Design is a high-level and complex thinking activity of human beings, using existing knowledge and technology to solve problems and create new things. With the rise and development of intelligent manufacturing, design has increasingly reflected its importance in the product life cycle. Firstly, the concept and connotation of complex product design is expounded systematically, and the different types of design are discussed. The four schools of design theory are introduced, including universal design, axiomatic design, TRIZ and general design. Then the research status of complex product design is analyzed, such as innovative design, digital design, modular design, reliability optimization design, etc. Finally, three key scientific issues worthy of research in the future are indicated, and five research trends of “newer, better, smarter, faster, and greener” are summarized, aiming to provide references for the equipment design and manufacturing industry.
Fishes have learned how to achieve outstanding swimming performance through the evolution of hundreds of millions of years, which can provide bio-inspiration for robotic fish design. The premise of designing an excellent robotic fish include fully understanding of fish locomotion mechanism and grasp of the advanced control strategy in robot domain. In this paper, the research development on fish swimming is presented, aiming to offer a reference for the later research. First, the research methods including experimental methods and simulation methods are detailed. Then the current research directions including fish locomotion mechanism, structure and function research and bionic robotic fish are outlined. Fish locomotion mechanism is discussed from three views: macroscopic view to find a unified principle, microscopic view to include muscle activity and intermediate view to study the behaviors of single fish and fish school. Structure and function research is mainly concentrated from three aspects: fin research, lateral line system and body stiffness. Bionic robotic fish research focuses on actuation, materials and motion control. The paper concludes with the future trend that curvature control, machine learning and multiple robotic fish system will play a more important role in this field. Overall, the intensive and comprehensive research on fish swimming will decrease the gap between robotic fish and real fish and contribute to the broad application prospect of robotic fish.
Multi-scale casting parts are important components of high-end equipment used in the aerospace, automobile manufacturing, shipbuilding, and other industries. Residual features such as parting lines and pouring risers that inevitably appear during the casting process are random in size, morphology, and distribution. The traditional manual processing method has disadvantages such as low efficiency, high labor intensity, and harsh working environment. Existing machine tool and serial robot grinding/cutting equipment do not easily achieve high-quality and high-efficiency removal of residual features due to poor dexterity and low stiffness, respectively. To address these problems, a five-degree-of-freedom (5-DoF) hybrid grinding/cutting robot with high dexterity and high stiffness is proposed. Based on it, three types of grinding/cutting equipment combined with offline programming, master-slave control, and other technologies are developed to remove the residual features of small, medium, and large casting parts. Finally, the advantages of teleoperation processing and other solutions are elaborated, and the difficulties and challenges are discussed. This paper reviews the grinding/cutting technology and equipment of casting parts and provides a reference for the research on the processing of multi-scale casting parts.
Initial residual stress is the main reason causing machining deformation of the workpiece, which has been deemed as one of the most important aspects of machining quality issues. The inference of the distribution of initial residual stress inside the blank has significant meaning for machining deformation control. Due to the principle error of existing residual stress detection methods, there are still challenges in practical applications. Aiming at the detection problem of the initial residual stress field, an initial residual stress inference method by incorporating monitoring data and mechanism model is proposed in this paper. Monitoring data during machining process is used to represent the macroscopic characterization of the unbalanced residual stress, and the finite element numerical model is used as the mechanism model so as to solve the problem that the analytic mechanism model is difficult to establish; the policy gradient approach is introduced to solve the gradient descent problem of the combination of learning model and mechanism model. Finally, the initial residual stress field is obtained through iterative calculation based on the fusing method of monitoring data and mechanism model. Verification results show that the proposed inference method of initial residual stress field can accurately and effectively reflect the machining deformation in the actual machining process.
The assembly process of aerospace products such as satellites and rockets has the characteristics of single- or small-batch production, a long development period, high reliability, and frequent disturbances. How to predict and avoid quality abnormalities, quickly locate their causes, and improve product assembly quality and efficiency are urgent engineering issues. As the core technology to realize the integration of virtual and physical space, digital twin (DT) technology can make full use of the low cost, high efficiency, and predictable advantages of digital space to provide a feasible solution to such problems. Hence, a quality management method for the assembly process of aerospace products based on DT is proposed. Given that traditional quality control methods for the assembly process of aerospace products are mostly post-inspection, the Grey-Markov model and T-K control chart are used with a small sample of assembly quality data to predict the value of quality data and the status of an assembly system. The Apriori algorithm is applied to mine the strong association rules related to quality data anomalies and uncontrolled assembly systems so as to solve the issue that the causes of abnormal quality are complicated and difficult to trace. The implementation of the proposed approach is described, taking the collected centroid data of an aerospace product’s cabin, one of the key quality data in the assembly process of aerospace products, as an example. A DT-based quality management system for the assembly process of aerospace products is developed, which can effectively improve the efficiency of quality management for the assembly process of aerospace products and reduce quality abnormalities.
Precision grinding is a key process for realizing the use of large-aperture aspherical optical elements in laser nuclear fusion devices, large-aperture astronomical telescopes, and high-resolution space cameras. In this study, the arc envelope grinding process of large-aperture aspherical optics is investigated using a CM1500 precision grinding machine with a maximum machinable diameter of Φ1500 mm. The form error of the aspherical workpiece induced by wheel setting errors is analytically modeled for both parallel and cross grinding. Results show that the form error is more sensitive to the wheel setting error along the feed direction than that along the lateral direction. It is a bilinear function of the feed-direction wheel setting error and the distance to the optical axis. Based on the error function above, a method to determine the wheel setting error is proposed. Subsequently, grinding tests are performed with the wheels aligned accurately. Using a newly proposed partial error compensation method with an appropriate compensation factor, a form error of 3.4 μm peak-to-valley (PV) for a Φ400 mm elliptical K9 glass surface is achieved.
The processed surface contour shape is extracted with the finite element simulation software. The difference value of contour shape change is used as the parameters of balancing surface roughness to construct finite element model of supersonic vibration milling in cutting stability domain. The surface roughness trial scheme is designed in the orthogonal test design method to analyze the surface roughness test result in the response surface methodology. The surface roughness prediction model is established and optimized. Finally, the surface roughness finite element simulation prediction model is verified by experiments. The research results show that, compared with the experiment results, the error range of the finite element simulation model is 27.5%–30.9%, and the error range of the empirical model obtained by the response surface method is between 4.4% and 12.3%. So, the model in this paper is accurate and will provide the theoretical basis for the optimization study of the auxiliary milling process of supersonic vibration.
Forming of various customized bending parts, small batches, as well as numerous types of materials is a new challenges for Industry 4.0, the current control strategies can not meet the precision and flexibility requirement, expected control strategy of bending processes need to not only resist unknown interferences of process condition and models, but also produce various new parts automatically and efficiently. In this paper, a precision and flexible bending control strategy based on analytical models and data models is proposed to build adaptive bending systems. New analytical prediction models for loading and unloading are established and suitable for various materials, a sequential identification strategy is proposed to search nominal properties using the four sub-optimization models. A data-based feedback model is established to prevent over-bending and eliminate online deviation. Above models are merged into a precision and flexible control strategy. The system firstly uses sub-optimization models to search the nominal point which is near to target point, secondly the system further uses feedback model to eliminate residual error between the nominal point and target point. Compared with four kinds sheet metals, the allowable ranges for variables are determined for a good convergence. The target bending angles were set to 20°, 40°, and 60°. Forty parts were tracked for each kind material, the adaptive bending system converged after one iteration, and exhibited better performances.
Measuring and reconstructing the shape of workpieces have been considered as a fundamental step in both reverse engineering and product quality control. Owing to increasing structural complexity of recent products, measurements from multiple directions are typically required in current scanning techniques. Specifically, the plane structured light can be applied to measure one area of a part at a time, with an additional algorithm required to merge the collected data of each area. Alternatively, the line structured light sensor integrated on CNC machines or CMMs could also realize multi-view measurement. However, the system needs to be repeatedly calibrated at each new direction. This paper presents a flexible scanning method by integrating laser line sensors with articulated arm coordinate measuring machines (AACMM). Since the output of the laser line sensor is 2D raw data in the laser plane, our system model introduces an explicit transformation from the 2D sensor coordinate frame to the 3D base coordinate frame of the AACMM (i.e., the translation and rotation the of the 2D sensor coordinate in the sixth coordinate system of AACMM). To solve the model, the “conjugate pairs” are proposed and identified by measuring a fixed point (e.g., a sphere center). Moreover, a search algorithm is adopted to find the optimal solution, which noticeably boosts the model accuracy. The experimental results show that the error of the system is about 0.2 mm, which is caused by the error of the AACMM, the sensor error and the calibration error. By measuring a complicated part, the proposed system is proved to be flexible and facilitate, with the ability to measure a part expediently from any necessary direction. Furthermore, the proposed calibration method can also be used for robot hand-eye relationship calibration.
The shallow subsurface defects are difficult to be identified and quantified by ultrasonic time-of-flight diffraction (TOFD) due to the low resolution induced by pulse width and beam spreading. In this paper, Sparse-SAFT is proposed to improve the time resolution and lateral resolution in TOFD imaging by combining sparse deconvolution and synthetic aperture focusing technique (SAFT). The mathematical model in the frequency domain is established based on the l1 and l2 norm constraints, and the optimization problem is solved for enhancing time resolution. On this basis, SAFT is employed to improve lateral resolution by delay-and-sum beamforming. The simulated and experimental results indicate that the lateral wave and tip-diffracted waves can be decoupled with Sparse-SAFT. The shallow subsurface defects with a height of 3.0 mm at the depth of 3.0 mm were detected quantitatively, and the relative measurement errors of flaw heights and depths were no more than 10.3%. Compared to conventional SAFT, the time resolution and lateral resolution are enhanced by 72.5 and 56% with Sparse-SAFT, respectively. Finally, the proposed method is also suitable for improving resolution to detect the defects beyond dead zone.
The inverse problem analysis method provides an effective way for the structural parameter identification. However, uncertainties wildly exist in the practical engineering inverse problems. Due to the coupling of multi-source uncertainties in the measured responses and the modeling parameters, the traditional inverse method under the deterministic framework faces the challenges in solving mechanism and computing cost. In this paper, an uncertain inverse method based on convex model and dimension reduction decomposition is proposed to realize the interval identification of unknown structural parameters according to the uncertain measured responses and modeling parameters. Firstly, the polygonal convex set model is established to quantify the epistemic uncertainties of modeling parameters. Afterwards, a space collocation method based on dimension reduction decomposition is proposed to transform the inverse problem considering multi-source uncertainties into a few interval inverse problems considering response uncertainty. The transformed interval inverse problem involves the two-layer solving process including interval propagation and optimization updating. In order to solve the interval inverse problems considering response uncertainty, an efficient interval inverse method based on the high dimensional model representation and affine algorithm is further developed. Through the coupling of the above two strategies, the proposed uncertain inverse method avoids the time-consuming multi-layer nested calculation procedure, and then effectively realizes the uncertainty identification of unknown structural parameters. Finally, two engineering examples are provided to verify the effectiveness of the proposed uncertain inverse method.
The manufacturing of spiral groove structure of two-dimensional valve (2D valve) feedback mechanism has shortcomings of both high cost and time-consuming. This paper presents a novel configuration of rotary electro-mechanical converter with negative feedback mechanism (REMC-NFM) in order to replace the feedback mechanism of spiral groove and thus reduce cost of valve manufacturing. In order to rapidly and quantitative evaluate the driving and feedback performance of the REMC-NFM, an analytical model taking leakage flux, edge effect and permeability nonlinearity into account is formulated based on the equivalent magnetic circuit approach. Then the model is properly simplified in order to obtain the optimal pitch angle. FEM simulation is used to study the influence of crucial parameters on the performance of REMC-NFM. A prototype of REMC-NFM is designed and machined, and an exclusive experimental platform is built. The torque-angle characteristics, torque-displacement characteristics, and magnetic flux density in the working air gap with different excitation currents are measured. The experimental results are in good agreement with the analytical and FEM simulated results, which verifies the correctness of the analytical model. For torque-angle characteristics, the overall torque increases with both current and rotation angle, which reaches about 0.48 N·m with 1.5 A and 1.5°. While for torque-displacement characteristics, the overall torque increases with current yet decrease with armature displacement due to the negative feedback mechanism, which is about 0.16 N·m with 1.5 A and 0.8 mm. Besides, experimental results of conventional torque motor are compared with counterparts of REMC-NFM in order to validate the simplified model. The research indicates that the REMC-NFM can be potentially used as the electro-mechanical converter for 2D valves in civil servo areas.
Existing models of bulk modulus for aerated hydraulic fluids primarily focus on the effects of pressure and air fraction, whereas the effect of temperature on bulk modulus is disregarded. Based on the lumped parameter method and the full cavitation model, combined with the improved Henry’s law and the air polytropic course equation, a theoretical model of dynamic bulk modulus for an aerated hydraulic fluid is derived. The effects of system pressure, air fraction, and temperature on bulk modulus are investigated using the controlled variable method. The results show that the dynamic bulk modulus of the aerated hydraulic fluid is inconsistent during the compression process. At the same pressure point, the dynamic bulk modulus during expansion is higher than that during compression. Under the same initial air faction and pressure changing period, a higher temperature results in a lower dynamic bulk modulus. When the pressure is lower, the dynamic bulk modulus of each temperature point is more similar to each other. By comparing the theoretical results with the actual dynamic bulk modulus of the Shell Tellus S ISO32 standard air-containing oil, the goodness-of-fit between the theoretical model and experimental value at three temperatures is 0.9726, 0.9732, and 0.9675, which validates the theoretical model. In this study, a calculation model of dynamic bulk modulus that considers temperature factors is proposed. It predicts the dynamic bulk modulus of aerated hydraulic fluids at different temperatures and provides a theoretical basis for improving the analytical model of bulk modulus.
Raising the rotational speed of an axial piston pump is useful for improving its power density; however, the churning losses of the piston increase significantly with increasing speed, and this reduces the performance and efficiency of the axial piston pump. Currently, there has been some research on the churning losses of pistons; however, it has rarely been analyzed from the perspective of the piston number. To improve the performance and efficiency of the axial piston pump, a computational fluid dynamics (CFD) simulation model of the churning loss was established, and the effect of piston number on the churning loss was studied in detail. The simulation analysis results revealed that the churning losses initially increased as the number of pistons increased; however, when the number of pistons increased from six to nine, the torque of the churning losses decreased because of the hydrodynamic shadowing effect. In addition, in the analysis of cavitation results, it was determined that the cavitation area of the axial piston pump was mainly concentrated around the piston, and the cavitation became increasingly severe as the speed increased. By comparing the simulation results with and without the cavitation model, it was observed that the cavitation phenomenon is beneficial for the reduction of churning losses. In this study, a piston churning loss test rig that can eliminate other friction losses was established to verify the accuracy of the simulation results. A comparative analysis indicated that the simulation results were consistent with the actual situation. In addition, this study also conducted a simulation study on seven and nine piston pumps with the same displacement. The simulation results revealed that churning losses of the seven pistons were generally greater than those of the nine pistons under the same displacement. In addition, regarding the same piston number and displacement, reducing the pitch circle radius of piston bores is effective in reducing the churning loss. This research analyzes the effect of piston number on the churning loss, which has certain guiding significance for the structural design and model selection of axial piston pumps.
Water-based lubrication is an effective method to achieve superlubricity, which implies a friction coefficient in the order of 10-3 or lower. Recent numerical, analytical, and experimental studies confirm that the surface force effect is crucial for realizing water-based superlubricity. To enhance the contribution of the surface force, soft and plastic materials can be utilized as friction pair materials because of their effect in increasing the contact area. A new numerical model of water-based lubrication that considers the surface force between plastic and elastic materials is developed in this study to investigate the effect of plastic flow in water-based lubrication. Considering the complexity of residual stress accumulation in lubrication problems, a simplified plastic model is proposed, which merely calculates the result of the dry contact solution and avoids repeated calculations of the plastic flow. The results of the two models show good agreement. Plastic deformation reduces the local contact pressure and enhances the function of the surface force, thus resulting in a lower friction coefficient.
Positioning and navigation technology is a new trend of research in mobile robot area. Existing researches focus on the indoor industrial problems, while many application fields are in the outdoor environment, which put forward higher requirements for sensor selection and navigation scheme. In this paper, a complete hybrid navigation system for a class of mobile robots with load tasks and docking tasks is presented. The work can realize large-range autonomous positioning and path planning for mobile robots in unstructured scenarios. The autonomous positioning is achieved by adopting suitable guidance methods to meet different application requirements and accuracy requirements in conditions of different distances. Based on the Bezier curve, a path planning scheme is proposed and a motion controller is designed to make the mobile robot follow the target path. The Kalman filter is established to process the guidance signals and control outputs of the motion controller. Finally, the autonomous positioning and docking experiment are carried out. The results of the research verify the effectiveness of the hybrid navigation, which can be used in autonomous warehousing logistics and multi-mobile robot system.
In the present study, the over-constrained hybrid manipulator R(2RPR)R/SP+RR is considered as the research objective. In this paper, kinematics of the hybrid manipulator, including the forward and inverse position, are analyzed. Then, the workspace is checked based on the inverse position solution to evaluate whether the workspace of the hybrid manipulator meets the requirements, and the actual workspace of the hybrid robot is analyzed. After that, the force analysis of the over-constrained parallel mechanism is carried out, and an ADAMS-ANSYS rigid-flexible hybrid body model is established to verify the simulation. Based on the obtained results from the force analysis, the manipulator structure is designed. Then, the structure optimization is carried out to improve the robot stiffness. Finally, calibration and workspace verification experiments are performed on the prototype, cutting experiment of an S-shaped aluminum alloy workpiece is completed, and the experiment verifies the machining ability of the prototype. This work conducts kinematics, workspace, force analyses, structural optimization design and experiments on the over-constrained hybrid manipulator R(2RPR)R/SP+RR, providing design basis and technical support for the development of the novel hybrid manipulator in practical engineering.
The kinematic redundancy is considered as a way to improve the performance of the parallel mechanism. In this paper, the kinematics performance of a three degree-of-freedom parallel mechanism with kinematic redundancy (3-DOF PM-KR) and the influence of redundant parts on the PM-KR are analyzed. Firstly, the kinematics model of the PM-KR is established. The inverse solutions, the Jacobian matrix, and the workspace of the PM-KR are solved. Secondly, the influence of redundancy on the PM-KR is analyzed. Since there exists kinematic redundancy, the PM-KR possesses fault-tolerant performance. By locking one actuating joint or two actuating joints simultaneously, the fault-tolerant workspace is obtained. When the position of the redundant part is changed, the workspace and singularity will be changed. The results show that kinematic redundancy can be used to avoid singularity. Finally, the simulations are performed to prove the theoretical analysis.
A novel compliant mechanism with RPR degrees of freedom (DOF) is proposed where R and P represent rotation and translation DOFs, respectively. The proposed compliant mechanism is obtained from dimension synthesizing a 2-RPU-UPR rigid parallel mechanism with the method of optimization of motion/force transfer characteristic. R, P and U represent rotation, translation and universal pairs, respectively. Firstly, inverse kinematics and Jacobian matrix are analyzed for the dimensional synthesis. Then, output transmission indexes of branches in the parallel mechanism are given. Dimensional synthesis is completed based on the normalized design parameter. And optimization of flexure joints based on constrained energy is carried out. Afterwards, the novel compliant mechanism is obtained by direct replacing method. Mechanical model of the compliant mechanism including static stiffness and input stiffness is built based on the pseudo-rigid body modeling method and virtual work principle. Finally, FEA simulation by Ansys Workbench is carried out to verify DOF, effectiveness of the dimension synthesis, and compliant model. Optimization of motion/force transfer characteristic is first applied for the design of compliant mechanisms to suppress drift of rotation axis in the paper.
Industrial robots are increasingly being used in machining tasks because of their high flexibility and intelligence. However, the low structural stiffness of a robot significantly affects its positional accuracy and the machining quality of its operation equipment. Studying robot stiffness characteristics and optimization methods is an effective method of improving the stiffness performance of a robot. Accordingly, aiming at the poor accuracy of stiffness modeling caused by approximating the stiffness of each joint as a constant, a variable stiffness identification method is proposed based on space gridding. Subsequently, a task-oriented axial stiffness evaluation index is proposed to quantitatively assess the stiffness performance in the machining direction. In addition, by analyzing the redundant kinematic characteristics of the robot machining system, a configuration optimization method is further developed to maximize the index. For numerous points or trajectory-processing tasks, a configuration smoothing strategy is proposed to rapidly acquire optimized configurations. Finally, experiments on a KR500 robot were conducted to verify the feasibility and validity of the proposed stiffness identification and configuration optimization methods.
For the development of a parallel mechanism (PM), it is necessary to establish a dynamic model which can accurately meet the requirements of real-time control. Compared with the general dynamic analysis model based on the inverse kinematics, the dynamic analysis model based on the forward kinematics has the advantage of low-complexity. In this paper, a new type of 3-DOF PM with analytical forward displacement analysis is proposed. Different from the general dynamic analysis based on the inverse kinematics, the displacement, velocity and acceleration equations of the PM are established and solved by forward kinematics. The inverse dynamic equation of the PM is constructed and solved by analyzing the forces on each link and based on Newton-Euler method. Then the theoretical results of an example are compared with the simulation results, which shows that the simulation results are basically consistent with the theoretical results. And the maximum error of the driving force of each pair is 1.32%, 5.8% and 5.2%, respectively, which verifies the correctness of the dynamic model. The PM has a potential application prospect in the grasping, spraying and picking of workpieces. The research results of this paper provide a theoretical basis for the design, manufacture and application of the PM.
Autonomous vehicles require safe motion planning in uncertain environments, which are largely caused by surrounding vehicles. In this paper, a driving environment uncertainty-aware motion planning framework is proposed to lower the risk of position uncertainty of surrounding vehicles with considering the risk of rollover. First, a 4-degree of freedom vehicle dynamics model, and a rollover risk index are introduced. Besides, the uncertainty of surrounding vehicles' position is processed and propagated based on the Extended Kalman Filter method. Then, the uncertainty potential field is established to handle the position uncertainty of autonomous vehicles. In addition, the model predictive controller is designed as the motion planning framework which accounts for the rollover risk, the position uncertainty of the surrounding vehicles, and vehicle dynamic constraints of autonomous vehicles. Furthermore, two edge cases, the cut-in scenario, and merging scenario are designed. Finally, the safety, effectiveness, and real-time performance of the proposed motion planning framework are demonstrated by employing a hardware-in-the-loop experiment bench.
There is an increasing awareness of the need to reduce traffic accidents and fatality due to vehicle collision. Post-impact hazards can be more serious as the driver may fail to maintain effective control after collisions. To avoid subsequent crash events and to stabilize the vehicle, this paper proposes a post-impact motion planning and stability control method for autonomous vehicles. An enabling motion planning method is proposed for post-impact situations by combining the polynomial curve and artificial potential field while considering obstacle avoidance. A hierarchical controller that consists of an upper and a lower controller is then developed to track the planned motion. In the upper controller, a time-varying linear quadratic regulator is presented to calculate the desired generalized forces. In the lower controller, a nonlinear-optimization-based torque allocation algorithm is proposed to optimally coordinate the actuators to realize the desired generalized forces. The proposed scheme is verified under comprehensive driving scenarios through hardware-in-loop tests.
The locomotive traction motor is described as a rotor-bearing system coupling the kinetic equations of the traction shaft and its support bearings with the determination of their elastic deformations in this study. Under the effect of excitations induced by the dynamic rotor eccentric distance and time-varying mesh stiffness, the elastic structure deformations of the shaft and support bearings are formulated in the vibration environment of the locomotive. In addition, the nonlinear contact forces between the components of the rolling bearing, the lubricating oil film, and radial clearance are comprehensively considered in this study. The results indicate that the elastic deformations of the shaft and bearings can change the dynamic responses of the traction motor and its support bearings. There are large differences between the ranges of the rotor motion calculated by the rigid and the flexible traction motor models when the intensified wheel-rail interaction is considered. With the increase of the rotor eccentricity, the results underscore the role of the elasticity of traction shaft and support bearings in dynamic researches of the traction motor. The critical value of the initial eccentric distance for the rub-impact phenomenon decreases from 1.23 mm to 1.15 mm considering the flexible effect of the shaft and bearings. This dynamics model of the traction motor can provide more accurate and reasonable simulation results for correlational dynamic researches.
High-speed trains often use temperature sensors to monitor the motion state of bearings. However, the temperature of bearings can be affected by factors such as weather and faults. Therefore, it is necessary to analyze in detail the relationship between the bearing temperature and influencing factors. In this study, a dynamics model of the axle box bearing of high-speed trains is established. The model can obtain the contact force between the rollers and raceway and its change law when the bearing contains outer-ring, inner-ring, and rolling-element faults. Based on the model, a thermal network method is introduced to study the temperature field distribution of the axle box bearings of high-speed trains. In this model, the heat generation, conduction, and dispersion of the isothermal nodes can be solved. The results show that the temperature of the contact point between the outer-ring raceway and rolling-elements is the highest. The relationships between the node temperature and the speed, fault type, and fault size are analyzed, finding that the higher the speed, the higher the node temperature. Under different fault types, the node temperature first increases and then decreases as the fault size increases. The effectiveness of the model is demonstrated using the actual temperature data of a high-speed train. This study proposes a thermal network model that can predict the temperature of each component of the bearings on a high-speed train under various speed and fault conditions.
There may be several internal defects in railway track work that have different shapes and distribution rules, and these defects affect the safety of high-speed trains. Establishing reliable detection models and methods for these internal defects remains a challenging task. To address this challenge, in this study, an intelligent detection method based on a generalization feature cluster is proposed for internal defects of railway tracks. First, the defects are classified and counted according to their shape and location features. Then, generalized features of the internal defects are extracted and formulated based on the maximum difference between different types of defects and the maximum tolerance among same defects’ types. Finally, the extracted generalized features are expressed by function constraints, and formulated as generalization feature clusters to classify and identify internal defects in the railway track. Furthermore, to improve the detection reliability and speed, a reduced-dimension method of the generalization feature clusters is presented in this paper. Based on this reduced-dimension feature and strongly constrained generalized features, the K-means clustering algorithm is developed for defect clustering, and good clustering results are achieved. Regarding the defects in the rail head region, the clustering accuracy is over 95%, and the Davies-Bouldin index (DBI) index is negligible, which indicates the validation of the proposed generalization features with strong constraints. Experimental results prove that the accuracy of the proposed method based on generalization feature clusters is up to 97.55%, and the average detection time is 0.12 s/frame, which indicates that it performs well in adaptability, high accuracy, and detection speed under complex working environments. The proposed algorithm can effectively detect internal defects in railway tracks using an established generalization feature cluster model.
In this work, high-manganese aluminium bronze CuMn13Al7 samples were prepared by arc additive manufacturing technology. The phase composition, microstructure, and crystal structure of the high-manganese aluminium bronze CuMn13Al7 arc additive manufactured samples were analysed using direct-reading spectrometer, metallographic microscope, scanning electron microscope, and transmission electron microscope. The micro-hardness tester, tensile tester, impact tester, and electrochemical workstation were also used to test the performance of the CuMn13Al7 samples. By studying the microstructure and properties of the CuMn13Al7 samples, it was found that preparation of the samples by the arc additive manufacturing technology ensured good forming quality, almost no defects, and good metallurgical bonding inside the sample. The metallographic structure (α + β + point phase) mainly comprises the following: the metallographic structure in the equiaxed grain region has an obvious grain boundary α; the metallographic structure in the remelting region has no obvious grain boundary α; the thermal influence on the metallographic structure produced a weaker grain boundary α than the equiaxed grain region. The transverse and longitudinal cross sections of the sample had uniform microhardness distributions, and the average microhardness values were 190.5 HV0.1 and 192.7 HV0.1, respectively. The sample also had excellent mechanical properties: yield strength of 301 MPa, tensile strength of 633 MPa, elongation of 43.5%, reduction of area by 58%, Charpy impact value of 68 J/cm2 at – 20 ℃, and dynamic potential polarisation curve test results. Further, it was shown that the average corrosion potential of the sample was – 284.5 mV, and the average corrosion current density was 4.1×10–3 mA/cm2.
For hypereutectic Nb-Si based alloys, primary Nb5Si3 phases typically grow in a faceted mode during equilibrium or near-equilibrium solidification, which damages the ductility and toughness. To address this issue, here we artificially manipulate the growth morphology of Nb5Si3 using electron beam surface melting (EBSM) and subsequent annealing treatments. Results show that such a non-equilibrium solidification pathway enables the transition from faceted growth to non-faceted dendritic growth of Nb5Si3, along with evident microstructure refinement, generation of metastable β-Nb5Si3 phases and elimination of chemical segregation. The transformation from β-Nb5Si3 to α-Nb5Si3 and Nb solid solution (Nbss) particles is triggered by the annealing treatment at 1450 ℃ for 5 h. Also, we find the annealing-mediated formation of inherited Nb5Si3 dendrites that maintain the dendritic morphology of the original as-solidified β-Nb5Si3 dendrites. This work thus provides a feasible routine to obtain thermally stable and refined α-Nb5Si3 dendrites in hypereutectic Nb-Si based alloys.