Search Results (9)

The application of robotic milling manipulators demonstrates a promising method for the efficient manufacturing of large-scale structures. However, the cutting accuracy and efficiency of milling robots are predominantly subjected to their low stiffness, which may easily cause chatter during machining. Accurate prediction of chatter stability for robots is of practical importance and is challenging. This paper develops a dynamic model of flexible link elements by considering link flexibility and joint torsional deformation and then constructs a multi-link flexible coupled dynamic model using the receptance coupling substructure analysis (RCSA) method. Subsequently, the equivalent dynamic parameters are identified via the particle swarm optimization (PSO) algorithm. On this basis, the end-effector frequency response functions (FRFs) of the robot under different poses are predicted, and the stability lobe diagram (SLD) for milling is generated based on chatter theory. Finally, the predicted FRFs and stability regions are validated through modal tests and milling experiments. Experimental results demonstrate that the proposed model can predict the end-effector dynamic characteristics and chatter occurrence conditions under different poses, confirming its effectiveness in the analysis of milling chatter stability. Quantitative validation yields a maximum error of 3% for predicted first-order modal frequencies and relative modal amplitude errors below 10%, with experimentally confirmed critical depths of cut of 0.1–0.2 mm at 3000 rev/min and 0.5–0.6 mm at 5000 rev/min. Full article

Robotic milling offers flexibility and lower capital cost than conventional CNC machining but is limited by low, pose-dependent structural stiffness. This study experimentally investigates how pose, cutting orientation, and engagement conditions govern dynamic response and machining accuracy, benchmarked against a CNC machine under matched conditions. Tool-point frequency response functions show that the robot exhibits dominant low-frequency structural modes at 8–15 Hz with compliances on the order of 10⁻⁵ m/N, one to two orders of magnitude more flexible than higher-frequency tool–holder modes (~10⁻⁶ m/N). In contrast, the CNC system is dominated by a stiff mode near 600 Hz (~2 × 10⁻⁷ m/N) with negligible low-frequency compliance. During cutting, the response is not resonance-driven; instead, low-frequency compliance induces modulation of spindle-synchronous vibrations, resulting in broadband spectral spreading and cycle-to-cycle variability. Poincaré analysis captures this modulation, which increases with spindle speed and depth of cut. Orientation-dependent alignment with compliant directions amplifies vibration and cross-axis coupling. Regression analysis shows a significant association between Z-direction vibration and depth-of-cut deviation (R = 0.739 locally; R = 0.363 globally). The results establish a framework linking compliance, modulation, and machining performance in robotic milling. Full article

In the new digital era, industrial robots are central to machining and flexible production in intelligent manufacturing. However, the rigidity of the six degrees-of-freedom (DOFs) serial robot is insufficient, which leads to chatter during machining and limits its application in high-precision machining, especially in the field of drilling, reaming and milling. A new method was proposed for modeling the global stiffness of the robot drilling system that incorporated the end-effector. Based on the virtual joint method and linear superposition principle, and considering the flexibility of the robot arm, the global stiffness model of the robot drilling system was established by simplifying the modeling process with dual quaternion. The results of the model validation experiments of deformation show that the maximum relative error of resultant end deformation is 8.80%, and the average relative error of resultant end deformation is 7.21%. This method provides a new method of global stiffness modeling for the robot drilling system, including the end-effector, and a new approach for stiffness improvement to overcoming the problem of insufficient robot stiffness in intelligent manufacturing industry. Full article

Machining efficiency and stability are crucial for achieving high-quality manufacturing outcomes. One of the primary challenges in machining is the suppression of chatter, which negatively impacts surface finish, tool longevity, and overall process reliability. This study proposes a machine learning-based approach to optimize feed rate in machining operations by integrating a genetic algorithm (GA)-calibrated cutting force model with vibration analysis. A theoretical cutting force dataset is generated under varying machining conditions, followed by frequency-domain analysis using Fast Fourier Transform (FFT) to identify feed rates that minimize chatter. These optimal feed rates are then used to train an Extreme Gradient Boosting (XGBoost) regression model, with Bayesian optimization employed for hyperparameter tuning. The trained model achieves an R² score of 0.7887, indicating strong prediction accuracy. To verify the model’s effectiveness, robotic milling experiments were conducted using a UR10e manipulator. Surface quality evaluations showed that the model-predicted feed rates consistently resulted in better surface finish and reduced chatter effects compared to conventional settings. These findings validate the model’s ability to enhance machining performance and demonstrate the practical value of integrating simulated dynamics and machine learning for data-driven parameter optimization in robotic systems. Full article

Industrial robots are widely used in the field of processing because of their many advantages, such as their high flexibility and wide processing range, but the chatter phenomenon limits their application. In this study, a chatter model for the robot milling of natural marble is established that analyzes the effect of the milling parameters and position. The chatter stability model is first established based on the zeroth-order approximation method, in which the robot milling system is simplified into a vibration system with two degrees of freedom. The milling force coefficients are determined by robot milling experiments, and the modal parameters of the robot milling system are identified based on the single-point excitation and pickup methods, which are essential for the establishment of the chatter stability model. Second, robot milling experiments are conducted to verify the established model, proving its high prediction accuracy. Finally, the effect of the milling parameters and position on the chatter stability of the robot milling system is discussed. These conclusions can be references for the robot milling of natural marble. Full article

Industrial robots are increasingly prevalent due to their large workspace and cost-effectiveness. However, their limited static and dynamic stiffness can lead to issues like mode coupling chatter and regenerative chatter in robotic milling processes, even at shallow cutting depths. These problems significantly impact performance, product quality, tool longevity, and can damage robot components. An active inertial actuator was deployed at the milling spindle to enhance dynamic stiffness and suppress low-frequency vibrations effectively. It was identified that the characteristics of the actuator change with its mounting orientation, a common scenario in robotic machining processes. This variation has not been reported in the literature. Our study includes the identification of model parameters for the actuator in both horizontal and vertical mountings. Additionally, the novelty of the present work lies in the specific design and implementation of compensation filters tailored for the active inertial actuator in both horizontal and vertical configurations. These filters address the unique challenges posed by low-frequency vibrations in robotic milling, offering significant improvements in dynamic stiffness and vibration suppression. Traditional model-based compensators were effective for vertical mounting, while pole-zero placement techniques with minimum phase systems were optimal for horizontal mounting. These compensators significantly enhanced dynamic stiffness, reducing maximum low-frequency robot structural modes by approximately 100% in horizontal mounting and approximately 214% in the vertical configuration of the actuator. This advancement promises to enhance industrial robot capabilities across diverse machining applications. Full article

With the advantages of large working space, low cost and more flexibility, industrial robots have become an important carrier in intelligent manufacturing. Due to the low rigidity of robotic milling systems, cutting vibrations are inevitable and have a significant impact on surface quality and machining accuracy. To improve the machining performance of the robot, a posture optimization approach based on the dynamic response index is proposed, which combines posture-dependent dynamic characteristics with surface quality for robotic milling. First, modal tests are conducted at sampled points to estimate the posture-dependent dynamic parameters of the robotic milling system. The modal parameters at the unsampled points are further predicted using the inverse distance weighted method. By combining posture-independent modal parameters with calibrating the cutting forces, a dynamic model of a robotic milling system is established and solved with a semi-discretization method. A dynamic response index is then introduced, calculated based on the extraction of the vibration signal peaks. The optimization model is validated through milling experiments, demonstrating that optimizing redundant angles significantly enhances milling stability and quality. Full article

Undesirable chatter is one of the key problems that restrict the improvement of robot milling quality and efficiency. The prediction of chatter stability, which is used to guide the selection of process parameters, is an effective method to avoid chatter in robot milling. Due to the weak stiffness of the robot, deformation caused by milling forces becomes an unavoidable problem, which will change the tool–workpiece contact area and affect the stability prediction. However, it is often simplified and neglected. In this paper, a multipoint contact dynamic model of robot milling is established, which considers the influence of force-induced deformation on the regenerative effect and process damping. The tool–workpiece contact area is discretized into a finite number of nodes along the axial direction so that the force and deformation at each node can be calculated separately. The different contact forms of the tool–workpiece under different process parameters are discussed in different cases, and the interaction process between cutting force and force-induced deformation is analyzed in detail. An iterative strategy is used to calculate the deformation of each node and the result of the tool–workpiece contact boundary. Finally, chatter stability of robot milling is predicted by a fully discrete method. Robot milling experiments were carried out to verify the predicted results. The results show that force-induced deformation is an important factor improving the stability prediction accuracy of robot milling, and a more accurate prediction result can be obtained by simultaneously considering force-induced deformation and process damping. Full article

Chatter detection in robotic milling is a difficult issue due to the complex dynamic behavior of robots. In this paper, a novel approach to detecting chatter in the robotic milling process is proposed. The method of improved complete ensemble empirical mode decomposition with adaptive noise is introduced for decomposing the milling vibration signals into a series of intrinsic mode functions (IMFs). The effective IMFs are chosen according to the correlation between the original signals and each IMF. Signal reconstruction is conducted using the selected IMFs. The weighted refined composite multiscale dispersion entropy is extracted from the reconstructed signals in order to characterize the chatter states. Then, a classification model is established for chatter detection. Experimental results prove that the proposed method is feasible for chatter detection in the robotic milling process under different robot configurations and machining parameters. Full article