem of selecting optimal process parameters to optimize multiple processing variables had been studied in precision manufacturing. Multi-objective particle swarm optimized neural networks system was put forward to determine the optimal cutting conditions with multi-objective particle swarm algorithm and multiple neural networks as prediction models of machining variables. Precision parts manufacturing of 7075 aluminum alloy would go through two operations of material removal and surface forming. Firstly, optimal cutting conditions were determined to minimize tool wear while maximizing metal removal rate in material removal stage. Secondly, it was significant and meaningful to select optimal cutting conditions corresponding to the best surface quality and minimum root mean square of tool vibration in surface forming stage. Orthogonal experiments had been carried out to observe the relationship between machining-related variables and cutting parameters in detail. Multiple neural networks were trained to establish predictive models of cutting process from orthogonal experimental and statistical data. Maximum deviation theory sorted the Pareto solutions searched by optimization process of neural networks driven by multi-objective particle swarm algorithm. The top ranking Pareto solutions had been determined as the optimal cutting parameters combination for material removal and surface forming stages, respectively. Finally, the proposed optimization system can also be used to optimize the processing of other difficult-to-machine materials.
Zhaopeng He; Tielin Shi; Jianping Xuan; Su Jiang; Yinfeng Wang. A Study on Multivariable Optimization in Precision Manufacturing Using MOPSONNS. International Journal of Precision Engineering and Manufacturing 2020, 21, 2011 -2026.
AMA StyleZhaopeng He, Tielin Shi, Jianping Xuan, Su Jiang, Yinfeng Wang. A Study on Multivariable Optimization in Precision Manufacturing Using MOPSONNS. International Journal of Precision Engineering and Manufacturing. 2020; 21 (11):2011-2026.
Chicago/Turabian StyleZhaopeng He; Tielin Shi; Jianping Xuan; Su Jiang; Yinfeng Wang. 2020. "A Study on Multivariable Optimization in Precision Manufacturing Using MOPSONNS." International Journal of Precision Engineering and Manufacturing 21, no. 11: 2011-2026.
Modern machinery becomes more precious with the advance of science, and fault diagnosis is vital for avoiding economical losses or casualties. Among massive diagnosis methods, deep learning algorithms stand out to open an era of intelligent fault diagnosis. Deep residual networks are the state-of-the-art deep learning models which can continuously improve performance by deepening the network structures. However, in vibration-based fault diagnosis, the transient property instability of vibration signal usually calls for time–frequency analysis methods, and the characters of time–frequency matrices are distinct from standard images, which brings some natural limitations for the diagnosis performance of deep learning algorithms. To handle this issue, an enhanced deep residual network named the multilevel correlation stack-deep residual network is proposed in this article. Wavelet packet transform is used to preprocess the sensor signal, and then the proposed multilevel correlation stack-deep residual network uses kernels with different shapes to fully dig various kinds of useful information from any local regions of the processed input. Experiments on two rolling bearing datasets are carried out. Test results show that the multilevel correlation stack-deep residual network exhibits a more satisfactory classification performance than original deep residual networks and other similar methods, revealing significant potentials for realistic fault diagnosis applications.
Shoucong Xiong; Shuai He; Jianping Xuan; Qi Xia; Tielin Shi. Enhanced deep residual network with multilevel correlation information for fault diagnosis of rotating machinery. Journal of Vibration and Control 2020, 27, 1713 -1723.
AMA StyleShoucong Xiong, Shuai He, Jianping Xuan, Qi Xia, Tielin Shi. Enhanced deep residual network with multilevel correlation information for fault diagnosis of rotating machinery. Journal of Vibration and Control. 2020; 27 (15-16):1713-1723.
Chicago/Turabian StyleShoucong Xiong; Shuai He; Jianping Xuan; Qi Xia; Tielin Shi. 2020. "Enhanced deep residual network with multilevel correlation information for fault diagnosis of rotating machinery." Journal of Vibration and Control 27, no. 15-16: 1713-1723.
Metal nanoparticles (NPs) are promising bonding materials to replace Sn alloys in fine size Cu-Cu bonding. However, the method of rapidly patterning NPs on solder joints with sizes less than 30 µm is one of the main barriers that impede the practical applications of NPs in Cu-Cu bonding, especially in mass production. In this paper, a novel method of patterning Ag NPs on Cu pads by selective wetting was introduced. Cu pads with diameters down to 5 µm were coated with Ag NPs successfully. When sizes of Cu pads were larger than 10 μm, high density could be achieved and the ratio of diameters to pitches of Cu pads could reach 2/3. Furthermore, the thickness and the coverage of the Ag NPs layer could be raised by repeating coating. In the bonding test, the shear strength increased significantly with the increase of the bonding temperature and the bonding time. It could reach 22.92 MPa after sintering for 5 min at 250 ℃ under a bonding pressure of 20 MPa in N2. With the aforementioned advantages, patterning NPs by selective wetting will be one of the potential methods for applying NPs to Cu pads in Cu-NPs-Cu bonding.
Qi Liang; Junjie Li; Tianxiang Li; Guanglan Liao; Zirong Tang; Tielin Shi. Patterning Ag nanoparticles by selective wetting for fine size Cu-Ag-Cu bonding. Nanotechnology 2020, 31, 355302 .
AMA StyleQi Liang, Junjie Li, Tianxiang Li, Guanglan Liao, Zirong Tang, Tielin Shi. Patterning Ag nanoparticles by selective wetting for fine size Cu-Ag-Cu bonding. Nanotechnology. 2020; 31 (35):355302.
Chicago/Turabian StyleQi Liang; Junjie Li; Tianxiang Li; Guanglan Liao; Zirong Tang; Tielin Shi. 2020. "Patterning Ag nanoparticles by selective wetting for fine size Cu-Ag-Cu bonding." Nanotechnology 31, no. 35: 355302.
In the research area of conductive inks for printed electronics, the preparation of conductive circuits generally shows high requirements on the process temperature, process efficiency, electrical conductivity, foldability and so on. In this paper, a new type of Ag complex conductive ink modified by PVAc (poly vinyl acetate) was proposed to enhance the conductivity and foldability of rapidly sintered Ag patterns. The conductive ink was fabricated by mixing silver oxalate as Ag precursor, 1,2-Diaminopropane as complexing reagent, methanol and isopropanol as organic solvents, and a small amount of PVAc as binding agent. Compared to the Ag complex ink without PVAc, the modified ink shows a better sintered microstructure with higher flatness and density after fast sintering at 140 °C–200 °C for 2 min, indicating that the addition of PVAc has a positive effect on the stability of Ag nucleation and microstructure formation. A low resistivity of 5.17 μΩ cm and an improved foldable property can be achieved at 180 °C by sintering the PVAc modified Ag complex ink on PI (polyimide) substrate, which indicates that the proposed ink is promised to be applied in printed circuits and flexible electronics.
Junjie Li; Xiwen Zhang; Xingyue Liu; Qi Liang; Guanglan Liao; Zirong Tang; Tielin Shi. Conductivity and foldability enhancement of Ag patterns formed by PVAc modified Ag complex inks with low-temperature and rapid sintering. Materials & Design (1980-2015) 2019, 185, 108255 .
AMA StyleJunjie Li, Xiwen Zhang, Xingyue Liu, Qi Liang, Guanglan Liao, Zirong Tang, Tielin Shi. Conductivity and foldability enhancement of Ag patterns formed by PVAc modified Ag complex inks with low-temperature and rapid sintering. Materials & Design (1980-2015). 2019; 185 ():108255.
Chicago/Turabian StyleJunjie Li; Xiwen Zhang; Xingyue Liu; Qi Liang; Guanglan Liao; Zirong Tang; Tielin Shi. 2019. "Conductivity and foldability enhancement of Ag patterns formed by PVAc modified Ag complex inks with low-temperature and rapid sintering." Materials & Design (1980-2015) 185, no. : 108255.
Geometric errors remarkably affect the dimensional accuracy of parts manufactured by ultra-precision machining. It is vital to consider the workpiece shape for the identification of crucial error types. This research investigates the prioritization analysis of geometric errors for arbitrary curved surfaces by using random forest. By utilizing multi-body system (MBS) theory, a volumetric error model is initially established to calculate tool position errors. An error dataset, which contains information of 21 geometric errors, workpiece shape, and dimensional errors, is then constructed by discretizing the workpiece surface along the tool path. The problem of identifying crucial geometric errors is translated into another problem of feature selection by applying random forest on the error dataset. Moreover, the influence extent of each geometric error on the dimensional accuracy of four typical curved surfaces is analyzed through numerical simulation, and crucial geometric errors are identified based on the proposed method. Then, an iterative method of error compensation is proposed to verify the reasonability of the determined crucial geometric errors by specifically compensating them. Finally, under compensated and uncompensated conditions, two sinusoidal grid surfaces are machined on an ultra-precision lathe to validate the prioritization analysis method. Findings show that the machining accuracy of the sinusoidal grid surface with crucial geometric error compensation is better than that without compensation.
Hongfei Tao; Ran Chen; Jianping Xuan; Qi Xia; Zhongyuan Yang; Xin Zhang; Shuai He; Tielin Shi. Prioritization analysis and compensation of geometric errors for ultra-precision lathe based on the random forest methodology. Precision Engineering 2019, 61, 23 -40.
AMA StyleHongfei Tao, Ran Chen, Jianping Xuan, Qi Xia, Zhongyuan Yang, Xin Zhang, Shuai He, Tielin Shi. Prioritization analysis and compensation of geometric errors for ultra-precision lathe based on the random forest methodology. Precision Engineering. 2019; 61 ():23-40.
Chicago/Turabian StyleHongfei Tao; Ran Chen; Jianping Xuan; Qi Xia; Zhongyuan Yang; Xin Zhang; Shuai He; Tielin Shi. 2019. "Prioritization analysis and compensation of geometric errors for ultra-precision lathe based on the random forest methodology." Precision Engineering 61, no. : 23-40.
The tensile mechanical behaviors of axial torsional copper nanorods with the diameter of 5–6.5 nm are investigated systematically by molecular dynamics simulation. When increasing the angle of torsion loading, the initial stress gradually departures from the near-zero state, and the elastic modulus remains essentially constant. The tensile yield is closely related to the surface deformation reflected by the average potential energy of surface atoms (PeSurf). In spite of varied torsion loading, the PeSurf of nanorods are promoted to a similar critical level by the torsion and tension, and then fall abruptly indicating the nanorods yield. For the nanorods with [001] orientation in long axis, the rotation loading improves the PeSurf at the start of tension and makes dislocation nucleation occur easily, leading to the decline of tensile yield strength. For the [110] orientated nanorods, the PeSurf rise induced by the torsion is relatively small and quite close to the range size of the critical level, conducing to the insignificant fluctuation of the tensile yield stress. Meanwhile, lowering the temperature, enlarging the aspect ratio, and shrinking the size can lighten the yield stress descend of [001] orientated nanorods in different extents. At a constant temperature, the PeSurf differences between the PeSurf at yield moment and initial PeSurf without any loadings for all [001] orientated nanorods disperse in a narrow range, no matter how the aspect ratio and size change. This work contributes to understanding the mechanical properties and yield mechanisms of the nanorods under the torsion-tension combined loading.
Lian Xiao; Jiacheng Zhang; Yiying Zhu; Tielin Shi; Guanglan Liao. Molecular dynamics simulation of the tensile mechanical behaviors of axial torsional copper nanorod. Journal of Nanoparticle Research 2019, 21, 1 -14.
AMA StyleLian Xiao, Jiacheng Zhang, Yiying Zhu, Tielin Shi, Guanglan Liao. Molecular dynamics simulation of the tensile mechanical behaviors of axial torsional copper nanorod. Journal of Nanoparticle Research. 2019; 21 (8):1-14.
Chicago/Turabian StyleLian Xiao; Jiacheng Zhang; Yiying Zhu; Tielin Shi; Guanglan Liao. 2019. "Molecular dynamics simulation of the tensile mechanical behaviors of axial torsional copper nanorod." Journal of Nanoparticle Research 21, no. 8: 1-14.
Hybrid MoS2/reduced graphene aerogels with rich micro-pore are fabricated through a hydrothermal method, followed by freeze-drying and annealing treatment. The porous structure could act as an electrode directly, free of binder and conductive agent, which promotes an improved electron transfer, and provides a 3D network for an enhanced ion transport, thus leading to an increased capacity and stable long cycle stability performance. Notably, the specific capacity of MoS2/reduced graphene aerogel is 1041 mA h g−1 at 100 mA g−1. Moreover, reversible capacities of 667 mA h g−1 with 58.6% capacity retention are kept after 100 cycles. The outstanding performance is beneficial from the synergistic effect of the MoS2 nanostructure and graphene conductive network, as well as the binder-free design. These results provide a route to integrate transition-metal-dichalcogenides with graphene to fabricate composites with rich micro-pores and a three-dimensional network for energy storage devices.
Yan Zhong; Tielin Shi; Yuanyuan Huang; Siyi Cheng; Chen Chen; Guanglan Liao; Zirong Tang. Three-dimensional MoS2/Graphene Aerogel as Binder-free Electrode for Li-ion Battery. Nanoscale Research Letters 2019, 14, 1 -8.
AMA StyleYan Zhong, Tielin Shi, Yuanyuan Huang, Siyi Cheng, Chen Chen, Guanglan Liao, Zirong Tang. Three-dimensional MoS2/Graphene Aerogel as Binder-free Electrode for Li-ion Battery. Nanoscale Research Letters. 2019; 14 (1):1-8.
Chicago/Turabian StyleYan Zhong; Tielin Shi; Yuanyuan Huang; Siyi Cheng; Chen Chen; Guanglan Liao; Zirong Tang. 2019. "Three-dimensional MoS2/Graphene Aerogel as Binder-free Electrode for Li-ion Battery." Nanoscale Research Letters 14, no. 1: 1-8.
We propose a novel one-step exposure method for fabricating three-dimensional (3D) suspended structures, utilizing the diffraction of mask patterns with small line width. An optical model of the exposure process is built, and the 3D light intensity distribution in the photoresist is calculated based on Fresnel-Kirchhoff diffraction formulation. Several 3D suspended photoresist structures have been achieved, such as beams, meshes, word patterns, and multilayer structures. After the pyrolysis of SU-8 structures, suspended and free-standing 3D carbon structures are further obtained, which show great potential in the application of transparent electrode, semitransparent solar cells, and energy storage devices.
Xianhua Tan; Tielin Shi; Jianbin Lin; Bo Sun; Zirong Tang; Guanglan Liao. One-Step Mask-Based Diffraction Lithography for the Fabrication of 3D Suspended Structures. Nanoscale Research Letters 2018, 13, 394 .
AMA StyleXianhua Tan, Tielin Shi, Jianbin Lin, Bo Sun, Zirong Tang, Guanglan Liao. One-Step Mask-Based Diffraction Lithography for the Fabrication of 3D Suspended Structures. Nanoscale Research Letters. 2018; 13 (1):394.
Chicago/Turabian StyleXianhua Tan; Tielin Shi; Jianbin Lin; Bo Sun; Zirong Tang; Guanglan Liao. 2018. "One-Step Mask-Based Diffraction Lithography for the Fabrication of 3D Suspended Structures." Nanoscale Research Letters 13, no. 1: 394.
Three-dimensional (3D) measurement of microstructures has become increasingly important, and many microscopic measurement methods have been developed. For the dimension in several millimeters together with the accuracy at sub-pixel or sub-micron level, there is almost no effective measurement method now. Here we present a method combining the microscopic stereo measurement with the digital speckle projection. A microscopy experimental setup mainly composed of two telecentric cameras and an industrial projection module is established and a telecentric binocular stereo reconstruction procedure is carried out. The measurement accuracy has firstly been verified by performing 3D measurements of grid arrays at different locations and cylinder arrays with different height differences. Then two Mitutoyo step masters have been used for further verification. The experimental results show that the proposed method can obtain 3D information of the microstructure with a sub-pixel and even sub-micron measuring accuracy in millimeter scale.
Kepeng Chen; Tielin Shi; Qiang Liu; Zirong Tang; Guanglan Liao. Microscopic Three-Dimensional Measurement Based on Telecentric Stereo and Speckle Projection Methods. Sensors 2018, 18, 3882 .
AMA StyleKepeng Chen, Tielin Shi, Qiang Liu, Zirong Tang, Guanglan Liao. Microscopic Three-Dimensional Measurement Based on Telecentric Stereo and Speckle Projection Methods. Sensors. 2018; 18 (11):3882.
Chicago/Turabian StyleKepeng Chen; Tielin Shi; Qiang Liu; Zirong Tang; Guanglan Liao. 2018. "Microscopic Three-Dimensional Measurement Based on Telecentric Stereo and Speckle Projection Methods." Sensors 18, no. 11: 3882.
Narrowband amplitude demodulation is an effective tool for extracting characteristic features in fault diagnosis of rolling element bearings. The quality of demodulation largely depends on the frequency band selected for the demodulation. Numerous criteria have been constructed to determine the optimal frequency band. However, independent frequency interferences and in-band noises in narrowband signals can greatly affect the values of the criteria, which may lead to an inaccurate result in locating the optimal band, in demodulating fault features, and finally in the fault detection. Inspired by the nonlocal means (NL-means) denoising method that has been widely used in image processing, this paper proposes the narrowband envelope spectra fusion (NESF) method to enhance fault features and suppress in-band noises before criterion calculation. The method suppresses in-band noises by averaging envelope spectra at neighborhood narrow bands. Meanwhile, some minor improvements are made to conventional narrowband envelope spectrum calculation method to enhance the similarity of the narrowband envelope spectra containing fault features, and finally optimize the fusion process. Then sparsity values of these denoised envelope spectra, which can lower the impact of independent frequency interferences, are utilized to determine the optimal band and select the optimal envelope spectrum. Frequency signatures of the extracted envelope spectrum can be utilized to indicate the status and fault types of rolling element bearings. A simulated bearing fault signal and three real bearing fault signals are used to validate the effectiveness of the proposed method through comparison studies with protrugram and sparsogram. The results show that the proposed method can effectively extract fault characteristics even in a harsh environment.
Jie Duan; Tielin Shi; Jian Duan; Jianping Xuan; Yongxiang Zhang. A narrowband envelope spectra fusion method for fault diagnosis of rolling element bearings. Measurement Science and Technology 2018, 29, 125106 .
AMA StyleJie Duan, Tielin Shi, Jian Duan, Jianping Xuan, Yongxiang Zhang. A narrowband envelope spectra fusion method for fault diagnosis of rolling element bearings. Measurement Science and Technology. 2018; 29 (12):125106.
Chicago/Turabian StyleJie Duan; Tielin Shi; Jian Duan; Jianping Xuan; Yongxiang Zhang. 2018. "A narrowband envelope spectra fusion method for fault diagnosis of rolling element bearings." Measurement Science and Technology 29, no. 12: 125106.