05 February 2025, Volume 61 Issue 3

    

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  Cognitive Empowerment for Human-robot Collaboration: Research Progress and Challenges

  KOU Yiqun, YANG Ye, LIU Jie, HU Youmin, LI Lin, YU Baichuan, XU Jiahe, HU Zhongxu, SHI Tielin

  Journal of Mechanical Engineering. 2025, 61(3): 1-22. https://doi.org/10.3901/JME.2025.03.001

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  In the transition from Industry 4.0 to Industry 5.0, a human-centered approach has gradually emerged as a focal point in the field of smart manufacturing. Current human-machine collaboration not only emphasizes technological advancements and efficiency improvements but also stresses the integration of human higher-order cognitive thinking with machine computational capabilities to achieve cognitive empowerment. Based on this premise, this study reviews existing research on cognitive empowerment in human-machine collaboration, focusing on key areas such as interactive perception, task planning and execution, and skill learning. The challenges of multimodal information integration, task reasoning, dynamic decision-making, and skill knowledge representation are highlighted. Furthermore, methods are proposed to support human-machine cognitive using knowledge graph construction technologies, as well as to optimize tasks and facilitate dynamic decision-making in complex environments through the application of knowledge graph reasoning techniques. Building upon an analysis of the limitations in current research on cognitive empowerment in human-machine collaboration, this study also forecasts the future directions for deep cognitive collaboration within intelligent manufacturing environments.
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  Digital Shop Floor Manufacturing Capability Modeling and Adaptive Scheduling in Human-cyber-physical Interconnected Environment

  HU Bingtao, ZHONG Ruirui, FENG Yixiong, YANG Chen, WANG Tianyue, HONG Zhaoxi, TAN Jianrong

  Journal of Mechanical Engineering. 2025, 61(3): 23-39. https://doi.org/10.3901/JME.2025.03.023

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  The development of Industry 5.0 presents higher requirements for the informatization, digitization, and intelligence of the manufacturing industry. To address the important challenges of the lack of traditional workshop manufacturing capacity organizational paradigm and intelligent scheduling technology, a digital workshop manufacturing capacity modeling and adaptive scheduling technology in the human-cyber-physical interconnected environment is proposed to achieve high-fidelity modeling of complex workshop manufacturing capacity and efficient scheduling of production resources. In order to effectively manage the production elements in the digital workshop, a digital workshop manufacturing capacity modeling technology that integrates the human-cyber-physical system is proposed. In addition, a deep reinforcement learning-driven adaptive scheduling algorithm (DRL-AS) is devised for the digital workshop, which models the flexible job shop scheduling problem in the form of heterogeneous directed acyclic graphs. Considering the complex coupling relationship between operations and machines, a multi-factor representation method based on hierarchical self-attention mechanism is designed to extract global features of the environmental state and assist the agent in making high-quality decisions. Proximal policy optimization (PPO) algorithm is used to train the proposed adaptive scheduling technology. Experimental results show that the scheduling performance and generalization performance of the proposed method are significantly better than those of the comparison algorithms.
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  Modeling and Optimization Decision of Green Design Scheme of Product Integrating Scenario

  GUO Jing, WANG Liming, QI Jin, HU Jie, KONG Lin

  Journal of Mechanical Engineering. 2025, 61(3): 40-51. https://doi.org/10.3901/JME.2025.03.040

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  The manufacturing and use of electromechanical products consume vast amounts of energy and resources, while generating significant amounts of pollutant emissions, resulting in severe environmental pollution problems. The use of whole life cycle design methods can minimize the negative environmental impact of products, and ultimately improve product quality and sustainability. Therefore, a method of product green design scheme expression based on directed network structure is proposed to realize effective integration of functional domain, structural domain, process domain and material domain information related to product life cycle design and multi-domain association mapping with the whole life cycle stage. The expression method of life cycle scene based on set theory is established, and the expression model of design scheme integrating life cycle scene is constructed by designing node interface step by step matching. According to the characteristics of the established network model of product design, the depth-first search algorithm with the pruning strategy is used to generate individual initial design schemes by traversing each design node in the design network model. A coding strategy based on the adjacent real number matrix is developed for the correlation between design nodes in the individual design scheme. By setting the differential evolution operator, the green design scheme of 2.5 MW wind turbine is optimized under the condition of meeting the environmental constraints of the product life cycle.
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  Thermal Error Modeling Method towards Electric Spindles Based on Digital Twin and Deep Transfer Learning

  MA Shuai, LENG Jiewu, CHEN Zhuyun, LI Weihua, LI Bo, LIU Qiang

  Journal of Mechanical Engineering. 2025, 61(3): 52-66. https://doi.org/10.3901/JME.2025.03.052

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  Thermal error is one of the main sources of electric spindle system errors, and thermal error modeling is an important means to improve system reliability. In machining scenarios where the electric spindle is loaded with tools, it needs to move in multiple directions, which makes real-time measurement difficult and difficult to collect sufficient thermal error samples. Furthermore, the data distribution under different working conditions presents large discrepancies, and a well-trained model under one working condition failed to obtain satisfactory prediction accuracy when applied to another working condition. To address these issues, a thermal error modeling approach based on digital twins and deep transfer learning is proposed. Firstly, a digital twin model of the thermal behavior of the electric spindle system is established, where the temperature fields and thermal deformation data under different working conditions can be simulated to alleviate the limitation of the scarcity of the thermal error samples in real scenarios. Secondly, a convolutional bidirectional long short-term memory network based on the domain adversarial mechanism is developed. The virtual data generated by the digital twin model is used as the source domain, and the real data is used as the target domain. Convolutional layers of different scales are used as the feature extractor to extract the spatial features of the temperature data from both the source and target domains so as to address the collinearity issue of multi-dimensional temperature features. The bidirectional long short-term memory network is constructed as a predictor to process the time-series relationship between temperature and thermal error and output predictions. Additionally, the adversarial training technique of domain adaptation is employed to confuse the two domain features and minimize the distribution discrepancies between both domains, thereby improving the model's generalization ability. Finally, a multi-source data collaborative collection platform is established to obtain real data under variable working conditions. Different transfer tasks are constructed to validate the proposed method and the results showed the proposed method successfully achieves thermal error modeling in the absence of labeled thermal error samples and exhibits good prediction performance.
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  Cross-domain Emerging Fault Diagnosis of Rotating Machinery Using Source-free Self-supervised Domain Adaptation Network

  YUE Ke, LI Jipu, CHEN Zhuyun, HE Guolin, DENG Shuhan, LI Weihua

  Journal of Mechanical Engineering. 2025, 61(3): 67-76. https://doi.org/10.3901/JME.2025.03.067

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  With the rapid advancement of next-generation artificial intelligence technologies, data-driven intelligent fault diagnosis methods have found widespread applications in mechanical equipment. High-precision diagnosis of intelligent diagnostic models typically relies on a large volume of labeled data. However, unpredictable new faults may occur during the operation of mechanical equipment, which makes it difficult to adopt the model trained on known samples to accurately identify newly occurring faults. Furthermore, data privacy restricts the accessibility of data, adding substantial complexity to the domain adaptation process for diagnostic models. To address these challenges, a source-free self-supervised domain adaptation network is proposed for cross-domain emerging fault diagnosis of rotating machinery, which enables diagnosis emerging fault in target domain without access to source domain. First, a source domain fault diagnosis model is established using labeled source samples. Subsequently, a self-supervised pseudo-labeling technique based on uncertainty information entropy is utilized to acquire a target domain dataset with high-quality pseudo-labels. Finally, we merge the pseudo-labeled fault dataset with unlabeled fault dataset, then train the model through an adversarial training strategy to realize emerging fault detection. The effectiveness of the proposed method is validated through experiments conducted on an automotive gearbox dataset. Experimental results show that the proposed method can accurately detect emerging faults while maintaining the performance in classifying known fault types.
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  Robust Tolerance Design for Failure Rate of Roadheader Cutting Unit Based on Human-machine Collaboration

  CUI Kaiyue, HONG Zhaoxi, LOU Shanhe, YAN Weiyu, FENG Yixiong, TAN Jianrong

  Journal of Mechanical Engineering. 2025, 61(3): 77-90. https://doi.org/10.3901/JME.2025.03.077

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  The robust operation of the roadheader cutting unit is a necessary condition for the timely and high-quality completion of excavation work. Robust tolerance design for failure rate of cutting unit is an effective measure to meet the robust operation while reducing manufacturing costs. Aiming at the problems of large sample size of fault data, lack of relevant robustness optimization mathematical models, and difficulty in solving high-dimensional objective functions, a robust tolerance method for failure rate of cutting unit based on human-machine collaboration is proposed. This method is supported by the strong computing power of computers. Firstly, the human interval fuzzy reasoning knowledge and maximum likelihood estimation method are employed to fit a significant number of failure data and obtain an approximate estimation of components' failure rates. Secondly, a robustness metric function is designed based on the mathematical goal programming knowledge and n-fold Riemannian integrals, and a high-dimensional and high-order robustness optimization mathematical model is constructed. Then, the seeker optimization algorithm and algorithm parameter analysis knowledge are utilized to solve the problem, and a relatively optimal solution of the failure rate tolerance is obtained. Finally, a multi-dimensional fluctuation simulation strategy based on integration is designed to obtain the fluctuation curve of the cutting unit’s failure rate. The comparison results show that the proposed robust tolerance design method for failure rate of cutting unit based on human-machine collaboration is capable of obtaining the extended ranges of components’ failure rates, and the cutting unit’s robust performance is maintained at a high level. With the combined advancements of human cognitive ability and computer computing intelligence, the proposed robust tolerance design method can be further applied to other complex systems through adaptive adjustments.
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  New Pattern of Operation & Maintenance Data and Multi-modal Knowledge Driven Improvement Design for Complex Products

  ZHAO Xin, REN Shan, ZHANG Geng, ZHANG Yingfeng

  Journal of Mechanical Engineering. 2025, 61(3): 91-104. https://doi.org/10.3901/JME.2025.03.091

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  Based on analysis of the challenges facing by complex products (CPs) manufacturing enterprises during innovative development, the limitations of existing product improvement design methods and the evolution directions of current product design paradigms, a new pattern and architecture of operation & maintenance (O&M) data and multi-modal knowledge (MMK) driven improvement design for CPs is proposed and designed. The operation logic of "configuration → monitoring → evaluation → correlation → feedback → improvement" for the proposed pattern is discussed. The related key technologies of the new pattern are put forward and elaborated, which included the O&M data proactive perceiving and lifecycle data value-adding calculation of CPs, the key functional modules performance degradation assessment and key design parameters identification of CPs, and the knowledge modeling and design scheme intelligent configuration for CPs improvement design, etc. By applying these technologies, the cross-stage and multi-business collaborative optimization of design and O&M stages can be facilitated effectively. As a result, the effectiveness of product improvement design schemes and the intelligence of scheme configuration processes are enhanced. The proposed new pattern and key technologies could provide important referential value to the research and application of exploring a new manufacturing service mode that leading the development of high-end equipment manufacturing industry.
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  Research on Dual-agent Work Mechanism with Human-smart System Collaboration in Human-centric Smart Manufacturing Cell

  LI Jiajia, YI Qian, FENG Yixiong, ZHU Pengxing, YI Shuping

  Journal of Mechanical Engineering. 2025, 61(3): 105-118. https://doi.org/10.3901/JME.2025.03.105

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  The technology-driven smart manufacturing cell faces a series of challenges such as insufficient flexibility to cope with dynamic environments, difficulty in dealing with atypical production disturbances, and limited decision-making for multi-scenario integration. To this end, a dual-agent work mechanism for human-smart system collaboration in human-centric smart manufacturing cells is proposed. The development and evolution of the manufacturing cell under the concept of human-centric smart manufacturing is analyzed, and the effective ways of human-smart system collaboration are explored. A dual-agent work mechanism constructed by enhanced perception, communication and interaction, dynamic feedback, collaborative smart decision-making, continuous learning, and self-adaptation is proposed and applied to the atypical scenario of fault diagnosis and self-healing, which includes condition monitoring, fault diagnosis, fault repair, recovery and validation, and continuous learning and improvement. The fault repair of a smart manufacturing cell for a transmission case of a heavy-duty vehicle is used as a case study to demonstrate the effectiveness of the dual-agent work mechanism.
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  Health Indicator of Equipment Condition Monitoring Based on Adaptive Nonlinear State Estimation Under Compound Fault Degradation

  PEI Xuewu, LI Xinyu, GAO Liang, GAO Yiping, CHEN Zhimin

  Journal of Mechanical Engineering. 2025, 61(3): 119-129. https://doi.org/10.3901/JME.2025.03.119

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  Modern machinery and equipment have been in service for a long time under harsh and changeable working conditions and it is inevitable that multiple fault types will occur, resulting in a degradation process of compound fault conditions. Traditional health indicators usually focus on the condition monitoring of a single fault degradation situation and are not suitable for equipment condition monitoring in this situation. To solve the above problems, a new health indicator construction method based on adaptive nonlinear state estimation (ANSE) for equipment condition monitoring under coupled fault degradation is proposed, which includes two parts: initial fault transient detection and early fault accurate location. First, a singular value feature sequence is constructed based on Hilbert singular value decomposition (Hilbert-SVD) as input to the state monitoring model. Then, the nonlinear state estimation (NSE) reconstruction error characteristic is used to construct the HI to amplify the difference between the initial fault sample and the normal sample. After that, in order to self-adapt transient detection of initial faults, ANSE model is constructed by introducing peak overthreshold (POT) algorithm to realize dynamic update of health threshold. Finally, resonance based sparse signal decomposition and maximum correlation kurtosis deconvolution (RSSD-MCKD) feature extraction, using power spectrum analysis of fault characteristic frequency to achieve accurate fault location. The effectiveness and robustness of the proposed method are verified in the bearing data set of two coupled fault degradation cases and the engineering verification is carried out in one fan main bearing data set. By analyzing the test results and comparing the results, the proposed method can better detect the initial faults of the equipment and also characterize the entire degradation process, indicating that the proposed method has strong condition monitoring ability.
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  Human-robot Autonomous Collaboration Method of Smart Manufacturing Systems Based on Large Language Model and Machine Vision

  HUANG Sihan, CHEN Jianpeng, XU Zhe, YAN Yan, WANG Guoxin

  Journal of Mechanical Engineering. 2025, 61(3): 130-141. https://doi.org/10.3901/JME.2025.03.130

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  In Industry 4.0, the emerging technologies such as artificial intelligence, big data, and the Internet of Things are appearing endlessly, accelerating the transformation and upgrading of the manufacturing industry. In this process, industry robot plays an increasingly important role, which also lays a solid foundation for the high-quality development of intelligent/smart manufacturing. With the proposal of Industry 5.0, human centricity concept becomes popular, which has given birth to the emerging field of human-centric smart manufacturing. The boundary between human and robot in the smart manufacturing systems gets blurred, and the research on human-robot autonomous collaboration has attracted more and more attentions. Therefore, proposes a human-robot autonomous collaboration method based on large language model and machine vision to improve the intelligence level of human-robot collaboration. First, dynamic perception of the working process for human-robot collaboration is carried out by the fusion of machine vision and deep learning, where the fusion of YOLO and transfer learning is adopted to accurately identify the operate progress and the long short-term memory network and attention mechanism are combined to recognize the actions of operator. Second, the large language model is fine-tuned for human-robot collaboration to realize autonomous operating decision for smart robot during the dynamic work process. Finally, a reducer assembly case is used to verify the effectiveness of the proposed method.
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  Knowledge Graph Construction and Intelligent Reasoning Methods for Human-computer Hybrid Interaction in Product Design

  GUO Xin, HUANG Zechuan, WANG Jie, ZHANG Kai, ZHAO Wu

  Journal of Mechanical Engineering. 2025, 61(3): 142-153. https://doi.org/10.3901/JME.2025.03.142

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  Product design is a creative activity oriented to the iterative and progressive evolution of user requirements, which centers on the application of multi-domain knowledge. To address the problems of low knowledge visualization in product design, weak knowledge reasoning under human-computer interaction and to improve the degree of product design intelligence, a product interactive design knowledge application model based on knowledge graph (KG) is proposed. Firstly, based on the role and interaction evolution mechanism between product design activities, the correlations between product design problems and solution knowledge are analyzed, and an expandable multi-layer knowledge graph for product design (m-KGPD) is constructed and used to structurally organize cross-domain, multi-disciplinary solution knowledge and to establish a knowledge requirement-solution knowledge information retrieval channel. Secondly, the data annotation platform doccano is used to carry out knowledge text annotation and model training set construction, and based on the BERT-BiLSTM-CRF model to carry out solution knowledge entity relationship extraction, to alleviate the repetitive manual operations in the large-scale textual knowledge extraction. The knowledge graph visualization platform GraphXR is used to complete the graph construction. Finally, based on the interactive genetic algorithm (IGA), an iterative inference method is proposed to satisfy the evolution rule of interactive product design, and match the optimal solution knowledge set for the product design knowledge requirements through the hybrid human-computer interaction. The feasibility and effectiveness of the method are verified by taking the interaction design process of the complex multi-rock formation hole-forming equipment as an example.
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  Flight Simulation Cockpit Imagery Biomimetic Visual Focus Prediction Model Based on FCN-enhanced Genetic Algorithm

  CHEN Guoqiang, SHEN Zhengyi, YANG Yuchi, LI Teng, XU Li

  Journal of Mechanical Engineering. 2025, 61(3): 154-166. https://doi.org/10.3901/JME.2025.03.154

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  The low cognitive aggregation of cockpit field of view (FOV) is identified as a key issue affecting the efficiency of flight simulation cockpit training. An approach based on fully convolutional networks (FCN) optimized genetic algorithm (GA) is proposed for predicting biomimetic visual focus in cockpit imagery. Firstly, cockpit FOV zones are determined based on grid embedding combined with eye tracker areas of interest, and the existing cockpit cognitive aggregation is validated using eye movement swarm plots and questionnaires. Secondly, a biomimetic design dataset is formed through dataset selection and optimization of eye movement grayscale images, and FCN models are trained for biological, product, and line drawing. Combining the FCN model with predicted inherent visual focus heatmaps and digital matrices of biological shapes, visual cognitive scores are calculated, zones are sorted according to subjective cognitive scores, and a biomimetic mapping relationship is established for expressing schematic designs of characterized zones. Finally, leveraging the FCN model, an adaptability function is established, and GA is utilized for optimizing schematic representations of zones. Decoding the optimal cockpit factors with the best match between visual and subjective cognition, refining the design, and validating the design scheme's visual cognition. The results demonstrate that the FCN-optimized GA model can enhance biomimetic design efficiency, resulting in cockpit FOV designs with higher cognitive aggregation, significantly improving the alignment between drivers' subjective cognition of cockpit FOV and visual cognition.
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  Robust Modeling of Robot Trajectories Based on Mixture Probabilistic Movement Primitives

  SU Yongbin, LIU Tundong

  Journal of Mechanical Engineering. 2025, 61(3): 167-177. https://doi.org/10.3901/JME.2025.03.167

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  To address the issue of outlier trajectories in the demonstration learning process for robots with a wide operating range, a method for robust modeling of robot demonstration trajectories based on mixture probability motion primitives is proposed. The method first preprocesses the collected demonstration trajectories using dynamic time warping (DTW) to optimize the distribution of trajectory points by optimizing the mapping matrix. This resolves the problem of varying trajectory lengths caused by jerky demonstration movements and uneven speeds. After obtaining trajectory sets with equal time lengths, a trajectory point threshold based on confidence intervals in the probabilistic motion primitive model is used to cluster and assign weights to samples within the trajectory set. Finally, a mixture probability motion primitive model is constructed based on the clustering results and weight parameters to suppress the parameter deviation caused by outlier trajectories and improve model robustness. To validate the effectiveness of the proposed method, experiments were conducted using handwritten letter trajectory datasets and real robotic arm feeding trajectory demonstrations. Metrics such as average Fréchet distance and average Euclidean distance are introduced for quantitative evaluation of the model’s reliability. The experimental results demonstrate that the proposed method effectively mitigates the parameter deviation caused by outlier trajectories. It exhibits stronger robustness compared to traditional probabilistic motion primitive models and Gaussian mixture models, highlighting its promising application prospects in the field of industrial robots with large workspace radii.
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  Review of Micro-bionic Robots

  LUO Zirong, HONG Yang, JIANG Tao, LIN Zening, YANG Yun, ZHU Qunwei

  Journal of Mechanical Engineering. 2025, 61(3): 178-196. https://doi.org/10.3901/JME.2025.03.178

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  As a micro-electromechanical system with a size of centimeters or below, micro-bionic robots have the characteristics of small size, light weight and excellent portability. They are widely used in complex environments such as environmental detection, target search, reconnaissance and strike. In order to enable researchers to understand the research progress of micro-bionic robots, a summary and analysis of relevant literature in the past 15 years are conducted based on the world’s largest abstract and citation database Scopus, providing a visual depiction of the development trends in the field of micro-bionic robots. The general characteristics and research status of micro-bionic robots are summarized from the three key points of the bionic movement form, manufacturing technology and driving technology of micro-bionic robots, supplemented by the introduction of the special research direction of bio-electromechanical hybrid micro robots. The technical bottleneck of the development of micro-bionic robots is fully analyzed, and the development idea of energy-driving-sense-control full flexible integration is put forward, which promotes the innovative development of integrated manufacturing technology. Based on the military and anti-terrorism and riot control application background, the characteristics and advantages of micro-bionic robots are fully analyzed, and the combat application conception with micro-bionic robots as the core is carried out. In addition, the application of micro-bionic robots in civil life is discussed. Finally, the shortcomings and future development of the existing micro-bionic robots are discussed and summarized, which provides a valuable reference for the development of the micro-bionic robots technology and its military application prospect.
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  Research Progress of Aerial Manipulators for Contact-based Operations

  SONG Guangming, HAO Shuang, JI Zichao, ZHANG Junyi, SONG Aiguo

  Journal of Mechanical Engineering. 2025, 61(3): 197-211. https://doi.org/10.3901/JME.2025.03.197

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  Aerial manipulators (AMs) are a new type of aerial robots that integrate multirotor unmanned aerial vehicles (UAVs) with robotic manipulators. With agile flight and manipulation capabilities simultaneously, AMs can reach high-altitude places to perform transportation or contact-based tasks, which leads to broad application prospects. Contact-based-operation-type AMs are advanced technical equipment for infrastructure maintenance which are urgently needed by many industries including oil and gas, transportation, hydraulic engineering, and electric power. In the past decade, contact-based-operation-type AM technologies have attracted increasing attentions in the field of robotics and automation. This research summarizes and analyzes the past studies on the key technologies for contact-based-operation-type AMs. Firstly, the contact-based operation modes of the AMs are introduced, which are divided into three types according to the interaction characteristics between the end effectors and the target objects, i.e., point contact, sliding contact, and intervention contact. Then, the mechanism design technologies are discussed by comparing some representative prototypes based on the conventional multirotor UAVs, the fully-actuated multirotor UAVs, the interconnected actuated multibody platforms, etc. The control methods are discussed, which include the flight control, the passive compliance control, the active compliance control, and the bilateral teleoperation control. Finally, the future development trends of these key technologies are prospected.
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  Dual-robot Surgical System for Collaborative Maxillofacial Osteotomy

  LUO Xuejin, ZHANG Runshi, DENG Yingyan, MO Hao, ZHU Jiayu, LIU Xinyu, HE Yang, WANG Junchen

  Journal of Mechanical Engineering. 2025, 61(3): 212-224. https://doi.org/10.3901/JME.2025.03.212

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  The complex craniomaxillofacial anatomy and narrow surgical field pose significant challenges for surgical procedures. These challenges include high surgical difficulty, cooperation between surgeons and assistants, and over-reliance on the surgeon’s experience. The fatigue of surgeons is reduced and the accuracy of operation is improved via the intelligent image analysis technology and high precision robotic system. To promote efficiency, a dual-robot surgical system for maxillofacial osteotomy is proposed. The self-supervised pre-training learning network is used to realize segmentation and reconstruction. The iterative closest point algorithm is employed for image alignment. The planning trajectory is realized and mapped from the image space to the robotic task space via the optical tracking and registration. The hybrid osteotomy control method combining admittance control and visual servo tracking is proposed in the 1 kHz real-time framework based on the EtherCAT. The dual-robot system is tested with a skull model. The human-machine interaction is demonstrated. The experimental results show that the dice of mandible segmentation is 94.95% and the osteotomy error is 1.68±0.26 mm, confirming the effectiveness of the proposed method.
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  Design and Control of a Pneumatic Artificial Muscle Actuated Exoskeleton Robot for Upper Limb Rehabilitation

  QIN Yanding, FAN Jiade, ZHANG Haoqi, TIAN Mengqiang, HAN Jianda

  Journal of Mechanical Engineering. 2025, 61(3): 225-236. https://doi.org/10.3901/JME.2025.03.225

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  A pneumatic artificial muscle (PAM) actuated exoskeleton is developed for upper limb rehabilitation and augmentation. Different from rigid actuation, the developed exoskeleton combines PAM and rigid link to achieve both flexible actuation and high-precision movement. This helps to reduce the risk of unwanted injury to users during the rehabilitation process. In structural design, a combination of direct driven and cable driven is adopted to provide 3 degrees-of-freedom actuation for the shoulder and elbow joints. The compact structure helps to facilitate its wearability. This research presents the kinematics modeling of the exoskeleton, and dynamic modeling is then finished using the three-element model of PAM and Lagrange method. For the hysteresis nonlinearity of PAM, the combination of direct inverse modeling and adaptive projection algorithm is adopted to achieve adaptive hysteresis compensation without offline modeling and inversion. Finally, the feasibility of the exoskeleton and the proposed controller is verified via hysteresis compensation and anti-interference experiments. Experimental results show that the developed exoskeleton features both flexible actuation and high motion accuracy, satisfying the needs of upper limb rehabilitation and augmentation.
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  Mechanism Design and Kinematics Analysis for a Solid Surface Deployable Antenna

  FAN Xiaodong, SHI Chuang, WANG Zhiyi, LIU Tianming, GUO Hongwei, LIU Rongqiang

  Journal of Mechanical Engineering. 2025, 61(3): 237-250. https://doi.org/10.3901/JME.2025.03.237

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  The solid surface deployable antenna has the characteristics of high reliability, high accuracy and good thermal stability. In view of the problems that the shape accuracy, linkage drive and locking stiffness of the solid surface deployable antenna structure are affected by the excessive number of rotating pairs and the presence of spherical joints, universal joints and other high pairs in the deployment mechanism of the solid surface deployable antenna, the two deployment methods of the solid surface deployable antenna rotating around the orthogonal double axis in two steps and rotating around the single axis in one time are compared. A deployable mechanism for deployable antenna on solid surface is proposed, which rotates around a single axis and deploys once. The parametric model for calculating the position of the rotation axis is constructed, and the kinematic model of the deployment mechanism is established. The numerical simulation software is used to simulate the established model, and the influence of the position of the rotation axis on the antenna storage rate and the gap between adjacent panels is analyzed. The finite element model of the antenna structure is established, and the antenna structure is optimized. The results show that the design of rotating around a single axis reduces the number of rotating pairs and does not use high pairs such as spherical joints and universal joints, which not only improves the deployment reliability of the mechanism, but also facilitates the linkage, driving, locking and stiffening of the mechanism. At the same time, the optimized structure has better mechanical performance, which is expected to provide reference for the theoretical research and engineering application of deployable antennas on solid surfaces.
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  Reliability Growth of FAST Actuator

  LEI Zheng, JIANG Peng, YAO Rui, WANG Yong

  Journal of Mechanical Engineering. 2025, 61(3): 251-258. https://doi.org/10.3901/JME.2025.03.251

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  The FAST observation efficiency was affected seriously by the low reliability of the actuator. To solve the problem, the actuator reliability growth scheme which included the self-purification function, drive system, environmental protection and condition monitoring is proposed. And the accelerated reliability growth test method for multiple items asynchronous corrective actions of actuator is designed. According to the reliability analysis, the mean time between failures (MTBF) predicted value of the reliability growth scheme for actuator is 81 536 h, and the value is more than 12 times that of in 2019. Three prototypes are used for the reliability growth test, and the accelerated reliability growth testing method which tracking and reciprocating continuous alternating working mode and increase the load scheme are designed. The test lasted from May 2019 to August 2022, and prototypes are failure-free working except for 3# had three times B failure. The reliability test is finished with the time truncation mode. And the test data is analyzed by the AMSAA model, and the test predicted result of MTBF is 80 061 h. The MTBF results of design and test agree with each other well, and the effectively of actuator reliability growth solution is proved. Perform statistical analysis on the one-year working data after completing all upgrades. And the average failure rate of the actuators in last year is 0.35 units a day, and the failure rate has been decreased by 12.2 times. This numerical values are consistent with the theoretical analysis and test results, and the effectiveness of the reliability growth scheme and the reliability accelerated test method is supported again. FAST actuator reliability growth has been achieved greatly by the research method of synthesizes the theoretical analysis, experimental verification and engineering practice, and the reliability of the actuator group system has increased by an order of magnitude, which can provide reference for other similar studies.
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  Surface Topography Prediction in Micro-milling Processing of Aerostatic Spindle under High-speed Effects

  SHI Jianghai, FENG Xin, CAO Hongrui

  Journal of Mechanical Engineering. 2025, 61(3): 259-271. https://doi.org/10.3901/JME.2025.03.259

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  Aerostatic spindle is the core component of ultra-precision machine tool, and its dynamic characteristics directly affect the machining performance of machine tool. Under high-speed conditions, the centrifugal force and gyroscopic torque of the rotor, and the dynamic parameters (stiffness and damping) of the air bearings vary with the speed, which in turn affects the dynamic characteristics of the aerostatic spindle. Due to the coupling effect between the dynamic characteristics of the spindle and the micro-milling process, the cutting force will fluctuate and change discontinuously under different cutting conditions. As a result, it often leads to a large vibration response of the spindle. If the vibration response is too large, it will cause the chatter phenomenon, which directly leading to workpiece surface processing failure. Therefore, based on Timoshenko beam element, an aerostatic spindle dynamic model considering speed effect and a 3D surface topography prediction model for micro-milling are established. And the influence of rotational speed and feed per tooth on the surface topography of the workpiece is quantitatively analyzed. The results show that during the steady cutting process, the distance between adjacent cutting marks on the workpiece surface increases gradually with the increase of the feed per tooth, causing the surface roughness increases linearly. When rotational speed and feed per tooth reach a small amount, the rotational speed can affect the machining efficiency, but has little effect on the roughness of the machined surface.
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  Joint Reconstruction on Vibration Information of Flexible Rotor Systems Based on Augmented Kalman Filter

  JIANG Minghong, GAO Xianghong, ZHANG Peng, LI Wengheng, CHEN Wulong, ZHU Changsheng

  Journal of Mechanical Engineering. 2025, 61(3): 272-283. https://doi.org/10.3901/JME.2025.03.272

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  Knowledge on rotor vibration at target positions is essential for evaluating or suppressing rotor vibration. However, limitations such as rotor structure or working environment may impede sensor installation at target positions. In order to reconstruct rotor vibrations at the target position using information collected from other positions, an augmented Kalman filter (AKF) is proposed for joint reconstruction on vibration and unbalanced excitations. Firstly, the influences of unbalance excitation on reconstruction results of classical Luenberger observer are investigated and the potential instability caused by excessive gain is elaborated. Then, a new augmented state variable is designed by expressing the unbalance excitation in the form of unbalance parameters and the discrete state equation of the augmented system is established accordingly. Finally, an AKF is designed to perform real-time estimation of the augmented system states, and the effectiveness of the joint reconstruction strategy is verified through simulation and experiments. The results show that the convergence speed of various observers is faster than dynamic changes of rotor systems, so they can be used for real-time reconstruction of rotor system vibration. Unbalance excitation will affect the reconstruction accuracy, and the accuracy of AKF considering the influence of unbalanced excitation is higher than that of traditional Luenberger observer. By using unbalance parameters instead of unbalanced excitation values as augmented variables, a Kalman filter based on the new augmented variable can achieve real-time reconstruction of rotor vibration over a large speed range.
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  Life Prediction of Planetary Gearbox Based on Competing Failure Mechanisms and Experimental Study

  WANG Hanming, DONG Qingbing, CHEN Zhuang, ZHAO Bo, SHI Xiujiang

  Journal of Mechanical Engineering. 2025, 61(3): 284-298. https://doi.org/10.3901/JME.2025.03.284

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  Gears are designed to carry out alternating loads during the transmission process, and the special structure of their involutes causes changes in the sliding/rolling ratio during the meshing process as well as changes in the friction performance caused by the morphology characteristics of the tooth surfaces, ultimately leading to contact fatigue or tooth root bending fatigue. The service life of planetary gear trains depends on the minimum life of various types of gears such as gear rings, planetary gears and sun gears under the competition of contact fatigue and bending fatigue. A finite element model is built up for a spur planetary geartrain, and the time-varying friction coefficient is taken into account. Critical plane methods based on the multi-axial fatigue criteria, such as Brown-Miller method, Fatemi-Socie method, Morrow method and Smith-Waston-Topper method are used to evaluate the fatigue crack initiation location and life distribution of competitive failure in planetary gear train. The residual stress from surface to core is fitted based on the experimentally measured hardness, and the fatigue parameters are then modified accordingly. Finally, fatigue tests are conducted for planetary gear trains under the same operating conditions to investigate the evolution process of surface pits on failed gears and verify the effectiveness and accuracy of the developed model. The results show that the contact fatigue strength of the gearbox is much lower than the bending fatigue strength. The Fatemi-Socie method has higher accuracy in evaluating the contact fatigue life of gears. The sun gear more likely suffers from contact fatigue compared with other gears due to its teeth meshing with different planetary gears in sequence, and pitting first occurs on the subsurface near the tooth root of the pitch line. The contact fatigue life and location considering residual stress are consistent with the experimental results. The modeling method developed in this study can provide insight into the life prediction of planetary gearboxes based on competitive failure fatigue, and the method can be used for the fatigue properties of other types of transmission components.
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  Optimization and Control of a Self-propelled Capsule Moving Small Intestine

  LIAO Maolin, LI Zhi, ZHU Jiapeng, WANG Zexu, JOSEPH Páez Chávez

  Journal of Mechanical Engineering. 2025, 61(3): 299-313. https://doi.org/10.3901/JME.2025.03.299

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  For gastrointestinal endoscopy, traditional wired endoscopy brings psychological burden and physical discomfort to patients, while capsule endoscopy faces challenges in achieving a comprehensive and efficient examination of the small intestine due to propulsion mechanisms and complexity of control. A small intestine-capsule coupling dynamics model is developed, in which both the flat and the narrow structures of the small intestine are considered. The dynamic behaviours of the self-propelled capsule moving in small intestine are analysed by considering the variable friction environment and the varying degrees of capsule wrapping on the capsule. Subsequently, the moving speed of capsule, energy consumption, and impact force acting on small intestine are set as the optimization objectives, the optimization algorithms (Six-Sigma + NSGA-II + Monte-Carlo) are combined to explore the optimized parameters for the controllable movement of the capsule. Finally, both the ADAMS simulations and the experimental tests are carried out to verify that the obtained optimization results can be used to control the self-propelled capsule to achieve the movement with a designed direction and speed, meanwhile both its energy consumption and the impact force can remain low levels.
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  Research on Dynamic-static Torque Sensing Method Based on Strain Measurement

  DONG Guangxu, LIN Ruishen, LUO Yajun, PENG Yuyang, YU Wenya, ZHANG Binghao, QIU Lan

  Journal of Mechanical Engineering. 2025, 61(3): 314-324. https://doi.org/10.3901/JME.2025.03.314

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  When torque sensors are connected between shafts, the precision of torque measurement will be reduced due to loading effect, and then the commonly used torque sensors can only test static torque or dynamic torque. In order to achieve high-precision measurement of static and dynamic torque, a dynamic-static torque sensing method based on strain measurement is proposed. Firstly, the torsional deformation model of shaft is established and the relationship between torque and rotational angle is deduced. The principal strain distribution of the torsional shaft is analyzed by finite element simulation, and the optimal location of strain measurement is determined. Four strain gauges are connected in the Wheatstone full-bridge manner to obtain the torsional principal strain signal under torsional loading, and the principal strain signal is modulated in the designed sensing system to realize the transformation from torsional principal strain to torque signal, which can realize the high precision measurement of static and dynamic torque. Finally, the experiments of static and dynamic torque are carried out based on the material testing machine (MTS) and friction test table, respectively. The results show that the test results of static torque are consistent with the theoretical torque and quasi-static loading torque of MTS, which possess high linearity, and the measurement error is less than 0.47%. Meanwhile, the experimental results of dynamic torque at rotational speeds of 60 r/min, 100 r/min, 200 r/min and 300 r/min have good agreement with the dynamic torque loaded by the friction testing machine, whose measurement error is less than 5%, which indicates that the low rotational speed has little influence on the dynamic torque measurement and proves the dynamic stability of the torque test. Therefore, the proposed dynamic-static torque sensing method based on strain measurement can realize the high precision measurement.
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  Research on the Relevant Meshing Theories and Meshing Performance of Elliptical Toroidal Worm Gears and Cylindrical Gears

  LING Siying, ZHANG Heng, YANG Zhenxiang, ZHANG Jiayang, LING Ming, WANG Fengtao

  Journal of Mechanical Engineering. 2025, 61(3): 325-336. https://doi.org/10.3901/JME.2025.03.325

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  The open-loop high-speed ratio and high-precision gear drive has important application value in the indexing system of high-end equipment and precision instruments, however, the involute enveloping toroidal worm (TI worm) gearing is influenced by the value of the helix angle, and there are issues of partial load and unable to participate in meshing with whole tooth flank in the transmission process, which cannot directly meet the indexing accuracy requirements of high-end equipment and precision instruments. To address these issues, firstly, a new form of elliptic toroidal worm (EI worm) gearing conjugated with the full tooth flank of involute gear is proposed, the spatial position relationship between the elliptic toroidal surface and the cylindrical surface of involute cylindrical gear is established, the generatrix equation of elliptic toroidal surface is derived, the relevant meshing theories of primary envelope elliptic toroidal worm is studied based on the principle of spatial conjugate mesh of gears. Secondly, by analyzing the influence of the meshing parameters on the instantaneous contact line, the distribution area of the instantaneous contact line on the gear tooth flank is optimized, the whole tooth flank of the cylindrical gear meshing with EI worm is realized. Finally, the influence of the meshing parameters on the induced normal curvature, lubrication angle and relative entrainment speed is analyzed. Under the ideal condition with the same parameters, by comparing the meshing area of gear and meshing performance of EI worm gearing and TI worm gearing, it is found that the meshing area of gear in EI worm gearing is 5.49 times larger than that of TI worm gearing, the induced normal curvature and lubrication angle are similar, and the relative entrainment speed is larger, so the EI worm gearing is of high fitting degree between conjugate tooth flank, high contact strength and good lubrication effect. The research results provided theoretical basis for high-performance worm gear gearing.
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  Research on Online Unbalance Identification for Ultra-high Speed Hybrid Gas Bearing-rotor System

  WANG Chenfei, WANG Xiaoli, ZHENG Chen

  Journal of Mechanical Engineering. 2025, 61(3): 337-346. https://doi.org/10.3901/JME.2025.03.337

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  Aiming at the problem that dynamic balancing method without trial weights neglects the bearing nonlinearity under the ultra-high speed, causing the low accuracy of multifaceted unbalance identification, an online unbalance identification method for hybrid gas bearing-rotor system considering the nonlinear gas film force is proposed. Firstly, a numerical solution termination method based on spectral amplitude is developed and a dynamic model of hybrid gas bearing-rotor system considering nonlinear transient gas film force is established to build an unbalance response database. Moreover, based on the hybrid genetic simulated annealing algorithm with both global optimization and local search capability, online unbalance identification of bearing-rotor systems under ultra-high speed is realized. The simulation and test results show that compared with the traditional time-period method as the termination condition of nonlinear dynamics calculation, the efficiency of the spectral amplitude method is greatly improved. Furthermore, compared with the unbalance identified by linear bearing force assumption, the online unbalance identification method considering the nonlinear gas film force improves the accuracy of unbalance identification, which can accurately identify the unbalance magnitude and phase of rotor system, and provide technical support for ultra-high speed online dynamic balance test.
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  Research Progress on Design, Manufacturing and Fatigue Properties of Lattice Structures

  GAO Zhuang, LIU Yuxin, ZHU Mingliang, XUAN Fuzhen

  Journal of Mechanical Engineering. 2025, 61(3): 347-375. https://doi.org/10.3901/JME.2025.03.347

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  Lightweight design is exceptionally needed in sectors including aerospace, automobile, and medical care because of its superiority in reducing energy consumption and enhancing performance of structures. Additive manufacturing can build complex structures by adding materials layer by layer. Today, the integration of lattice structure and additive manufacturing has shed new light on the design and manufacturing of high-performance lightweight structures. However, the lattice structure based on additive manufacturing is often subjected to cyclic loading in service and thus exposed to fatigue damage. The coordination between lightness and anti-fatigue property has yet been achieved in research and development of structures employed in the new generation of aerospace industry. In this review, an introduction of the methods utilized in design and manufacturing of lightweight structures is presented, and a summary of techniques in on the subject is given. After that, a review of the research on fatigue properties of additive-manufactured lattice structures is offered, and it is figured out that the integration of design and manufacturing is feasible to ensure the anti-fatigue property of these structures.
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  Research Progress on Design and Fabrication of Unidirectional Liquid Self-driven Transport Structure

  TANG Heng, XIE Yansong, SUN Yalong, WU Chunxia, TANG Yong

  Journal of Mechanical Engineering. 2025, 61(3): 376-391. https://doi.org/10.3901/JME.2025.03.376

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  Unidirectional liquid self-driven transport structure refers to the structure in which the liquid is able to move, spread or penetrate in a specific direction driven by the surface energy of the liquid without the action of external field. Many organisms in nature, such as cacti, pitcher plants, spiders, lizards, etc., can collect or discharge water with their unidirectional liquid self-driven transport structures to ensure their life activities. Because of no requirement of additional energy input, the unidirectional liquid self-driven transport structure is convenient to realize the lightweight of the equipment, and has a broad application prospect in medicine, chemical industry, energy, clothing and other fields. The principles of unidirectional liquid self-driven transport are introduced, the development and application of unidirectional liquid self-driven transport structure are reviewed, and the research progress of the design and fabrication of different unidirectional liquid self-driven transport structure is summarized. Finally, the development status and challenges of unidirectional liquid self-driven transport structures are summarized, and the future research is scientifically predicted and prospected.
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  The Influence Mechanism of Structural Parameters and Forming Angle on the Quality of Laser Powder Bed Fusion Inner Channel Forming

  GU Dongdong, SUN Jingjia, ZHU Qingjie, WANG Rui, YANG Jiahui, ZHANG Yuxi

  Journal of Mechanical Engineering. 2025, 61(3): 392-402. https://doi.org/10.3901/JME.2025.03.392

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  Laser additive manufacturing technology provides a new technological approach for the design and manufacturing of high-performance metal components in the internal flow channel of complex tail nozzles in the aerospace field. The complex and variable channel diameters and forming angles of internal channel components pose a serious challenge to the integrated forming of laser additive manufacturing. Using Hastelloy X (HX) powder as the raw material, a nickel based high-temperature alloy circular inner channel component is formed using laser powder bed fusion (LPBF) technology. The effects of channel diameter and forming angle on the forming quality of the inner channel are studied, and an optimized plan for LPBF forming of nickel based high-temperature composite inner channel components is provided. Research has shown that when the diameter of the flow channel increases and the forming angle approaches 90 °, the deviation in the contour size of the inner flow channel is relatively small, only 3.35%. When the diameter of the flow channel increases from 0.5 mm to 2.0 mm, the actual deviation value in the X direction of the inner flow channel decreases from 22% to 6.25%; The actual deviation value in the Y direction has decreased from 16% to 4.75%; When the forming angle is close to the vertical plane, the forming density of the component is increased (98.8%). When the forming angle increases from 0 ° to 90 °, the density of the inner channel increases from 96% to 98.8%, and the surface roughness Ra decreases from 73.46 μm to 16.11 μm.
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  Fabric-based Piezoresistive Flexible Pressure Sensor Jet Printing Molding

  XIAO Yuan, TONG Yao, YANG Leipeng, GUO Xinlei, GUO Dongyuan

  Journal of Mechanical Engineering. 2025, 61(3): 403-410. https://doi.org/10.3901/JME.2025.03.403

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  The flexible pressure sensor is an essential component of wearable smart textiles, which is the key to realizing the interaction between the human body and external information. As one critical ingredient of sensors, electrodes are significant in sensor signal transformation. Aiming at the existing fabric-based flexible pressure sensor electrode preparation process is complicated, with a low integration degree with fabric. The work proposes a method of forming metal electrodes on fabric surfaces using a combination of microdroplet spraying and chemical deposition techniques to realize the preparation of fabric-based flexible pressure sensors using polyester fabric impregnated with carbon nanotube solution as a piezoresistive layer. The prepared electrodes and piezoresistive layers were observed microscopically, and the performance and application of the prepared sensors are tested. The results show that the generating silver particles are homogeneous and coat the fabric surface, with an average sheet resistance of 0.029 Ω/sq for the fabric metal electrodes; there are voids between the fibers of the piezoresistive layer, which is compressible, and its surface resistance gradually decreases with the increase of the number of carbon nanotube solution dipping; the sensor can operate at 0 to 20 kPa, has a high sensitivity of 0.316 kPa^-1, good pressure and frequency response stability and repeatability. In addition, the sensor can provide clear feedback on finger pressing, finger bending, glottal knot vocalization, and music-playing signals, which have specific application prospects in motion monitoring and medical health.
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  Effect of Vehicle Vibration on Morphology, Microstructure and Properties of Carbon Steel Produced by Wire Arc Additive Manufacturing

  CAI Chengheng, KUANG Weifeng, LI Zhenhua, SHI Xuezhi

  Journal of Mechanical Engineering. 2025, 61(3): 411-421. https://doi.org/10.3901/JME.2025.03.411

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  At present, the application scope and prospects of the battlefield environment additive manufacturing and maintenance support system have become relatively clear. Wire arc additive manufacturing technology has the advantages of high deposition efficiency and strong environmental adaptability, which can quickly complete the repair work and enable equipment to be quickly put into use on the battlefield. However, how to perform real-time manufacturing and repair in a mobile vehicle environment remains a challenge. To investigate the potential of applying wire arc additive manufacturing to the manufacturing and repair of components on mobile platforms, a study was conducted under vehicle-induced vibrations ranging from 1.5 Hz to 5 Hz, focusing on the morphology, microstructure, and mechanical properties of low carbon steel specimens. The results indicate that with the increase in the vibration frequency of the vehicle platform, the acceleration of both the welding gun and the substrate continuously rises. This transition leads to a change from well-formed specimens to failed formations, with spattering and arc extinction becoming prevalent beyond 3 Hz. Correlation analysis reveals that the vibration of the welding gun has a more significant impact on the formation process compared to substrate vibration. Under steady-state conditions, the formed specimen exhibited tensile strength, yield strength, and elongation of 489 MPa, 385 MPa, and 0.34, respectively. In contrast, lower frequency vibrations contributed to grain size refinement and improved mechanical properties, achieving a maximum ultimate tensile strength of 501 MPa and maximum elongation of 0.35. However, higher frequency vibrations led to coarser grain structures, a slight reduction in mechanical properties, resulting in reduced tensile strength of 483 MPa and elongation of 0.31. Crucially, higher frequency vibrations caused deterioration in the appearance of the formed parts, hindering successful formation and ultimately failing to meet manufacturing requirements. Overall, this study provided a solid experimental foundation for realizing "anytime, anywhere" wire arc additive manufacturing in combat units.
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  Modeling and Multi-objective Optimization of Laser Cleaning Quality of 7075 Aluminum Alloy

  WANG Wei, LI Xiaoxu, LIU Weijun, BIAN Hongyou, XING Fei, WANG Jing

  Journal of Mechanical Engineering. 2025, 61(3): 422-439. https://doi.org/10.3901/JME.2025.03.422

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  In the process of laser cleaning, it has become a challenge to coordinate and control parameters to ensure efficient and high-precision cleaning quality due to the numerous energy and motion parameters involved in laser processing. To address these issues, a BOX Behnke design (BBD) response surface experiment is designed with process parameters (laser power, scanning speed, pulse frequency) as optimization variables, and surface roughness, oxygen removal rate, and static contact angle as multi-objective optimization indicators after cleaning. A response surface model and GA-BP neural network model are established. Particularly, a multi-objective sparrow algorithm based on the improvement of the good point set and adaptive normal distribution weights is proposed. Therefore, the problems of poor initial population quality, easy to fall into local optima, low population diversity in the later stages of iteration, and low local development ability have been solved. Furthermore, the model is optimized and the optimal process parameter combination obtained through TOPSIS is as follows: pulse frequency 2.6 kHz, laser power 245 W, and scanning speed 2 900 mm/s. In addition, comparative analysis and process experiments of multiple algorithms are conducted to verify the effectiveness of the algorithms. The results showed that the proposed model and algorithm are applied for optimization. As a result, compared with the original sample, the surface roughness is reduced by 40.26%, the oxygen content is reduced by 96.97%, the static contact angle is increased by 58.32%, and the cleaning quality was significantly improved.
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  Application of Lightweight Convolutional Neural Network Method Based on SERepVGG-A2 in Melt Pool State Recognition

  LIU Hanru, CHEN Guiying, XU Zelin, PENG Shitong, GUO Jianan, LIU Weiwei, WANG Fengtao

  Journal of Mechanical Engineering. 2025, 61(3): 440-448. https://doi.org/10.3901/JME.2025.03.440

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  A Quasi-Aiming at the problem that the traditional convolutional neural network is applied to the state recognition of the molten pool, the shallow convolutional neural network cannot effectively extract the characteristics of the molten pool, and the deep convolutional neural network has the problems of large number of parameters and high redundancy. A lightweight convolutional neural network (SERepVGG-A2) method is quantified. Firstly, in order to solve the problem of a large number of useless background areas in the original melt pool image, the region of interest (ROI) extraction of the melt pool image is performed to optimize the data set; then, in order to ensure the model feature extraction ability and reduce the complexity of the model, a lightweight convolutional neural network model combining residual structure, attention mechanism and structural parameter reorganization is proposed to identify the molten pool state. The proposed method is verified and illustrated with the online molten pool data of Ti-10Mo titanium alloy in the directed energy deposition process. The results show that the proposed lightweight network model can not only effectively identify the molten pool characteristics according to the molten pool signal, but also it has higher melting pool identification efficiency than traditional CNN methods.