Robotic Polishing Research Articles

Finite-time SMC-based admittance controller design of macro-micro robotic system for complex surface polishing operations

In the field of robotic polishing, achieving uniform material removal typically involves addressing the issue of constant contact force control. However, multi-source external disturbances in the polishing scenarios of complex workpiece surfaces can severely affect the robot’s force control accuracy. To enhance the responsiveness and disturbance rejection capabilities of robots in the compliant polishing process, this paper proposes an adaptive admittance controller with practical finite-time stability. A virtual control input is introduced into the basic admittance control framework in light of the state space theory, aiming to provide flexibility for common adaptive law designs. On this basis, a robust sliding mode control (SMC) algorithm is proposed to suppress external disturbances. The force tracking error is theoretically proven to achieve finite-time convergence when applying the proposed control strategy. Experimental results across various polishing scenarios demonstrate that, compared with the existing admittance control strategies, the proposed method can reduce fluctuations of the polishing force and improve the surface quality, thus verifying its effectiveness.

A non-destructive measurement method for thermal barrier coating thickness in a robot polishing process based on point cloud processing

The adhesion state between the bond coat and top coat of thermal barrier coatings (TBCs) is influenced by the surface roughness of the bond coat. Under certain operating conditions, polishing is employed to reduce the surface roughness of the bond coat, thereby enhancing the adhesion strength between the top coat and bond coat and improving the TBC’s resistance to high-temperature oxidation. In order to accurately control the thickness of the bond coat during the polishing process, this study proposes a non-destructive measurement method for coating thickness based on point clouds. By preprocessing, registering, and calculating the distance between the point cloud generated by laser scanning the turbine blade, the thickness of the bond coat was determined. The method exhibits greater accuracy in regions with small curvature variations. Compared to measurements obtained using a metallographic microscope, the maximum thickness deviation between the two methods is 4.004 μm, and the relative error is less than 5%. Experimental results demonstrate that this method can accurately measure coating thickness, offering advantages such as real-time processing, flexibility in application scenarios, high measurement efficiency, and relatively low requirements for coating materials.

Design and Optimization of a Cable-Driven Parallel Polishing Robot With Kinematic Error Modeling

Abstract This article presents the design and optimization of a cable-driven parallel polishing robot (CDPPR) with kinematic error modeling and introduces an improved nondominated sorting genetic algorithm II (NSGA-II) for multiobjective optimization. First, the mechanical design and kinematic and static modeling of the CDPPR are conducted. Subsequently, a kinematic error transfer model is established based on the evidence theory by considering the change of exit points of cables, and an error index is derived to measure the accuracy of the robot. Besides, another two performance indices including the workspace and static stiffness are proposed. Thus, a multiobjective optimization model is established to optimize the workspace, static stiffness, and error index, and an improved NSGA-II is developed. Finally, an experimental scaled prototype of the CDPPR is constructed, and numerical examples and experimental results demonstrate the effectiveness of the improved NSGA-II and the stability of the optimal configuration.

The parameters optimization of robotic polishing with force controlled for mold steel based on Taguchi method

The parameters optimization of robotic polishing with force controlled for mold steel based on Taguchi method

An Investigation of Real-Time Robotic Polishing Motion Planning Using a Dynamical System

When addressing the technical challenges of achieving precise force tracking during the local polishing process of polishing robots, controlling the contact state between the robot and the workpiece surface is essential. To this end, a contact motion-planning strategy based on dynamic systems is designed to generate trajectory routes during local polishing. The trajectory simulation of the local modulation dynamic system is achieved through the employment of the support vector regression (SVR) algorithm with a Gaussian kernel, facilitating the learning process. The feasibility and stability of planning local paths are validated using the local modulation dynamic system. To maintain a constant contact force between the end-effector polishing robot and the workpiece, an integral adaptive impedance control strategy is utilized, enabling the robot’s compliant control. Subsequently, an experimental system for the polishing robot is constructed in order to verify the effectiveness of the motion-planning system. The experimental results demonstrate that the proposed motion-planning approach is applicable in practical polishing processes, ensuring smooth contact and maintaining the desired contact force when scanning nonlinear surfaces, and thus showcasing stability and practicality.

An automatic robot polishing control method for compound surface comprising plane and curved surfaces

An automatic robot polishing control method for compound surface comprising plane and curved surfaces

Direct trajectory optimization of macro-micro robotic system using a Gauss pseudospectral framework

Trajectory planning is a crucial aspect of macro-micro robotic systems (MMRSs), especially when the system has high degrees of freedom (DOFs). In the field of robotic polishing, the MMRS is usually composed of an industrial robot and an end-effector, which is responsible for polishing force control. Therefore, the compliance of the macro-robot can be minimized by trajectory planning to reduce its impact on the micro-robot. This study proposes a trajectory planning strategy based on Gauss pseudospectral method for a 9-DOF MMRS. Different from traditional sequential solution strategies, it can be used to obtain an approximate global optimal trajectory. Firstly, the velocity-level kinematics model of MMRS is built, which comprehensively considers the workpiece placement pose and task redundancy. Secondly, an optimal control model for trajectory planning is developed through an effective variable allocation. On the premise of considering traditional trajectory smoothness constraints, a constraint on manipulability is additionally analyzed to avoid reaching a singular configuration during compliance optimization. Thirdly, a Gauss pseudospectral framework based on the optimal control model is proposed, and the costate mapping theorem is proved. The latter provides a theoretical basis for the efficiency and accuracy of the proposed method. Finally, comparison results demonstrate the effectiveness of the proposed method.

Optimization of robotic polishing process parameters for mold steel based on artificial intelligence method

Aimed to achieve quantitative control of workpiece surface after robotic polishing and improve polishing efficiency, a two-step processing optimization method involves artificial intelligence algorithms is investigated. Firstly, based on XGBoost algorithm, a prediction model for polished workpiece surface depending on key parameters is proposed, and the accuracy of the model is verified by experiments. After that, by using the above model, the influence of each parameter on the roughness was evaluated quantitatively. Subsequently, target roughness-driven optimization of processing parameters was presented by combining the roughness prediction model with NSGA II-TOPSIS algorithm based on the influence of each parameter on the roughness. To verify the proposed processing optimization method, polishing experiments of mold steel samples were conducted. The experimental results show that the maximum absolute error between the predicted and experimental roughness is 0.035 μm, and the maximum relative error is <9%. At the same time, when the minimum is set as the optimization objective. With the same length of polishing path, the feed rate is increased from 0.25 mm/s to 0.37 mm/s, and the efficiency is improved to 48%. The NSGA II-TOPSIS algorithm can achieve quantitative control of mold steel surface roughness after robotic polishing to improve polishing efficiency, and provide a basis for reasonable selection of processing parameters, which have certain practical value.

Surface Performance of Titanium Alloy Brake Shell Polished by Industrial Robot Based on Digital Twin

The titanium alloy brake shell is an important component used in aviation, but its surface polishing is mostly done manually, making it difficult to ensure surface quality and consistency. As a result, an industrial robot polishing system based on digital twin is proposed, which can realize the interaction between physical and virtual platforms by using digital twin technology, acquire various parameters in real time, and monitor the polishing process. Based on this system, a removal depth model was established, and the polishing parameters to be analyzed were determined by combining the removal depth model. On this basis, the influence law of polishing parameters on surface roughness is analyzed through physical tests, and orthogonal experiments are used to optimize the polishing parameters. The results show that the surface roughness is reduced to 0.171 μm after optimization. Finally, the reliability of the polishing system is verified through the polishing machining test, and the surface quality of titanium alloy brake shell is significantly improved.

Contact Force Control of Robot Polishing System Based on Vision Control Algorithm

Contact Force Control of Robot Polishing System Based on Vision Control Algorithm

Autonomous robotic polishing of free-form poly-surfaces: planning from scanning in realistic industrial setting

We propose a novel solution for generating robot tool paths to achieve uniform surface polishing of free-form poly-surfaces, addressing the limitations of imperfect surface reconstruction in real industrial settings and of abruptly varying curvatures. Existing approaches assume perfect 3D surface knowledge and overlook pieces with abrupt changes in principal curvatures, leading to unsatisfactory outcomes like non-smooth paths and incomplete coverage. Our research aims to develop an algorithm that adapts the polishing path for uniform coverage despite imperfect reconstruction or aggressive varying curvatures of the objects to be polished. Users can control the path points density, overlapping, edge proximity, and scanning direction. By segmenting the surface based on curvature similarity and connecting the separately computed trajectories, we minimize polishing time and achieve optimal polishing quality of free-form poly-surfaces in real industrial settings.

The Real-Time Local Surface Model Construction Method of Unknown-Model Workpieces for Robotic Polishing

The Real-Time Local Surface Model Construction Method of Unknown-Model Workpieces for Robotic Polishing

Monitoring robot machine tool sate via neural ODE and BP-GA

Tool wear during robotic polishing affects material removal rates and surface roughness, leading to erratic and inconsistent polishing quality. Therefore, a method that can predict the tool state is needed to replace the robot end tool in time. In this paper, based on the cutting-edge neural ordinary differential equations (Neural ODE) and BP neural network optimization based on genetic algorithm (BP-GA), we propose a method to identify the tool state during robotic machining: firstly, a new training method of Neural ODE is proposed to avoid the model from falling into poor stationary points, and then on this basis, Neural ODE is utilized to predict the changes of vibration signals during robot machining; secondly, the predicted vibration signals of the tool are processed using variable modal decomposition method to extract the eigen kurtosis index and envelope entropy of the modal function as the vibration signal eigenvectors, and compare them with the traditional vibration signal eigenvectors. Finally, the predicted tool states were identified using BP-GA, and numerical experiments yielded an F1 score of 91.76% and an accuracy of 96.55% for model identification.

Active Disturbance Rejection Control Algorithm for the Driven Branch Chain of a Polishing Robot

To overcome poor error suppression performance and low control accuracy in the polishing robot-driven branch chain control system, this paper proposes an improved active disturbance rejection control (ADRC) from the design of the derived nonlinear function. Subsequently, the tracking differentiator (TD), extended state observer (ESO) and nonlinear state error feedback (SEF) are designed in the ADRC, and the driven branch’s ADRC servo-control system is established based on the permanent magnet synchronous motor (PMSM) with each driven branch. Meantime, by establishing first-order and second-order ADRC, current-loop control, and speed-and-position-loop control are realized, respectively. Finally, this study analysed differences in the speed and motor rotor error performance between the proportional-integral-derivative (PID)control and ADRC control strategy by using Simulink. Furthermore, an experiment platform, including hardware and software, is built to validate some inclusions. The results show that the ADRC not only realises high-precision trajectory tracking control but also ensures the rapid response performance of the system.

A Methodology to Support Robotic Polishing of Molds Integrated with CAD/CAM

Typically, the final process in the manufacture of molds is mechanical polishing, a process predominantly conducted manually. This is an important process step to improve the surface quality of the mold without affecting the geometry of the cavity. Currently, the European mold manufacturing industry faces two challenges for the polishing phase of manufacture. Firstly, economics when compared to global low-cost production areas. Secondly, the industry is starting to suffer the loss of skilled benchmen specialized in performing such operations. To address these two problems, robotic polishing has been investigated as an alternative or supplement to the conventional manual polishing process in the manufacturing of molds because of its high efficiency, improved reliability, and robustness. This paper presented a new methodology that allows robotic polishing to replace majority of manual polishing work for the manufacture of molds and achieves complete information integration with current CAD/CAM systems. Based on a number of experimental works, the types of features that are suitable for robotic polishing have been identified and a new robotic polishing feature classification has been proposed. A new polishing process planning has been developed to be integrated with current CAD/CAM systems, which includes a robotic polishing process knowledge database; a polishing process selection method; and a polishing process sequencing using genetic algorithm aiming to combine minimum polishing time and the specific constraints and rules for polishing process sequencing. Finally, a case study based on the proposed methods has been given, which demonstrates the proposed methodology is able to be implemented in the practical environment to allow robotic polishing to take majority of manual polishing workloads for mold manufacturing and achieve information integration with current CAD/CAM systems.

Plug-and-play positioning error compensation model for ripple suppressing in industrial robot polishing.

The industrial robot-based polisher has wide applications in the field of optical manufacturing due to the advantages of low cost, high degrees of freedom, and high dynamic performance. However, the large positioning error of the industrial robot can lead to surface ripple and seriously restrict the system performance, but this error can only be inefficiently compensated for by measurement before each processing at present. To address this problem, we discovered the period-phase evolution law of the positioning error and established a double sine function compensation model. In the self-developed robotic polishing platform, the results show that the Z-axis error in the whole workspace after compensation can be reduced to ±0.06m m, which reaches the robot repetitive positioning error level; the Spearman correlation coefficients between the measurement and modeling errors are all above 0.88. In the practical polishing experiments, for both figuring and uniform polishing, the ripple error introduced by the positioning error is significantly suppressed by the proposed model under different conditions. Besides, the power spectral density (PSD) analysis has shown a significant suppression in the corresponding frequency error. This model gives an efficient plug-and-play compensation model for the robotic polisher, which provides possibilities for further improving robotic processing accuracy and efficiency.

Design of Backstepping Sliding Mode Control for a Polishing Robot Pneumatic System Based on the Extended State Observer

Due to advantages such as a high power-to-weight ratio, a simple structure, and low cost, pneumatic systems are widely applied in automation. However, precise position control of pneumatic actuators is challenging because of factors such as friction, compressibility, and external disturbances. This paper presents a backstepping sliding mode control (BSMC) strategy based on the extended state observer (ESO) for pneumatic cylinder position tracking. A nonlinear model of the pneumatic system is first established, then system states and disturbances are estimated by an ESO, next the BSMC approach is developed using backstepping method and sliding mode control theory, and the stability of the ESO and controller is analyzed using Lyapunov theory. Finally, simulations and experiments on a pneumatic testbed are performed to compare the effectiveness of the proposed approach with PID control. The results show that the proposed strategy improves tracking accuracy and robustness against disturbances, with a 77.04% reduction in root mean square error (RMSE). This research provides a promising control solution for automated pneumatic polishing robots.

AL-ProMP: Force-relevant skills learning and generalization method for robotic polishing

Skill learning in robot polishing is gaining attention and becoming a hot issue. Current studies on skill learning in robot polishing are mainly about trajectory skills, and force-relevant skills learning models are less studied. A skill learning method with good generalization and robustness is one of the elements worth investigating. In this study, a force-relevant skills learning method called arc-length probabilistic movement primitives (AL-ProMP) is proposed to improve the efficiency of robot polishing force planning. AL-ProMP learns the mapping between the contact force and polishing trajectory, and the temporal scaling factor and force scaling factor in AL-ProMP enable better robustness of force planning in speed scaling tasks and polishing tasks in different scenarios. Speed scaling is an important property for adaptation of the polishing policy. For the generalization of polishing skills to different polishing tools in robotics disc polishing tasks of unknown geometric model workpieces, a novel force scaling factor for different polishing discs is proposed according to the contact force model. In addition, polishing contact position learning provides the basis for polishing trajectory generalization. Finally, it is experimentally verified that the proposed method is effective in learning and generalizing the demonstrated skills and improving the polishing surface quality of the workpiece with unknown geometric model.

Simulation of Auto Parts Polishing Control Strategy Based on PID Control

With the large increase in automobile production, the demand for automobile parts is also increasing day by day. Aluminum alloy is the most widely used material in automobile parts. The production process of aluminum alloy parts has been automated, but the polishing process is still mainly manual. The high cost and low efficiency of manual polishing restrict the development of parts production technology. Therefore, it is imperative to develop a set of automatic polishing systems for aluminum alloy parts. Robot application in the polishing field has become a trend, but the force/bit coupling relationship in the polishing process of a robot is complicated, which brings great difficulty to the control of the polishing force. To solve the above problems, the key to obtaining stable polishing quality is to realize the force/bit decoupling of robot polishing and control the output constant polishing force. In this paper, an end-effector is used to realize the decoupling of force and position, and a new constant-force polishing control strategy is proposed to improve the automatic control level of robot polishing aluminum alloy auto parts. According to the nonlinear characteristics of the end-effector mathematical model and the feedback linearization characteristics of the backstepping method, a new type of backstepping PID constant force controller is designed, and the control flow is set up for simulation analysis. Simulink was used to build the simulation process and set up a reasonable controller gain for simulation verification. The simulation results of end-effector output polishing force under different strategies show that the reverse PID control has better response speed and anti-interference ability than the simple reverse control.

Design and modeling of a passive magnetic vibration absorber for robotic polishing process

Due to the low stiffness of workpieces, chattering easily occurs during robotic polishing operations and leads to poor surface quality and reduced machining accuracy. In this work, in order to reduce vibration during robotic polishing, a passive vibration absorber was designed by the principles of homopolar repulsion and electromagnetic induction. The passive vibration absorber included an upper platform, a damping block, and a lower platform. The flange of the robot was connected to the end polishing effector through the top platform, and its base platform and damping block were movable along the guiding shafts. In addition, permanent magnets were attached to the top and lower platforms and the damping block. During the polishing process, a repulsive force was generated between the homo-polar permanent magnets existing on the upper plate and the damping block. Similarly, a repulsive force was also generated between the bottom platform and the damping block. Due to the magnetic field generated by the eddy current, the magnetic field of the permanent magnets was time-varying and opposite to the external magnetic field of the moving damping block, which generated a repulsive force to compensate for vibration during the polishing process. As the workpiece had multiple modes involved in vibration and its dynamic characteristics varied with the position of the grinding tool, the repulsive force and the damping force effectively reduced vibration and dissipated the energy of the oscillating system. The modeling and dynamic analysis of the proposed vibration absorber were performed to achieve an optimal design. Finally, experimental studies were carried out to evaluate the effects of this vibration absorber. The experimental results demonstrated that the absorber could effectively limit the fluctuation of the polishing force, suppress the vibration of the spindle, and enhance the surface quality of the polished workpiece.