Search Results (93)

Titanium alloy thin-walled components are extensively used in aerospace engineering, yet their milling stability remains a persistent challenge due to vibration-induced surface anomalies. This study develops an advanced dynamic model for the face milling of titanium alloy thin-walled structures, systematically integrating axial cutting dynamics with regenerative chatter mechanisms and nonlinear process damping phenomena. The proposed framework crucially accounts for time-varying tool–workpiece interactions and damping characteristics, enabling precise characterization of stability transitions under dynamically varying axial immersion conditions. A novel extension of the semi-discretization method is implemented to resolve multi-parameter stability solutions, establishing a computational paradigm for generating three-dimensional stability lobe diagrams (3D SLDs) that concurrently evaluate spindle speed, cutting position, and the axial depth of a cut. Comprehensive experimental validation through time-domain chatter tests demonstrates remarkable consistency between theoretical predictions and empirical chatter thresholds. The results reveal that process damping significantly suppresses chatter at low spindle speeds, while regenerative effects dominate instability at higher speeds. This work provides a systematic framework for optimizing machining parameters in thin-walled component manufacturing, offering improved accuracy in stability prediction compared to traditional two-dimensional SLD methods. The proposed methodology bridges the gap between theoretical dynamics and industrial applications, facilitating efficient high-precision machining of titanium alloys. Full article

Hydraulically driven flexible robotic arms (HDFRAs) play an indispensable role in industrial precision operations such as aerospace assembly and nuclear waste handling, owing to their high power density and adaptability to complex environments. However, inherent mechanical flexibility-induced vibrations, hydraulic nonlinear dynamics, and electromechanical coupling effects lead to multi-timescale control challenges, severely limiting high-precision trajectory tracking performance. The present study introduces a novel hierarchical control framework employing dual-timescale perturbation analysis, which effectively addresses the constraints inherent in conventional single-timescale control approaches. First, the system is decoupled into three subsystems via dual perturbation parameters: a second-order rigid-body motion subsystem (SRS), a second-order flexible vibration subsystem (SFS), and a first-order hydraulic dynamic subsystem (FHS). For SRS/SFS, an adaptive fast terminal sliding mode active disturbance rejection controller (AFTSM-ADRC) is designed, featuring a dual-bandwidth extended state observer (BESO) to estimate parameter perturbations and unmodeled dynamics in real time. A novel reaching law with power-rate hybrid characteristics is developed to suppress sliding mode chattering while ensuring rapid convergence. For FHS, a sliding mode observer-integrated sliding mode coordinated controller (SMO-ISMCC) is proposed, achieving high-precision suppression of hydraulic pressure fluctuations through feedforward compensation of disturbance estimation and feedback integration of tracking errors. The globally asymptotically stable property of the composite system has been formally verified through systematic Lyapunov-based analysis. Through comprehensive simulations, the developed methodology demonstrates significant improvements over conventional ADRC and PID controllers, including (1) joint tracking precision reaching ${10}^{-4}$ rad level under nominal conditions and (2) over 40% attenuation of current oscillations when subjected to stochastic disturbances. These results validate its superiority in dynamic decoupling and strong disturbance rejection. Full article

The present study concentrates on passive damping technology, in which the damping of vibrations is accomplished by the integration of lattice structures into the boring bar. To complete this process, several steps must be followed. First, the largest possible hollow space within the boring bar was determined, and the two main influencing factors—stiffness and natural frequency—were harmonized. A rigorous analysis of vibration reduction was conducted on the basis of a validated simulation model. This analysis involved six distinct lattice structures designed using ANSYS SpaceClaim 19.0. In light of the findings, a specialized, application-specific CAD simulation tool was developed, employing appropriate methodologies to circumvent the limitations of conventional CAD software. For the hollow integrated into the boring bar, ellipsoidal shapes were shown to be preferable to cylindrical ones due to their superior dynamic performance. The initial lattice structure, namely a cube lattice with side cross supports, exhibited an enhancement in damping of 55.58% in comparison with the reference model. Following this result, five additional modelling steps were performed, leading to an optimal outcome with a 67.79% reduction in vibrations. Moreover, the modifications made to the beam diameter of the lattice units yielded enhanced dynamic performance, as evidenced by a vibration suppression of 69.81%. The implementation of complex modelling steps, such as the integration of a hollow and the integration of lattice structures, could be successfully achieved through the development of a suitable and user-friendly simulation tool. The effectiveness of the simulation tool in enabling parameterized modelling for scalable lattice structures was demonstrated. This approach was found to be expeditious in terms of the time required for implementation. The potential exists for the extension of this simulation tool, with the objective of facilitating research projects with a view to optimization, i.e., a large number of research projects. 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

A well-designed clamping layout significantly enhances the dynamic stiffness of a manufacturing system, improving its stability and suppressing cutting chatter in workpieces. This paper focuses on the machining of thin-walled beams, which are prone to vibration and have low stiffness, especially under hydraulic floating clamping conditions. By analyzing the system stability domain, we propose a method to improve system stiffness through strategic design of support module layouts. Finite element dynamic simulations and modal hammer experiments were conducted to validate this approach. The results show that the proposed layout design method increases the relative central frequency by 13.49% and the relative fundamental frequency by 8.51%. These findings demonstrate a substantial improvement in the dynamic stiffness of the part-clamping system, confirming that the auxiliary support module layout design method effectively enhances system dynamic stiffness and suppresses cutting chatter. Full article

Tool Chatter or Self-Excited Vibration is a common issue in machining processes. This phenomenon arises due to various factors, such as tool rigidity, depth of cut, spindle speed, etc., leading to poor surface finish, excessive tool wear, and premature deterioration of machine components. To prevent tool chatter, a real-time chatter detection algorithm was developed using a low-cost accelerometer in combination with internal machine variables. The algorithm operates without requiring a prior model of the specific tool characteristics, making it capable of detecting chatter by simply knowing the number of teeth of the active tool. Furthermore, the implementation of the detection algorithm meets the strict requirements of real-time embedded systems, ensuring high determinism, low latency, and minimal computational cost. This enables efficient and optimal integration into the machine. The developed chatter detection system was validated through machine-based experimental testing. Full article

Bladed components are essential in engines and propulsion systems, but their thin structures, complex geometries, and significant material removal rates during machining make them challenging to manufacture. This study investigates the chatter phenomenon in blade whirling milling, a promising method for improving machining efficiency. Multi-sensor signals, including vibration and acoustic emission signals, are collected during roughing and finishing machining. Time-domain, frequency-domain, and time-frequency features are extracted, filtered, and fused using principal component analysis (PCA) to retain relevant information while ensuring computational efficiency. The features are then input into an MLGRU-based chatter recognition model, incorporating a self-attention mechanism (SAM) for enhanced performance. The experimental results show that the proposed model achieves an average recognition accuracy of 89.16%, with a response time within 0.4 s, reflecting its effectiveness and timeliness in chatter detection. The findings also validate that the blade edge regions are more prone to chatter, especially during rough machining, due to their lower rigidity and greater sensitivity to external excitations. Full article

One of the machining processes that can be performed on a lathe machine is the boring process, which is used to enlarge holes in a workpiece using a cutting tool called a boring bar. During the boring process, excessive vibration or chatter occurs, which can significantly degrade the surface quality of the workpiece. To reduce these vibrations, a system called the Mass-Rubber Dynamic Vibration Absorber (MR-DVA) is used. A tapered MR-DVA is used, and it is located on the cavity of the customized boring bar. The absorber mass is made of brass, and the absorber stiffness is made of natural rubber. The MR-DVA is designed with various dimension ratios: 10/21, 14/21, and 16/21. The boring bar designed consists of a regular boring bar and a customized boring bar (with MR-DVA) with a diameter of 32 mm and a length-to-diameter (L/D) ratio of 7. The experiment was carried out with variations in cutting parameters such as the cutting speed, feed rate, and depth of cut as well as the variations in the taper ratio of the MR-DVA. The experiment shows that a customized boring bar with a tapered MR-DVA can reduce vibration, and the combination of variation that is optimal to reduce vibration is a customized boring bar with a taper ratio of 14/21. With the obtained optimal parameters for reducing vibrations, the manufacturing efficiency and product quality will be enhanced. 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

Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due to discrepancies between simulated and actual machining environments. The primary goal of this study is to bridge the gap between simulation-based machine learning models and real-world applications by developing and validating a Random Forest-based chatter detection system. This research focuses on improving manufacturing efficiency through reliable chatter detection by integrating Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL). The study applies a Random Forest classification model trained on over 140,000 simulated machining datasets, incorporating techniques like Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL) to adapt the model for real-world operational data. The model is validated against 1600 real-world machining datasets, achieving an accuracy of 86.1%, with strong precision and recall scores. The results demonstrate the model’s robustness and potential for practical implementation in industrial settings, highlighting challenges such as sensor noise and variability in machining conditions. This work advances the use of predictive analytics in machining processes, offering a data-driven solution to improve manufacturing efficiency through more reliable chatter detection. Full article

Carbon fiber-reinforced polymer (CFRP)/titanium alloy (Ti6Al4V) stacks are widely used in the aerospace industry due to their excellent physical properties. The substantial demand for drilling components in the aerospace industry necessitates the implementation of enhanced processing efficiency and drilling quality standards. Six-degrees-of-freedom robots are commonly used in the aerospace industry due to their high production efficiency, high flexibility, and low labor costs. However, due to the weak stiffness, chatter is prone to occur during processing, which has a detrimental impact on the quality of the finished product. As an advanced processing technology, ultrasonic-assisted machining technology can effectively reduce the cutting force and suppress the chatter in the drilling process, so it is widely used in production. In this paper, first, the robot kinematic (dexterity) and stiffness performance is analyzed. Then, the appropriate range of the machining plane and the posture of the robot in the workspace are selected. Finally, the vibration and CFRP entrance damage during the machining process are compared and studied in conventional robotic drilling (CRD) and ultrasonic-assisted robotic drilling (UARD). The experimental results demonstrate that the UARD is an effective method for reducing vibration during the machining process. Compared with the CRD, the CFRP entrance delamination damage in UARD is reduced. Under the appropriate processing parameters, the entrance delamination factor could be reduced by 15%, and the burr height could be reduced by 45%. Obviously, the UARD is a promising process to improve the CFRP entrance delamination damage. Full article

Sensor-based monitoring of process and tool condition in milling is a key technology for improving productivity and workpiece quality, as well as enabling automation of machine tools. However, industrial implementation of such monitoring systems remains a difficult task, since they require high sensitivity and minimal impact on CNC machines and cutting conditions. This paper presents a novel multi-sensory tool holder for measurement of process forces and vibrations in direct proximity to the cutting tool. In particular, the sensor system has an integrated temperature sensor, a triaxial accelerometer and strain gauges for measurement of axial force and bending moment. It is equipped with a self-sufficient electric generator and wireless data transmission, allowing for a tool holder design without interfering contours. Milling and drilling experiments with varying cutting parameters are conducted. The measurement data are analyzed, pre-processed and verified with reference signals. Furthermore, the suitability of all integrated sensors for detection of dynamic instabilities (chatter) is investigated, showing that bending moment and tangential acceleration signals are the most sensitive regarding this monitoring task. Full article

In milling processes in which material removal is performed periodically from solid material, dynamic effects are generally considered to be responsible for instabilities and subsequent productivity limits. Usually, in such applications, the process-inherent complex dynamic load spectrum on machines, tools and workpieces is considered together with vibration-based relative displacements that can be attributed to the regenerative effect. There are numerous techniques in the literature addressing the suppression of these dynamic effects, but they require a large amount of analysis and implementation effort as well as specific expert knowledge. The approach presented here, however, provides a universally applicable method for suppressing chatter vibrations and deflections. By applying structure elements to the flanks of the minor cutting edges of HSS end mills, it was possible to increase the chatter-free limiting depth of cut a_p,crit in the milling processes of the aluminum alloy EN AW-7075. Structured tools were used in ramp milling tests to investigate various effects, such as the influence of certain geometric design features on the stabilization potential compared to a reference tool. Furthermore, the effects of varied process parameter configurations and wear-related effects on the performance of the tool concept were focused on as well. The three key design features of the cutting edge and the structured profiles were identified from the results of the investigation, which, when combined in the most efficient design, in each case led to the development of an optimized structure and process configuration with cumulative potential for increasing the stability limit up to 200%. Full article

This paper introduces a complete real-time algorithm, where the chatter is detected and eliminated by spindle speed manipulation via the chatter energy feedback calculated from the axis encoder measurement. The proposed method does not require profound knowledge of the machining dynamics; instead, the entire algorithm exploits the fact that milling vibrations consist of forced vibrations at spindle speed harmonics and chatter vibrations that are close to one of the natural modes, with sidebands which are spread at the multiples of spindle speed frequency above and below the chatter frequency. The developed algorithm is able to identify the amplitude, phase and frequency of all the harmonics constituting the periodic forced and chatter vibrations. The key challenge is to select dominant chatter frequencies for the calculation of a robust and accurate chatter energy ratio feedback; this is achieved by utilizing the frequency estimation variance of EKF as a novel chatter indicator. Based on the chatter energy ratio feedback, the controller overrides the spindle speed in order to suppress the chatter energy below a particular threshold value. The varying spindle speed challenge is handled by updating the state transition matrices of the Kalman filters and real-time calculation of the band-pass filter coefficients, based on the derived discrete time transfer functions. The developed algorithm is tested on a Deckel FP5cc CNC which is in-house retrofitted and has a PC-based controller for the real-time application of the proposed algorithm. It is shown that the real-time chatter frequency and amplitude estimates are compatible with off-line FFT analysis, and chatter can be successfully eliminated by energy feedback. Full article

The milling of thin-wall geometries has been a challenge due to inherent chatter vibrations and workpiece deflections. Moreover, tool path generation strategies in CAD-CAM systems are not able to fully address all such concerns. The objective of this study is to demonstrate potential 5-axis milling tool path strategies, which do not exist in the conventional tool path generation. The demonstration is performed for increased efficiency in milling of thin-wall features considering the main limitation of chatter. The effects of varying workpiece dynamics on milling stability are shown in case studies through simulations and cutting experiments. Based on the simulation results, tool path strategies are developed. The effect of tool path generation and the relation to parameter selection are highlighted. Most of the discussion relies on previously reported experimental results. The results showed that by tailoring the tool path considering the concerns and limitations associated with thin-wall part structure and geometry, it is possible to increase productivity by at least two folds. Full article