Nonlinear impedance control with trajectory adaptation for collaborative robotic grinding

Stiffness adjustment is an important feature of human arm control.The adaptive variable impedance control can adapt to the robotic stiffness,but may result in a large overshoot.In this paper,nonlinear impedance control is proposed for collaborative robotic grinding,where nonlinear force feedback is designed to compensate for the nonlinear stiffness of the environment.Thus,the interaction system can be linearization to ensure the system stability.Moreover,a target trajectory adaptation strategy is studied to ensure the force tracking requirement.Then,switching law between trajectory tracking and force tracking is proposed when the robot performs a complex grinding task.The stability of the switch control as well as the trajectory adaptation law is proved.Experiments are conducted in a robotic grinding test rig,where the robot is used to grind a turbine blade.Experimental results show that the nonlinear impedance control can obtain stable grinding force,and have better grinding quality than the linear impedance control.

Designing and Optimization of an Off-line Programming System for Robotic Belt Grinding Process

Off-line programming （OLP） system becomes one of the most important programming modules for the robotic belt grinding process, however there lacks research on increasing the grinding dexterous space depending on the OLP system. A new type of grinding robot and a novel robotic belt grinding workcell are forwarded, and their features are briefly introduced. An open and object-oriented off-line programming system is developed for this robotic belt grinding system. The parameters of the trimmed surface are read from the initial graphics exchange specification （IGES） file of the CAD model of the workpiece. The deBoor-Cox basis function is used to sample the grinding target with local contact frame on the workpiece. The numerical formula of inverse kinematics is set up based on Newton＇s iterative procedure, to calculate the grinding robot configurations corresponding to the grinding targets. After the grinding path is obtained, the OLP system turns to be more effective than the teach-by-showing system. In order to improve the grinding workspace, an optimization algorithm for dynamic tool frame is proposed and performed on the special robotic belt grinding system. The initial tool frame and the interval of neighboring tool frames are defined as the preparation of the algorithm. An optimized tool local frame can be selected to grind the complex surface for a maximum dexterity index of the robot. Under the optimization algorithm, a simulation of grinding a vane is included and comparison of grinding workspace is done before and after the tool frame optimization. By the algorithm, the grinding workspace can be enlarged. Moreover the dynamic tool frame can be considered to add one degree-of-freedom to the grinding kinematical chain, which provides the theoretical support for the improvement of robotic dexterity for the complex surface grinding.

Resonance and Bifurcation of Fractional Nonlinear Systems with Power Damping Term for Robot Grinding

A fractional nonlinear system with power damping term is introduced to study the forced vibration system in order to solve the resonance and bifurcation problems between grinding wheel and steel bar during robot grinding.The robot,grinding wheel and steel bar are reduced to a spring-damping second-order system model.The implicit function equations of vibration amplitude of the dynamic system with coulomb friction damping,linear damping,square damping and cubic damping are obtained by average method.The stability of the system is analyzed and explained,and the stability condition of the system is proposed.Then,the amplitude-frequency characteristic curves of the system under different fractional differential orders,nonlinear stiffness parameters,fractional differential term coefficients and external excitation amplitude are analyzed.It is shown that the fractional differential term in the dynamic system is the damping characteristic.Then the influence of four kinds of damping on the vibration amplitude of the system under the same parameter is investigated and it is proved that the cubic damping suppresses the vibration of the system to the maximum extent.Finally,based on the idea that the equilibrium point of the system is the constant part of the Fourier series expansion term,the bifurcation behavior caused by the change of damping parameters in linear damping,square damping and cubic damping systems with different values of fractional differential order is investigated.

Grinding/Cutting Technology and Equipment of Multi-scale Casting Parts

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.

A trajectory planning method on error compensation of residual height for aero-engine blades of robotic belt grinding

While the traditional trajectory planning methods are used in robotic belt grinding of blades with an uneven machining allowance distribution, it is hard to obtain the preferable profile accuracy and surface quality to meet the high-performance requirements of aero-engine. To solve this problem, a novel trajectory planning method is proposed in this paper by considering the developed interpolation algorithm and the machining allowance threshold. The residual height error obtained from grinding experiments of titanium alloy sample was compensated to modify the calculation model of row spacing, and a new geometric algorithm was presented to dynamically calculate the cutter contact points based on this revised calculation model and the dichotomy method. Subsequently, the off-line machining program is generated based on a double-vector controlling method to obtain an optimal contact posture. On this basis, three sets of robotic grinding tests of titanium alloy blades were conducted to investigate the advantages of the proposed method.The comparative experimental results revealed that the presented algorithm had improved the surface profile accuracy of blade by 34.2% and 55.1%, respectively. Moreover, the average machined surface roughness was achieved to 0.3 μm and the machining efficiency was obviously promoted. It is concluded that this research work is beneficial to comprehensively improve the machined quality of blades in robotic belt grinding.

Analysis and prediction of surface roughness for robotic belt grinding of complex blade considering coexistence of elastic deformation and varying curvature

Precision prediction of machined surface roughness is challenging facing the robotic belt grinding of complex blade,since this process is accompanied by significant elastic deformation.The resulting poor prediction accuracy,to a great extent,is attributed to the existing prediction model which less considers the dynamics.In this paper,an improved scallop height model is developed to predict and assess the machined surface roughness by taking into account the elastic deformation and the varying curvature of blade,then robotic belt grinding experiments are carried out to evaluate the proposed model from the perspective of surface roughness.Finally factors that influence the scallop height are analyzed,and the suitable empirical equation of surface roughness is proposed to assess and predict the surface quality from the aspect of blade concave and convex surface by adopting the constant scallop height machining.

Intelligent learning model-based skill learning and strategy optimization in robot grinding and polishing

With the rapid advancement of manufacturing in China,robot machining technology has become a popular research subject.An increasing number of robots are currently being used to perform complex tasks during manual operation,e.g.,the grinding of large components using multi-robot systems and robot teleoperation in dangerous environments,and machining conditions have evolved from a single open mode to a multisystem closed mode.Because the environment is constantly changing with multiple systems interacting with each other,traditional methods,such as mechanism modeling and programming are no longer applicable.Intelligent learning models,such as deep learning,transfer learning,reinforcement learning,and imitation learning,have been widely used;thus,skill learning and strategy optimization have become the focus of research on robot machining.Skill learning in robot machining can use robotic flexibility to learn skills under unknown working conditions,and machining strategy research can optimize processing quality under complex working conditions.Additionally,skill learning and strategy optimization combined with an intelligent learning model demonstrate excellent performance for data characteristics learning,multisystem transformation,and environment perception,thus compensating for the shortcomings of the traditional research field.This paper summarizes the state-of-the-art in skill learning and strategy optimization research from the perspectives of feature processing,skill learning,strategy,and model optimization of robot grinding and polishing,in which deep learning,transfer learning,reinforcement learning,and imitation learning models are integrated into skill learning and strategy optimization during robot grinding and polishing.Finally,this paper describes future development trends in skill learning and strategy optimization based on an intelligent learning model in the system knowledge transfer and nonstructural environment autonomous processing.

A wheel-type in-pipe robot for grinding weld beads

To improve the safety and efficiency of polishing operations in circular boiler headers, a new type of wheel-drive polishing robot was developed in this study. The robot was designed to grind weld beads on the inner walls of pipes in diameter between 550 mm and 714 mm. The robot consists of a moving structure, a positioning structure, and a polishing structure. Charge coupled device （CCD） cameras and line lasers are used in the robot＇s vision system, thus the robot can be manually controlled to move, locate, and grind quickly and accurately. The experimental results showed that the robot performed well in practical applications.