2021年, 第34卷, 第3期　
刊出日期：2021-06-16

  

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Special Issue on AI-Enabled Monitoring Diagnosis & Prognosis
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  AI-Enabled Monitoring, Diagnosis & Prognosis

  Ruqiang Yan, Xuefeng Chen, Weihua Li, Robert X. Gao

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 68-68. https://doi.org/10.1186/s10033-021-00589-w

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  Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review

  Zhibin Zhao, Jingyao Wu, Tianfu Li, Chuang Sun, Ruqiang Yan, Xuefeng Chen

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 56-56. https://doi.org/10.1186/s10033-021-00570-7

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  Prognostics and Health Management (PHM), including monitoring, diagnosis, prognosis, and health management, occupies an increasingly important position in reducing costly breakdowns and avoiding catastrophic accidents in modern industry. With the development of artificial intelligence (AI), especially deep learning (DL) approaches, the application of AI-enabled methods to monitor, diagnose and predict potential equipment malfunctions has gone through tremendous progress with verified success in both academia and industry. However, there is still a gap to cover monitoring, diagnosis, and prognosis based on AI-enabled methods, simultaneously, and the importance of an open source community, including open source datasets and codes, has not been fully emphasized. To fill this gap, this paper provides a systematic overview of the current development, common technologies, open source datasets, codes, and challenges of AI-enabled PHM methods from three aspects of monitoring, diagnosis, and prognosis.
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  Practical Options for Adopting Recurrent Neural Network and Its Variants on Remaining Useful Life Prediction

  Youdao Wang, Yifan Zhao, Sri Addepalli

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 69-69. https://doi.org/10.1186/s10033-021-00588-x

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  The remaining useful life (RUL) of a system is generally predicted by utilising the data collected from the sensors that continuously monitor different indicators. Recently, different deep learning (DL) techniques have been used for RUL prediction and achieved great success. Because the data is often time-sequential, recurrent neural network (RNN) has attracted significant interests due to its efficiency in dealing with such data. This paper systematically reviews RNN and its variants for RUL prediction, with a specific focus on understanding how different components (e.g., types of optimisers and activation functions) or parameters (e.g., sequence length, neuron quantities) affect their performance. After that, a case study using the well-studied NASA's C-MAPSS dataset is presented to quantitatively evaluate the influence of various state-of-the-art RNN structures on the RUL prediction performance. The result suggests that the variant methods usually perform better than the original RNN, and among which, Bi-directional Long Short-Term Memory generally has the best performance in terms of stability, precision and accuracy. Certain model structures may fail to produce valid RUL prediction result due to the gradient vanishing or gradient exploring problem if the parameters are not chosen appropriately. It is concluded that parameter tuning is a crucial step to achieve optimal prediction performance.
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  Deep Learning-Driven Data Curation and Model Interpretation for Smart Manufacturing

  Jianjing Zhang, Robert X. Gao

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 71-71. https://doi.org/10.1186/s10033-021-00587-y

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  Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments, smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art. While the widespread application of deep learning (DL) has opened up new opportunities to accomplish the goal, data quality and model interpretability have continued to present a roadblock for the widespread acceptance of DL for real-world applications. This has motivated research on two fronts: data curation, which aims to provide quality data as input for meaningful DL-based analysis, and model interpretation, which intends to reveal the physical reasoning underlying DL model outputs and promote trust from the users. This paper summarizes several key techniques in data curation where breakthroughs in data denoising, outlier detection, imputation, balancing, and semantic annotation have demonstrated the effectiveness in information extraction from noisy, incomplete, insufficient, and/or unannotated data. Also highlighted are model interpretation methods that address the pblack-boxq nature of DL towards model transparency.
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  Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

  Hosameldin O. A. Ahmed, Asoke K Nandi

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 37-37. https://doi.org/10.1186/s10033-021-00553-8

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  Roller bearing failure is one of the most common faults in rotating machines. Various techniques for bearing fault diagnosis based on faults feature extraction have been proposed. But feature extraction from fault signals requires expert prior information and human labour. Recently, deep learning algorithms have been applied extensively in the condition monitoring of rotating machines to learn features automatically from the input data. Given its robust performance in image recognition, the convolutional neural network (CNN) architecture has been widely used to learn automatically discriminative features from vibration images and classify health conditions. This paper proposes and evaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis. The first stage in the proposed method is to generate the RGB vibration images (RGBVIs) from the input vibration signals. To begin this process, first, the 1-D vibration signals were converted to 2-D grayscale vibration Images. Once the conversion was completed, the regions of interest (ROI) were found in the converted 2-D grayscale vibration images. Finally, to produce vibration images with more discriminative characteristics, an algorithm was applied to the 2-D grayscale vibration images to produce connected components-based RGB vibration images (RGBVIs) with sets of colours and texture features. In the second stage, with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and to classify bearing health conditions. Two cases of fault classification of rolling element bearings are used to validate the proposed method. Experimental results of this investigation demonstrate that RGBVI-CNN can generate advantageous health condition features from bearing vibration signals and classify the health conditions under different working loads with high accuracy. Moreover, several classification models trained using RGBVI-CNN offered high performance in the testing results of the overall classification accuracy, precision, recall, and F-score.
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  Dynamic Distribution Adaptation Based Transfer Network for Cross Domain Bearing Fault Diagnosis

  Yixiao Liao, Ruyi Huang, Jipu Li, Zhuyun Chen, Weihua Li

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 52-52. https://doi.org/10.1186/s10033-021-00566-3

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  In machinery fault diagnosis, labeled data are always difficult or even impossible to obtain. Transfer learning can leverage related fault diagnosis knowledge from fully labeled source domain to enhance the fault diagnosis performance in sparsely labeled or unlabeled target domain, which has been widely used for cross domain fault diagnosis. However, existing methods focus on either marginal distribution adaptation (MDA) or conditional distribution adaptation (CDA). In practice, marginal and conditional distributions discrepancies both have significant but different influences on the domain divergence. In this paper, a dynamic distribution adaptation based transfer network (DDATN) is proposed for cross domain bearing fault diagnosis. DDATN utilizes the proposed instance-weighted dynamic maximum mean discrepancy (IDMMD) for dynamic distribution adaptation (DDA), which can dynamically estimate the influences of marginal and conditional distribution and adapt target domain with source domain. The experimental evaluation on cross domain bearing fault diagnosis demonstrates that DDATN can outperformance the state-of-the-art cross domain fault diagnosis methods.
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  An Interpretable Denoising Layer for Neural Networks Based on Reproducing Kernel Hilbert Space and its Application in Machine Fault Diagnosis

  Baoxuan Zhao, Changming Cheng, Guowei Tu, Zhike Peng, Qingbo He, Guang Meng

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 44-44. https://doi.org/10.1186/s10033-021-00564-5

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  Deep learning algorithms based on neural networks make remarkable achievements in machine fault diagnosis, while the noise mixed in measured signals harms the prediction accuracy of networks. Existing denoising methods in neural networks, such as using complex network architectures and introducing sparse techniques, always suffer from the difficulty of estimating hyperparameters and the lack of physical interpretability. To address this issue, this paper proposes a novel interpretable denoising layer based on reproducing kernel Hilbert space (RKHS) as the first layer for standard neural networks, with the aim to combine the advantages of both traditional signal processing technology with physical interpretation and network modeling strategy with parameter adaption. By investigating the influencing mechanism of parameters on the regularization procedure in RKHS, the key parameter that dynamically controls the signal smoothness with low computational cost is selected as the only trainable parameter of the proposed layer. Besides, the forward and backward propagation algorithms of the designed layer are formulated to ensure that the selected parameter can be automatically updated together with other parameters in the neural network. Moreover, exponential and piecewise functions are introduced in the weight updating process to keep the trainable weight within a reasonable range and avoid the ill-conditioned problem. Experiment studies verify the effectiveness and compatibility of the proposed layer design method in intelligent fault diagnosis of machinery in noisy environments.
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  Deep Spatiotemporal Convolutional-Neural-Network-Based Remaining Useful Life Estimation of Bearings

  Xu Wang, Tianyang Wang, Anbo Ming, Qinkai Han, Fulei Chu, Wei Zhang, Aihua Li

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 62-62. https://doi.org/10.1186/s10033-021-00576-1

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  The remaining useful life (RUL) estimation of bearings is critical for ensuring the reliability of mechanical systems. Owing to the rapid development of deep learning methods, a multitude of data-driven RUL estimation approaches have been proposed recently. However, the following problems remain in existing methods: 1) Most network models use raw data or statistical features as input, which renders it difficult to extract complex fault-related information hidden in signals; 2) for current observations, the dependence between current states is emphasized, but their complex dependence on previous states is often disregarded; 3) the output of neural networks is directly used as the estimated RUL in most studies, resulting in extremely volatile prediction results that lack robustness. Hence, a novel prognostics approach is proposed based on a time-frequency representation (TFR) subsequence, three-dimensional convolutional neural network (3DCNN), and Gaussian process regression (GPR). The approach primarily comprises two aspects: construction of a health indicator (HI) using the TFR-subsequence-3DCNN model, and RUL estimation based on the GPR model. The raw signals of the bearings are converted into TFR-subsequences by continuous wavelet transform and a dislocated overlapping strategy. Subsequently, the 3DCNN is applied to extract the hidden spatiotemporal features from the TFR-subsequences and construct HIs. Finally, the RUL of the bearings is estimated using the GPR model, which can also define the probability distribution of the potential function and prediction confidence. Experiments on the PRONOSTIA platform demonstrate the superiority of the proposed TFR-subsequence-3DCNN-GPR approach. The use of degradation-related spatiotemporal features in signals is proposed herein to achieve a highly accurate bearing RUL prediction with uncertainty quantification.
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  Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

  Weixin Xu, Huihui Miao, Zhibin Zhao, Jinxin Liu, Chuang Sun, Ruqiang Yan

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 53-53. https://doi.org/10.1186/s10033-021-00565-4

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  As an integrated application of modern information technologies and artificial intelligence, Prognostic and Health Management (PHM) is important for machine health monitoring. Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry. In this paper, a multi-scale Convolutional Gated Recurrent Unit network (MCGRU) is proposed to address raw sensory data for tool wear prediction. At the bottom of MCGRU, six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network, which augments the adaptability to features of different time scales. These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations. At the top of the MCGRU, a fully connected layer and a regression layer are built for cutting tool wear prediction. Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.
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  Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

  Zhe Yang, Dejan Gjorgjevikj, Jianyu Long, Yanyang Zi, Shaohui Zhang, Chuan Li

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 54-54. https://doi.org/10.1186/s10033-021-00569-0

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  Supervised fault diagnosis typically assumes that all the types of machinery failures are known. However, in practice unknown types of defect, i.e., novelties, may occur, whose detection is a challenging task. In this paper, a novel fault diagnostic method is developed for both diagnostics and detection of novelties. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that its performance is satisfactory both in detection of novelties and fault diagnosis, outperforming other state-of-the-art methods. This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect, but also detect unknown types of defects.
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  Deep Learning Based Data Fusion for Sensor Fault Diagnosis and Tolerance in Autonomous Vehicles

  Huihui Pan, Weichao Sun, Qiming Sun, Huijun Gao

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 72-72. https://doi.org/10.1186/s10033-021-00568-1

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  Environmental perception is one of the key technologies to realize autonomous vehicles. Autonomous vehicles are often equipped with multiple sensors to form a multi-source environmental perception system. Those sensors are very sensitive to light or background conditions, which will introduce a variety of global and local fault signals that bring great safety risks to autonomous driving system during long-term running. In this paper, a real-time data fusion network with fault diagnosis and fault tolerance mechanism is designed. By introducing prior features to realize the lightweight network, the features of the input data can be extracted in real time. A new sensor reliability evaluation method is proposed by calculating the global and local confidence of sensors. Through the temporal and spatial correlation between sensor data, the sensor redundancy is utilized to diagnose the local and global confidence level of sensor data in real time, eliminate the fault data, and ensure the accuracy and reliability of data fusion. Experiments show that the network achieves state-of-the-art results in speed and accuracy, and can accurately detect the location of the target when some sensors are out of focus or out of order. The fusion framework proposed in this paper is proved to be effective for intelligent vehicles in terms of real-time performance and reliability.
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  Industrial Internet-enabled Resilient Manufacturing Strategy in the Wake of COVID-19 Pandemic: A Conceptual Framework and Implementations in China

  Tao Peng, Qiqi He, Zheng Zhang, Baicun Wang, Xun Xu

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 48-48. https://doi.org/10.1186/s10033-021-00573-4

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  COVID-19 pandemic has accelerated the re-shaping of globalized manufacturing industry. Achieving a high level of resilience is thereby a recognized, essential ability of future manufacturing systems with the advances in smart manufacturing and Industry 4.0. In this work, a conceptual framework for resilient manufacturing strategy enabled by Industrial Internet is proposed. It is elaborated as a four-phase, closed-loop process that centered on proactive industry assessment. Key enabling technologies for the proposed framework are outlined in data acquisition and management, big data analysis, intelligent services, and others. Industrial Internet-enabled implementations in China in response to COVID-19 have then been reviewed and discussed from 3Rsa€? perspective, i.e. manufacturer capacity Recovery, supply chain Resilience and emergency Response. It is suggested that an industry-specific and comprehensive selection coordinated with the guiding policy and supporting regulations should be performed at the national, at least regional level.
Review
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  Key Technologies and Development Trends in Advanced Intelligent Sawing Equipments

  Yangyu Wang, Yongle Zhang, Dapeng Tan, Yongchao Zhang

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 30-30. https://doi.org/10.1186/s10033-021-00547-6

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  As a starting point in equipment manufacturing, sawing plays an important role in industrial production. Intelligent manufacturing equipment is an important carrier of intelligent manufacturing technologies. Due to the backwardness of intelligent technology, the comprehensive performance of sawing equipments in China is obviously different from that in foreign countries. State of the art of advanced sawing equipments is investigated along with the technical bottleneck of sawing machine tool manufacturing, and a new industrial scheme of replacing turning-milling by sawing is described. The key technologies of processing-measuring integrated control, multi-body dynamic optimization, the collaborative sawing network framework, the distributed cloud sawing platform, and the self-adapting service method are analyzed; with consideration of the problems of poor processing control stableness, low single machine intelligence level, no on-line processing data service and active flutter suppression of sawing with widewidth and heavy-load working conditions. Suggested directions for further research, industry implementation, and industry-research collaboration are provided.
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  Laser Additive Manufacturing on Metal Matrix Composites: A Review

  Neng Li, Wei Liu, Yan Wang, Zijun Zhao, Taiqi Yan, Guohui Zhang, Huaping Xiong

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 38-38. https://doi.org/10.1186/s10033-021-00554-7

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  Important progresses in the study of laser additive manufacturing on metal matrix composites (MMCs) have been made. Recent efforts and advances in additive manufacturing on 5 types of MMCs are presented and reviewed. The main focus is on the material design, the combination of reinforcement and the metal matrix, the synthesis principle during the manufacturing process, and the resulted microstructures as well as properties. Thereafter, the trend of development in future is forecasted, including: Formation mechanism and reinforcement principle of strengthening phase; Material and process design to actively achieve expected performance; Innovative structure design based on the special properties of laser AM MMCs; Simulation, monitoring and optimization in the process of laser AM MMCs.
Mechanism and Robotics
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  Geometric Design-based Dimensional Synthesis of a Novel Metamorphic Multi-fingered Hand with Maximal Workspace

  Wei An, Jun Wei, Xiaoyu Lu, Jian S. Dai, Yanzeng Li

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 41-41. https://doi.org/10.1186/s10033-021-00558-3

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  Current research on robotic dexterous hands mainly focuses on designing new finger and palm structures, as well as developing smarter control algorithms. Although the dimensional synthesis of dexterous hands with traditional rigid palms has been carried out, research on the dimensional synthesis of dexterous hands with metamorphic palms remains insufficient. This study investigated the dimensional synthesis of a palm of a novel metamorphic multi-fingered hand, and explored the geometric design for maximizing the precision manipulation workspace. Different indexes were used to value the workspace of the metamorphic hand, and the best proportions between the five links of the palm to obtain the optimal workspace of the metamorphic hand were explored. Based on the fixed total length of the palm member, four nondimensional design parameters that determine the size of the palm were introduced; through the discretization method, the influence of the four design parameters on the workspace of the metamorphic hand with full-actuated fingers and under-actuated fingers was analyzed. Based on the analysis of the metamorphic multi-fingered hand, the symmetrical structure of the palm was designed, resulting in the largest workspace of the multi-fingered hand, and proved that the metamorphic palm has a massive upgrade for the workspace of underactuated fingers. This research contributed to the dimensional synthesis of metamorphic dexterous hands, with practical significance for the design and optimization of novel metamorphic hands.
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  Design and Analysis of a Multi-Legged Robot with Pitch Adjustive Units

  Qiang Ruan, Jianxu Wu, Yan-an Yao

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 64-64. https://doi.org/10.1186/s10033-021-00578-z

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  The paper proposes a novel multi-legged robot with pitch adjustive units aiming at obstacle surmounting. With only 6 degrees of freedom, the robot with 16 mechanical legs walks steadily and surmounts the obstacles on the complex terrain. The leg unit with adjustive pitch provides a large workspace and empowers the legs to climb up obstacles in large sizes, which enhances the obstacle surmounting capability. The pitch adjustment in leg unit requires as few independent adjusting actuators as possible. Based on the kinematic analysis of the mechanical leg, the biped and quadruped leg units with adjustive pitch are analyzed and compared. The configuration of the robot is designed to obtain a compact structure and pragmatic performance. The uncertainty of the obstacle size and position in the surmounting process is taken into consideration and the parameters of the adjustments and the feasible strategies for obstacle surmounting are presented. Then the 3D virtual model and the robot prototype are built and the multi-body dynamic simulations and prototype experiments are carried out. The results from the simulations and the experiments show that the robot possesses good obstacle surmounting capabilities.
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  Acceleration-Dependent Analysis of Vertical Ball Screw Feed System without Counterweight

  Cunfan Zou, Huijie Zhang, Jun Zhang, Dongdong Song, Hui Liu, Wanhua Zhao

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 65-65. https://doi.org/10.1186/s10033-021-00575-2

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  The distinguishing feature of a vertical ball screw feed system without counterweight is that the spindle system weight directly acts on the kinematic joints. Research into the dynamic characteristics under acceleration and deceleration is an important step in improving the structural performance of vertical milling machines. The magnitude and direction of the inertial force change significantly when the spindle system accelerates and decelerates. Therefore, the kinematic joint contact stiffness changes under the action of the inertial force and the spindle system weight. Thus, the system transmission stiffness also varies and affects the dynamics. In this study, a variable-coefficient lumped parameter dynamic model that considers the changes in the spindle system weight and the magnitude and direction of the inertial force is established for a ball screw feed system without counterweight. In addition, a calculation method for the system stiffness is provided. Experiments on a vertical ball screw feed system under acceleration and deceleration with different accelerations are also performed to verify the proposed dynamic model. Finally, the influence of the spindle system position, the rated dynamic load of the screw-nut joint, and the screw tension force on the natural frequency of the vertical ball screw feed system under acceleration and deceleration are studied. The results show that the vertical ball screw feed system has obviously different variable dynamics under acceleration and deceleration. The influence of the rated dynamic load and the spindle system position on the natural frequency under acceleration and deceleration is much greater than that of the screw tension force.
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  Oil Film Stiffness of Double Involute Gears Based on Thermal EHL Theory

  Zhaoming Yin, Zhimin Fan, Feng Jiang

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 60-60. https://doi.org/10.1186/s10033-021-00582-3

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  Lubrication failure is one of the main failure forms of gear failure. Time varying meshing stiffness is an important factor affecting the dynamic behavior of gears. However, the influence of oil film stiffness is usually ignored in the research process. In this paper, according to the meshing characteristics of double involute gears, based on the non-Newtonian thermal EHL theory, a new calculation method of normal and tangential oil film stiffness for double involute gears is established by the idea of subsection method. The oil film stiffness difference between double involute gears and common involute gears is analyzed, and the influence of tooth waist order parameters, working conditions, and thermal effect on the oil film stiffness are studied. The results reveal that there are some differences between normal and tangential oil film stiffness between double involute gears and common involute gears, but there is little difference. Compared with the torque, rotation speed and initial viscosity of the lubricating oil, the tooth waist order parameters have less influence on the oil film stiffness. Thermal effect has a certain influence on normal and tangential oil film stiffness, which indicates that the influence of thermal effect on the oil film can not be ignored. This research proposes a calculation method of normal and tangential oil film stiffness suitable for double involute gears, which provides a theoretical basis for improving the stability of the transmission.
Intelligent Manufacturing Technology
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  Three-Dimensional Reconstruction of Welding Pool Surface by Binocular Vision

  Zunan Gu, Ji Chen, Chuansong Wu

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 47-47. https://doi.org/10.1186/s10033-021-00567-2

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  Current research of binocular vision systems mainly need to resolve the camera's intrinsic parameters before the reconstruction of three-dimensional (3D) objects. The classical Zhang' calibration is hardly to calculate all errors caused by perspective distortion and lens distortion. Also, the image-matching algorithm of the binocular vision system still needs to be improved to accelerate the reconstruction speed of welding pool surfaces. In this paper, a preset coordinate system was utilized for camera calibration instead of Zhang' calibration. The binocular vision system was modified to capture images of welding pool surfaces by suppressing the strong arc interference during gas metal arc welding. Combining and improving the algorithms of speeded up robust features, binary robust invariant scalable keypoints, and KAZE, the feature information of points (i.e., RGB values, pixel coordinates) was extracted as the feature vector of the welding pool surface. Based on the characteristics of the welding images, a mismatch-elimination algorithm was developed to increase the accuracy of image-matching algorithms. The world coordinates of matching feature points were calculated to reconstruct the 3D shape of the welding pool surface. The effectiveness and accuracy of the reconstruction of welding pool surfaces were verified by experimental results. This research proposes the development of binocular vision algorithms that can reconstruct the surface of welding pools accurately to realize intelligent welding control systems in the future.
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  Self-Adaptive Control System for Additive Manufacturing Using Double Electrode Micro Plasma Arc Welding

  Nan Li, Ding Fan, Jiankang Huang, Shurong Yu, Wen Yuan, Miaomiao Han

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 59-59. https://doi.org/10.1186/s10033-021-00581-4

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  Wire arc additive manufacturing (WAAM) has been investigated to deposit large-scale metal parts due to its high deposition efficiency and low material cost. However, in the process of automatically manufacturing the high-quality metal parts by WAAM, several problems about the heat build-up, the deposit-path optimization, and the stability of the process parameters need to be well addressed. To overcome these issues, a new WAAM method based on the double electrode micro plasma arc welding (DE-MPAW) was designed. The circuit principles of different metal-transfer models in the DE-MPAW deposition process were analyzed theoretically. The effects between the parameters, wire feed rate and torch stand-off distance, in the process of WAAM were investigated experimentally. In addition, a real-time DE-MPAW control system was developed to optimize and stabilize the deposition process by self-adaptively changing the wire feed rate and torch stand-off distance. Finally, a series of tests were performed to evaluate the control system's performance. The results show that the capability against interferences in the process of WAAM has been enhanced by this self-adaptive adjustment system. Further, the deposition paths about the metal part's layer heights in WAAM are simplified. Finally, the appearance of the WAAM-deposited metal layers is also improved with the use of the control system.
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  High-Speed Machining of Malleable Cast Iron by Various Cutting Tools Coated by Physical Vapor Deposition

  Suyang Li, Haisheng Lin, Tingjie Zhang, Jianbo Sui, Chengyong Wang

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 46-46. https://doi.org/10.1186/s10033-021-00561-8

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  The coating material of a tool directly affects the efficiency and cost of machining malleable cast iron. However, the machining adaptability of various coating materials to malleable cast iron has been insufficiently researched. In this paper, turning tests were conducted on cemented carbide tools with different coatings (a thick TiN/TiAlN coating, a thin TiN/TiAlN coating, and a nanocomposite (nc) TiAlSiN coating). All coatings were applied by physical vapor deposition. In a comparative study of chip morphology, cutting force, cutting temperature, specific cutting energy, tool wear, and surface roughness, this study analyzed the cutting characteristics of the tools coated with various materials, and established the relationship between the cutting parameters and machining objectives. The results showed that in malleable cast iron machining, the coating material significantly affects the cutting performance of the tool. Among the three tools, the nc-TiAlSiN-coated carbide tool achieved the minimum cutting force, the lowest cutting temperature, least tool wear, longest tool life, and best surface quality. Moreover, in comparisons between cemented-carbide and compacted-graphite cast iron machined under the same conditions, the wear mechanism of the coated tools was found to depend on the cast iron being machined. Therefore, the performance requirements of a tool depend on multiple factors, and selecting an appropriately coated tool for a particular cast iron material is essential.
Advanced Transportation Equipment
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  Vertical Tire Forces Estimation of Multi-Axle Trucks Based on an Adaptive Treble Extend Kalman Filter

  Buyang Zhang, Ting Xu, Hong Wang, Yanjun Huang, Guoying Chen

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 55-55. https://doi.org/10.1186/s10033-021-00559-2

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  Vertical tire forces are essential for vehicle modelling and dynamic control. However, an evaluation of the vertical tire forces on a multi-axle truck is difficult to accomplish. The current methods require a large amount of experimental data and many sensors owing to the wide variation of the parameters and the over-constraint. To simplify the design process and reduce the demand of the sensors, this paper presents a practical approach to estimating the vertical tire forces of a multi-axle truck for dynamic control. The estimation system is based on a novel vertical force model and a proposed adaptive treble extend Kalman filter (ATEKF). To adapt to the widely varying parameters, a sliding mode update is designed to make the ATEKF adaptive, and together with the use of an initial setting update and a vertical tire force adjustment, the overall system becomes more robust. In particular, the model aims to eliminate the effects of the over-constraint and the uneven weight distribution. The results show that the ATEKF method achieves an excellent performance in a vertical force evaluation, and its performance is better than that of the treble extend Kalman filter.
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  Simulation Research on Dynamic Characteristics of Hydraulic Mount

  Yanhua Liu, Xin Guan, Pingping Lu, Rui Guo

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 49-49. https://doi.org/10.1186/s10033-021-00571-6

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  At present, research on hydraulic mounts has mainly focused on the prediction of the dynamic stiffness and loss angle. Compared to the traditional finite element analysis method, the programming method can be used to analyze hydraulic mounts for a rapid and accurate understanding of the influence of the different mounting parameters on the dynamic stiffness and loss angle. The aims of this study were to investigate the nonlinear dynamic characteristics of a hydraulic mount, and to identify the parameters that affect the dynamic stiffness and loss angle using MATLAB software programs to obtain the influence curves of the parameters, so as to use suitable parameters as the basis for vibration analysis. A nonlinear mechanical model of a hydraulic mount was established according to the basic principles of fluid dynamics. The dynamic stiffness and loss angle of the dimensionless expression were proposed. A numerical calculation method for the dynamic performance evaluation index of the hydraulic mount was derived. A one-to-one correspondence was established between the structural parameters and peak frequency of the evaluation index. The accuracy and applicability of the mechanical model were verified by the test results. The results demonstrated the accuracy of the nonlinear mechanical model of the hydraulic mount, and the vehicle driving comfort was greatly improved by the optimization of the structural parameters.
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  Design and Performance Study of Clutch Disc Assembly of Wide-Angle, Large-Hysteresis, Multistage Damper

  Maoqing Xie, Leigang Wang, Yao Huang

  Chinese Journal of Mechanical Engineering. 2021, 34(3): 50-50. https://doi.org/10.1186/s10033-021-00572-5

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  The clutch is an important component of the vehicle driveline system. One of its major functions is to attenuate or eliminate the torsional vibration and noise of the driveline system caused by the engine. Based on experiments of vibration damping under different vehicle conditions, the structure and functional principle of a clutch-driven disc assembly for a wide-angle, large-hysteresis, multistage damper is investigated in this study using an innovative combined approach. Furthermore, a systematic integration of key technologies, including wide-angle low-stiffness damping technology, large-hysteresis clutch technology, a novel split pre-damping structure technology, damping structure technology for component cushioning, and multistage damping structure technology, is proposed. The results show that the total torsional angle of the wide-angle large-hysteresis, multistage damper is more than twice that of the traditional clutch damper. The multistage damping design allows a better consideration of various damping requirements under different vehicle conditions, which can effectively address problems of severe idle vibrations and torsional resonance that occur under idled and accelerated conditions. Meanwhile, the use of a large-hysteresis structure and wear-resistant materials not only improves the vibration damping performance, but also prolongs the product service life, consequently resulting in multi-faceted optimization and innovative products.