Regularized automatic frequency response function acquisition of a milling robot operating in a high-dimensional workspace

Because robotic milling has become an important means for machining significant large parts,obtaining the structural frequency response function(FRF)of a milling robot is an important basis for machining process optimization.However,because of its articulated serial structure,a milling robot has an enormous number of operating postures,and its dynamics are affected by the motion state.To accurately obtain the FRF in the operating state of a milling robot,this paper proposes a method based on the structural modification concept.Unlike the traditional excitation method,the proposed method uses robot joint motion excitation instead of hammering excitation to realize automation.To address the problem of the lack of information brought by motion excitation,which leads to inaccurate FRF amplitudes,this paper derives the milling robot regularization theory based on the sensitivity of structural modification,establishes the modal regularization factor,and calibrates the FRF amplitude.Compared to the commonly used manual hammering experiments,the proposed method has high accuracy and reliability when the milling robot is in different postures.Because the measurement can be performed directly and automatically in the operation state,and the problem of inaccurate amplitudes is solved,the proposed method provides a basis for optimizing the machining posture of a milling robot and improving machining efficiency.

Multi-Objective Redundancy Optimization of Continuous-Point Robot Milling Path in Shipbuilding

The 6-DOF manipulator provides a new option for traditional shipbuilding for its advantages of vast working space,low power consumption,and excellent flexibility.However,the rotation of the end effector along the tool axis is functionally redundant when using a robotic arm for five-axis machining.In the process of ship construction,the performance of the parts’protective coating needs to bemachined tomeet the Performance Standard of Protective Coatings(PSPC).The arbitrary redundancy configuration in path planning will result in drastic fluctuations in the robot joint angle,greatly reducing machining quality and efficiency.There have been some studies on singleobjective optimization of redundant variables,However,the quality and efficiency of milling are not affected by a single factor,it is usually influenced by several factors,such as the manipulator stiffness,the joint motion smoothness,and the energy consumption.To solve this problem,this paper proposed a new path optimization method for the industrial robot when it is used for five-axis machining.The path smoothness performance index and the energy consumption index are established based on the joint acceleration and the joint velocity,respectively.The path planning issue is formulated as a constrained multi-objective optimization problem by taking into account the constraints of joint limits and singularity avoidance.Then,the path is split into multiple segments for optimization to avoid the slow convergence rate caused by the high dimension.An algorithm combining the non-dominated sorting genetic algorithm(NSGA-II)and the differential evolution(DE)algorithm is employed to solve the above optimization problem.The simulations validate the effectiveness of the algorithm,showing the improvement of smoothness and the reduction of energy consumption.

High precision and efficiency robotic milling of complex parts:Challenges,approaches and trends

Due to the advantages of large workspace,low cost and the integrated vision/force sensing,robotic milling has become an important way for machining of complex parts.In recent years,many scholars have studied the problems existing in the applications of robotic milling,and lots of results have been made in the dynamics,pose planning,deformation control etc.,which provides theoretical guidance for high precision and high efficiency of robotic milling.From the perspective of complex parts robotic milling,this paper focuses on machining process planning and control techniques including the analysis of the robot-workspace,robot trajectory planning,vibration monitoring and control,deformation monitoring and compensation.As well as the principles of these technologies such as robot stiffness characteristics,dynamic characteristics,chatter mechanisms,and deformation mechanisms.The methods and characteristics related to the theory and technology of robotic milling of complex parts are summarized systematically.The latest research progress and achievements in the relevant fields are reviewed.It is hoped that the challenges,strategies and development related to robotic milling could be clarified through the carding work in this paper,so as to promote the application of related theories and technologies in high efficiency and precision intelligent milling with robot for complex parts.

Smoothness-oriented path optimization for robotic milling processes

Industrial robots are increasingly used for five-axis machining operations, where the rotation of the end effector along the toolaxis direction is functionally redundant. This functional redundancy should be carefully resolved when planning the robot path according to the tool path generated by a computer-aided manufacturing(CAM) system. Improper planning of the redundancy may cause drastic variations of the joint motions, which could significantly decrease the machining efficiency as well as the machining accuracy. To tackle this problem, this paper presents a new optimization-based methodology to globally resolve the functional redundancy for the robotic milling process. Firstly, a global performance index concerning the smoothness of the robot path at the joint acceleration level is proposed. By minimizing the smoothness performance index while considering the avoidance of joint limits and the singularity and the constraint of the stiffness performance, the resolution of the redundancy is formulated as a constrained optimization problem. To efficiently solve the problem, the sequential linearization programming method is employed to improve the initial solution provided by the conventional graph-based method. Then, simulations for a given tool path are presented. Compared with the graph-based method, the proposed method can generate a smoother robot path in which a significant reduction of the magnitude of the maximum joint acceleration is obtained, resulting in a smoother tool-tip feedrate profile. Finally, the experiment on the robotic milling system is also presented. The results show that the optimized robot path of the proposed method obtains better surface quality and higher machining efficiency, which verifies the effectiveness of the proposed method.

A cortical bone milling force model based on orthogonal cutting distribution method

In orthopedic surgery,the bone milling force has attracted attention owing to its significant influence on bone cracks and the breaking of tools.It is necessary to build a milling force model to improve the process of bone milling.This paper proposes a cortical bone milling force model based on the orthogonal cutting distribution method(OCDM),explaining the effect of anisotropic bone materials on milling force.According to the model,the bone milling force could be represented by the equivalent effect of a transient cutting force in a rotating period,and the transient milling force could be calculated by the transient milling force coefficients,cutting thickness,and cutting width.Based on the OCDM,the change in transient cutting force coefficients during slotting can be described by using a quadratic polynomial.Subsequently,the force model is updated for robotic bone milling,considering the low stiffness of the robot arm.Next,an experimental platform for robotic bone milling is built to simulate the milling process in clinical operation,and the machining signal is employed to calculate the milling force.Finally,according to the experimental result,the rationality of the force model is verified by the contrast between the measured and predicted forces.The milling force model can satisfy the accuracy requirement for predicting the milling force in the different processing directions,and it could promote the development of force control in orthopedic surgery.

Early chatter identification based on optimized VMD with multi-band information fusion and compression method in robotic milling process

Undesirable self-excited chatter has always been a typical issue restricting the improvement of robotic milling quality and efficiency.Sensitive chatter identification based on processing signals can prompt operators to adjust the machining process and prevent chatter damage.Compared with the traditional machine tool,the uncertain multiple chatter frequency bands and the band-moving of the chatter frequency in robotic milling process make it more challenging to extract chatter information.This paper proposes a novel method of chatter identification using optimized variational mode decomposition(OVMD)with multi-band information fusion and compression technology(MT).During the robotic milling process,the number of decomposed modes k and the penalty coefficient a are optimized based on the dominant component of frequency scope partition and fitness of the mode center frequency.Moreover,the mayfly optimization algorithm(MA)is employed to obtain the global optimal parameter selection.In order to conquer information collection about the uncertain multiple chatter frequency bands and the band-moving of the chatter frequency in robotic milling process,MT is presented to reduce computation and extract signal characteristics.Finally,the cross entropy of the image(CEI)is proposed as the final chatter indicator to identify the chatter occurrence.The robotic milling experiments are carried out to verify the proposed method,and the results show that it can distinguish the robotic milling condition by extracting the uncertain multiple chatter frequency bands and overcome the band-moving of the chatter frequency in robotic milling process.