Explore the vast range of titles available.

Characterized by high rigidity and precision, large working space, and reconfigurability, hybrid kinematic machines are widely used in the five-axis machining of large parts in situ. The feedrate is limited by the velocity, acceleration, and jerk of actuated joints in high-speed machining due to the nonlinear motion introduced by the use of revolute joints and parallel kinematic module. To achieve a good balance between the machining accuracy and efficiency, an offline feedrate-scheduling algorithm considering the drive constraints of a five-axis hybrid machine is proposed. By adding a dimension of the curve parameter, the feedrate profile expressed by a cubic uniform B-spline is mapped into a two-dimensional curve with the redefined control points. Then, the feedrate-scheduling process is completed by iteratively modulating the control points of feedrate profile. The velocity, acceleration, and jerk of actuated joints are calculated by the kinematic analysis for a dual non-uniform rational basis spline (NURBS) toolpath. Based on this, the feedrate constraint equations are derived considering the geometry and drive constraints. The scheduled feedrate profile remains constant in most parameter intervals, while it changes smoothly in transition intervals without violating constraints. Simulations and experiments are carried out on the TriMule600/800 machining platform, and the results validate the correctness and effectiveness of the proposed algorithm.

During milling of the thin floor of a pocket which is located on a monolithic structure, it is very difficult to achieve high accuracy and good surface quality due to its low rigidity. In this paper, a novel approach is proposed to overcome the difficulties that are encountered during milling of the thin floor. The method is realized by using a small axial depth of cut but placing a moving support at the back surface of the thin floor during machining, in which the support will move with the cutter at the same velocity. An experimental platform is built to demonstrate the validity of the proposed method. The experimental results show that the proposed method can effectively improve the accuracy and surface quality of the thin floor of a pocket on the monolithic structure.

Large thin-walled parts are widely utilized in many different fields such as aircraft manufacturing. However, the vibration caused by the milling force during processing unfortunately affects the processing quality. To reduce the vibration, a multi-point flexible following support head is proposed, which can adjust the supporting force in mirror milling in real time. By installing the milling cutter and support head at the end of the milling robot and support robot, respectively, and allowing them to symmetrically move during processing, the vibration of the thin-walled parts can be suppressed. In this study, the supporting force equations of the multi-point flexible following support head are established. Then, the vibration response of the thin-walled part with two clamped edges and two free edges (CCFF plate) under alternating external loads is analyzed, and the simulation model of the mirror milling vibration is established. The simulation is carried out under the milling condition with and without the support head to verify the vibration suppression effect of the support head. Finally, the mirror milling experiment verifies that the multi-point flexible following support head can decrease the amplitude by 98% compared with milling without a support head, and the processing quality is significantly improved.

To mitigate the detrimental effects of joint elasticity and transmission errors on contour accuracy and to improve the multi-axis motion performance of hybrid robots, this study investigates contour error modeling and control by leveraging additional grating sensors for real-time measurements. Accounting for the inherent pose coupling characteristics of hybrid robots, a novel contour error modeling method is proposed that employs six-dimensional exponential coordinates for error description and incorporates an efficient search algorithm for foot point determination. Building upon an existing grating sensor feedback control framework, a proportional contour controller is developed. Experimental validation on the TriMule-200 hybrid robot demonstrates an enhancement in end-effector contour accuracy.

As critical components of aircraft skins and rocket fuel storage tank shells, large thin-walled workpieces are susceptible to vibration and deformation during machining due to their weak local stiffness. To address these challenges, we propose a novel tunable electromagnetic semi-active dynamic vibration absorber (ESADVA), which integrates with a magnetic suction follower to form a followed ESADVA (follow-ESADVA) for mirror milling. This system combines a tunable magnet oscillator with a follower, enabling real-time vibration absorption and condition feedback throughout the milling process. Additionally, the device supports self-sensing and frequency adjustment by providing feedback to a linear actuator, which alters the distance between magnets. This resolves the traditional issue of being unable to directly monitor vibration at the machining point due to space constraints and tool interference. The frequency shift characteristics and vibration absorption performance are comprehensively investigated. Theoretical and experimental results demonstrate that the prototyped follow-ESADVA achieves frequency synchronization with the milling tool, resulting in a vibration suppression rate of approximately 47.57%. Moreover, the roughness of the machined surface decreases by 18.95%, significantly enhancing the surface quality. The results of this work pave the way for higher-quality machined surfaces and a more stable mirror milling process.

This paper proposes an improved data-driven calibration method for a six degrees of freedom (DOF) hybrid robot. It focuses mainly on improving the measurement efficiency and practicability of existing data-driven calibration methods through the following approaches. (1) The arbitrary motion of the hybrid robot is equivalently decomposed into three independent sub-motions by motion decomposition. Sequentially, the sub-motions are combined according to specific motion rules. Then, a large number of robot poses can be acquired in the whole workspace via a limited number of measurements, effectively solving the curse of dimensionality in measurement. (2) A mapping between the nominal joint variables and joint compensation values is established using a back propagation neural network (BPNN), which is trained directly using the measurement data through a unique algorithm involving inverse kinematics. Thus, the practicability of data-driven calibration is significantly improved. The validation experiments are carried out on a TriMule-200 robot. The results show that the robot’s maximal position/orientation errors are reduced by 91.16%/88.17% to 0.085 mm/0.022 deg, respectively, after calibration.

The discontinuity of tangent, curvature, and curvature derivative of the 5-axis toolpath consisting of line and arc segments defined by the G01/G02/G03 commands limits the performance of hybrid robot in high-efficiency and high-precision milling machining. To guarantee the high-order continuous and smooth machining, a C 3 continuous corner smoothing 5-axis toolpath containing spatial arc segments is semi-analytically generated. Firstly, the control points of the smoothing quintic B-spline for line and arc segments are constructed analytically based on the G 3 continuity conditions. Within the constraints of the established explicit smoothing error expressions, the G 3 continuous smoothing for three kinds of tooltip positions, namely, spatial line-line pairs, arc-arc pairs, and line-arc pairs, is investigated. Meanwhile, the smoothing error model of tool orientation is analytically established. Then, the parameter synchronization of the tool orientation at the junction following the tooltip position is achieved by replacing the remaining segments with seven B-spline curves as synchronization curves. Finally, simulations and experiments with the typical 5-axis toolpath containing spatial arc segments are carried out to verify the validity of the proposed method in ensuring C 3 continuous motion in the robot operation space and good following performance in the joint space.

This work uses numerical methods to investigate the hydrodynamic forces that act on a turbine ring gate during emergency shutdown. Accurately predicting the hydrodynamic forces on such gates is important because the forces constitute a design criterion of ring gates, especially in the case of the downward-pulling hydrodynamic force, which directly affects the opening or closing of the gate. In this article, we use Fluent, which is a computational fluid dynamics code, to simulate the flow patterns around the ring gate and to calculate the hydrodynamic forces on the gate. We numerically simulate the three-dimensional unsteady turbulence over the entire flow passage of the turbine. The numerical results show that the hydrodynamic forces are induced mainly by flow acceleration under the bottom surface of the ring gate. When the ring gate is at 90% closing position, the downward-pulling hydrodynamic force is maximal; near the completely closed position, the hydrodynamic force is more sensitive to the position of the ring gate. This investigation of the hydrodynamic forces that act on the ring gates provides the theoretical basis for determining the operational capacity and the optimal design of ring gates.

This paper presents a systematic approach for estimating the forces applied to tools during friction stir welding (FSW), which can be implemented by the following two steps: (1) computing the temperature field of metal sheets through numerical simulation and (2) substituting the obtained thermal cycles of several points on the metal sheets into an analytical model of tool forces. The novelty of this approach lies in that it ingeniously combines the numerical simulation model of the temperature field and the analytical model of tool forces. In this case, the axial force, traverse force, and torque can be easily determined by only measuring temperature at a specific location underneath the sheets, thus avoiding time-consuming numerical simulations of large plastic deformations of the metal sheet material and expensive force sensors. After a brief description of the tool forces modelling strategy, the numerical simulation modelling of the temperature field and the analytical modelling of tool forces are introduced in detail. Then, an illustrative example comprising the simulation and corresponding experiment is carried out to demonstrate the effectiveness of the proposed approach. The consistency between the simulation and experimental values of the tool forces demonstrates the great potential of the proposed method in tool force prediction and other FSW applications.

In the automatic polishing process, the wear of polishing tools and the change of polishing parameters will affect the Preston coefficient, which makes it difficult to establish an accurate material removal model to achieve stable and excellent polishing quality. In this paper, a robotic polishing parameter optimization method considering time-varying wear is proposed to address these issues. First, combining the rich information in the theoretical modeling method with the data-driven regression method, a material removal regression model incorporating prior knowledge is proposed, which greatly reduces the large amount of experimental data required by the original regression model. The proposed model is able to track the wear variation of the sandpaper as well as the effect of polishing parameters. Then, based on the proposed prediction model, the genetic algorithm is used to optimize the polishing parameters in order to achieve better machining quality and less energy consumption. Finally, the experimental verification is carried out on the hybrid robot polishing test bench. The results show that the proposed material removal regression model incorporating prior knowledge has higher prediction accuracy and less required experimental data than existing models. The proposed robot polishing parameter optimization method can effectively compensate for tool wear and ensure the consistency of material removal during polishing while reducing energy consumption.