Robotic Milling Research Articles

Is process damping effective in the stability of robotic milling?

Chatter stability is a major constraint in milling, where low and high cutting speeds are used. At low cutting speed regime, process damping leads to increased stability, whereas at high cutting speeds lobing effect is beneficial. Excitation frequency depends on spindle speed and the number of cutting flutes on the milling tool. Hence the vibration mode governing chatter stability varies for multi-mode milling systems. In CNC milling, low frequency structural modes are stiffer than cutting spindle-holder-tool (SHT) assembly. However, robotic milling demonstrates a distinct behavior as low frequency modes are significantly more flexible. This study investigates the effect of robot structure induced low frequency vibration modes on stability limits at low cutting speeds, where process damping is expected to increase stability limits. Time domain simulations are used to explain the variation of the dominant mode from high frequency to low frequency with the decreasing spindle speed. Simulated stability diagram for the multi-mode robotic milling system is verified by experiments. It was shown that especially the vibration modes in the range of 15 to 20 Hz do not generate enough process damping force due to long vibration waves, i.e. cutting speed – to – chatter frequency ratio, when low frequency modes govern chatter stability. Simulation of stability diagrams showed that there is a spindle speed region where the stability lobes governed by the robot structure crosscut the stability lobes governed by the THS assembly. Due to the inherent effect of tool diameter (D) and number of cutting flutes (Z) on cutting speed and excitation frequency, this region shifts according to the D/Z ratio. It was shown through simulations that D/Z ratio is a critical metric to benefit from process damping without the interference of the low frequency excitation of the robotic structure. The simulation results are used to provide suggestions for milling tool selection in robotic milling, where the main conclusion is to use lower D/Z ratio, which means that using high number of cutting flutes.

Digital twin-driven virtual commissioning for robotic machining enhanced by machine learning

Robotic machining has been increasingly applied in intelligent manufacturing production lines. Compared with the traditional machine tools, commissioning for robotic machining system (RMS) is particularly important due to the low accuracy of industrial robots (IRs). Traditional site commissioning has large workload and is difficult to handle the multi-source errors. Since digital twin (DT) provides strategies for staying synchronized with the physical entities in whole lifecycle, a DT-driven virtual commissioning (VC) system for RMS is developed in this study to improve machining accuracy and reduce the difficulty of commissioning. Firstly, the framework of DT-driven VC system is designed including several function modules such as interaction, data pre-processing, DT model of RMS (RMSDT), and optimization service. Since RMSDT is the kernel of precise VC, a machine learning-enhanced RMSDT oriented to actual machining path prediction is then constructed based on a proposed joint error equivalent strategy, which can fully consider the coupled multi-source errors of machining robot. After that, a practical consistency retention method for RMSDT is proposed based on a stepwise updating strategy, where the model performance can be maintained with low updating costs. Finally, a visual VC system is developed for the experimental 6-degree of freedom robotic milling platform to verify the feasibility and effectiveness of the VC framework. Multiple experiments are also performed to test the performance of RMSDT and contour error compensation. This study has useful reference for the enterprises engaged in RMS and has positive significance for promoting the robotic machining.

Chatter suppression in robotic milling using active contact force actuator

Chatter during the robotic milling process is an obstacle to achieving high milling performance in the field of intelligent manufacturing. Chatter in robotic milling occurs due to low stiffness, resulting in low milling efficiency and poor surface quality. To enhance the stability of the robotic milling, we introduce a novel active contact force actuator, which can improve the milling process stiffness by actively applying the force between the robot and the workpiece. The actuator can adapt to different machining conditions, and provide different sizes of active contact force by controlling the cylinder pressure, and does not interfere with the machining process. The principle of milling stability improvement with the novel actuator is proved, then the equivalent stability prediction diagram is derived to verify the effectiveness of the actuator. The performance of the actuator is finally verified through robotic milling experiments, the results show that the actuator can effectively suppress low-frequency chatter in the robotic milling, and maintain effective chatter suppression capabilities even under the high spindle-speed and feed- speed condition.

Controlling harmful milling vibration for robotic milling system based on the optimization of milling posture and spindle speed

Harmful machining vibration including milling chatter and resonance are easily generated during robotic milling process, and it seriously restrict the machining ability of the robot. To address this problem, a harmful machining vibration-controlled method based on the combined optimization of milling posture and spindle speed is proposed for robotic milling system in this paper. With this context, firstly, the robot stiffness model is established based on the theory of the multi-body with small-deformation, and the stiffness ellipsoid at the tool cutting point is used to optimize the milling posture, so that the structure stiffness of the robot can be enhanced; Then, the robot chatter stability model is constructed considering the modal coupling effect, and the spindle speed is optimized to restrain the milling chatter and resonance. Finally, with the optimized milling posture and spindle speed, the milling process is planned and performed with a Staubli TX-200 robot, and two experiments for workpiece with steel and aluminum are implemented to verify the proposed method. The experiment results demonstrate that the comprehensive optimization of milling posture and spindle speed can efficiently control the harmful machining vibration for the robotic milling system, thus, the machining quality of the workpiece can be improved.

Research on the influence of cutter overhang length on robotic milling chatter stability

Serial industrial robots are widely in demand in the field of large-scale component processing and manufacturing. However, the structure characteristic of serial robots makes it easy to generate chatter during milling and the overhang length of milling cutter has an important influence on the stability of robotic milling. Milling cutter overhang length refers to the length from the tip of the cutter to the protrusion of the spindle, to study the influence of milling cutter overhang length on the stability behavior of robotic milling, the modal parameters of milling cutter under different overhang length were obtained by hammer experiment, and its dynamic characteristics were analyzed. The stability behavior for robotic milling under different cutter overhang lengths was analyzed by using the stability lobe diagrams, and the stability lobe diagrams were verified by robotic milling experiments. The results show that the rigidity and natural frequency of milling cutter decrease with the increase of overhang length. There is a big gap between the stability lobe diagrams and the actual machining state when the cutter overhang length is short, and the low frequency vibration is easy to occur in the robotic milling process when the spindle speed is low, which is most probably because that the absorbed vibration energy by the cutter has been transferred to the robot body before the milling cutter vibrates. When the cutter overhang is increased and the feed direction is along the y-axis of the robot global coordinate system, the stability lobe diagrams by considering the multi-mode coupling effect is more consistent with the actual milling state.

Feasibility of 5G-enabled process monitoring in milling operations

5G monitoring holds immense potential for revolutionizing manufacturing processes by enabling real-time data transmission, remote control, enhanced quality control, and increased efficiency. However, it also presents challenges related to 5G monitoring infrastructure. To explore 5G’s potential for process monitoring, this study introduces a novel 5G-enabled architecture designed to address the challenges, enhancing the process monitoring’s efficiency, accuracy, and reliability in the case of milling operation. To investigate the feasibility of this sophisticated 5G network for process monitoring, two testbeds, i.e., the 5G robotic milling testbed and the 5G CNC milling testbed, have been developed. An accelerometer and a laser scanner have been retrofitted with 5G communications capability to capture critical process signals in the testbeds, respectively. It has shown that the sensor data can be upstreamed to a 5G edge server for data analytics and visualization in ultra-low latency. This work highlights the transformative impact of 5G communication on process monitoring for time-critical manufacturing.

Investigating the Impact of Robotic Milling Parameters on the Surface Roughness of Al-Alloy Fabricated by Wire Arc Additive Manufacturing

This paper takes the single-wall wall manufactured by wire arc additive manufacturing (WAAM) as the research object and compares it with the as-cast aluminum alloy with the same series. By using feed rate, cutting depth, spindle speed, etc., as single or compound parameters, the machinability of the sample is analyzed. The results indicate that the influence of varying parameters on the as-deposited aluminum alloy follows the order of feed rate > cutting depth > spindle speed. As the feed rate increases, the surface roughness initially decreases and then increases, with the optimal surface quality achieved at 12 mm/s (with a surface roughness of 2.013 μm). Different from the as-deposited alloy, the influence of the parameters on the as-cast alloys follows the order of spindle speed > cutting depth > feed rate. The experiments reveal that, for both as-deposited and as-cast states, the trends of the impact of cutting depth and spindle speed on surface quality are consistent. However, at low feed rates (2–12 mm/s), for as-deposited states, the surface quality of as-deposited samples becomes smoother as the feed rate increases (contrary to common knowledge). This result can be attributed to the elevated milling temperature, which softens the material, making it easier to remove and reducing the surface roughness.

Online vibration monitoring using laser vibrometer and acoustic emission techniques in robotic milling CFRP composites

Carbon fiber reinforced polymer (CFRP) material has superior mechanical properties, often utilized in the aerospace industry. However, its prone to surface defects such as burrs and delamination under the influence of machining vibration, thereby compromising component quality. This study evaluated the relevant vibration signal features via signal processing methods, and investigated the effect of machining parameters on machining stability during robotic milling of CFRP composite. Specifically, the study assessed machining status by analyzing milling signals and compared surface quality between vibration and stable machining stages, the former corresponds to a larger Sa on the machined surface, with more pronounced profile fluctuations. Machining vibration manifests distinct characteristics in laser vibrometer signals, exhibiting in time-frequency plots with short-term signal enhancements at multiple frequencies. This phenomenon occurs more frequently during entry and exit stages. The results indicated that feed speed has a limited effect on milling stability within the selected parameter range, and severe machining vibration occurred at lower spindle speed (12,000 rpm) and higher milling depth (2 mm). Corresponding surface defects formation mechanisms were discussed, revealing that the formation of uncut burrs at the top was related to axial milling force, while interlayer debonding cracks stemmed from variations in fiber orientation between adjacent layers.

Neural-network-based trajectory error compensation for industrial robots with milling force disturbance

ABSTRACT Industrial robots are increasingly used in advanced manufacturing fields such as aerospace due to their high efficiency and low cost. In robotic machining applications, deviation of the tool centre point trajectory from the desired path due to load disturbances acting on the end-effector of an industrial robot can result in poor dimensional accuracy and surface quality of products. Therefore, improving robot trajectory accuracy under external load disturbances is extremely important. This study proposes an error compensation methodology using neural networks optimized by a hybrid marine predators-grid search algorithm to compensate for robot trajectory error. Two neural networks are developed: one for predicting load disturbances and the other for predicting trajectory errors in robotic machining. The milling experiment results show that the compensated robot trajectory errors in x, y, and z directions are reduced by 65%, 76%, and 77% respectively, which proves the effectiveness of this method in improving the robotic milling accuracy.

Low-frequency chatter suppression for robotic milling using a novel MRF absorber

Robotic milling has a unique advantage for large and complex parts. However, it is extremely prone to low-frequency chatter due to the robot structure mode. In robotic milling, low-frequency chatter has a huge impact on machining quality and efficiency. In this paper, a novel MRF (Magnetorheological fluid) absorber is used to suppress low-frequency chatter during robotic milling based on the proposed low-frequency chatter suppression strategy. First, the GPR (Gaussian process regression) model is used to predict the impulse response function based on a few measured impulse response functions. Next, a cutting force model is developed considering the tool runout and the robot structure mode. The parameters and coefficients of the cutting force model are optimized by the measured cutting forces. Then, the low-frequency chatter frequency is predicted considering the modal coupling effect based on the predicted impulse response function and the simulated cutting force. Finally, an MRF absorber is designed based on the chatter frequency range. The controlled storage modulus of the MRF in the pre-yield region enables a wider frequency shift characteristic of the designed MRF absorber. The MRF absorber controls the input current based on the predicted low-frequency chatter frequency to achieve chatter suppression during robotic milling. Robotic milling experiments verified that the MRF absorber can effectively suppress low-frequency chatter. The experimental results show that the Energy Ratio Chatter Indicator of the industrial robot with MRF absorber is less than 0.1 under different milling conditions.

Improving Robotic Milling Performance through Active Damping of Low-Frequency Structural Modes

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.

Posture and path optimization based on stiffness performance index in robotic milling

Posture and path optimization based on stiffness performance index in robotic milling

A self-adaptive agent for flexible posture planning in robotic milling system

In recent years, industrial robots are widely used in the milling processes of the fundamental components such as aeronautic parts, aerospace cabins and blades on the marine propellers of the worships, because of their flexible structure, large operating space, and robust capacity of rapid reconfiguration. Along with the robotic machining process, multiple types of geometrical and physical constraints display the time-varying characteristics, which are difficult to be considered simultaneously by the traditional static planning methods on the robotic milling posture. Therefore, a deep reinforcement learning (DRL) enhanced virtual repulsive potential field flexible model is proposed for the robotic milling posture dynamic planning. The preliminary planning of static milling posture is realized based on the fixed setting of the virtual modal parameters in the posture planning model, which is treated as a theory preparation for training the deep reinforcement learning agent. To develop the static planning model, multiple constraints are established by considering the performances of geometric, kinematic and machining precision simultaneously. Furthermore, a reward function is constructed for the dynamic agent, comprehensively combining the long and short reward benefits in nonlinear correlation with virtual repulsive forces. By taking the virtual modal parameters as the action space of the dynamic agent, a DRL enhanced agent VRPF-SAC is created and trained under the dynamic milling environment from virtual repulsive planning field. Four tasks of large size robotic milling simulations and experiments are designed and developed to validate the effectiveness of the flexible planning model. In comparison with the static model, the flexible planning model displays the excellent global effects covering the kinematic, and precision performance, with 65.78 %, 72.04 %, and 70.25 % reduction in velocity, acceleration, and jerk of robotic joints, and ensuring the effective precision performance from −0.365 to 0.283 mm of large size workpiece with large curvature changes. The proposed flexible posture planning architecture can provide a basic for robotic dynamic milling of core parts with large size, narrow space and sharp curvatures of the aeronautic parts, aerospace cabins and worship propellers.

Surface morphology, burr formation, and spindle axial drift in high-speed robotic milling of complex features

Industrial robots are garnering increased attention in the manufacturing sector, their application in high-precision tasks such as milling is however hindered by inherent limitations, notably low stiffness and instability. This study aims to investigate the influence of cutting parameters within segmented curve trajectories on surface quality during high-speed robotic milling process. The investigation covers surface morphology, the mechanism of burr formation, and the underlying causes of tool axial runout. The findings reveal that as spindle speed increases and feed speed decreases, the surface topography of the machined workpiece gradually improves and the quantity and dimensions of burrs on both sides of the slot have been decreased. Axial runout occurs during robotic milling due to insufficient robot stiffness, leading to vibrations that cannot effectively resist the reaction forces of cutting forces.

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

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

Influence of different position modal parameters on milling chatter stability of orthopedic surgery robots

This research is dedicated to exploring the dynamics of milling chatter stability in orthopedic surgery robots, focusing on the impact of position modal parameters on chatter stability. Initially, we develop a dynamic milling force model for the robotic milling process that integrates both modal coupling and regenerative effects. We then employ the zero-order frequency domain method to derive a chatter stability domain model, visually represented through stability lobe diagrams (SLDs). Through conducting hammer test experiments, we ascertain the robot's modal parameters at varying positions, enabling the precise generation of SLDs. This study also includes experimental validation of the chatter SLD analysis method, laying the groundwork for further examination of chatter stability across different positional modal parameters. Finally, our analysis of the variations in modal parameters on the stability of robot milling chatter yields a theoretical framework for optimizing cutting parameters and developing control strategies within the context of orthopedic surgery robots.

A novel mode coupling mechanism for predicting low-frequency chatter in robotic milling by providing a vibration feedback perspective

The low-frequency chatter (LFC) of the robot body during milling severely constrains the machining efficiency. Taking the classical mode coupling mechanism as the underlying foundation, previous studies have developed various stability models to predict the LFC in robotic milling. However, the classical mode coupling mechanism has recently been proven to be applicable only to operations such as threading and boring, showing significant deviations in robotic milling. In response, this paper proposes a novel vibration feedback-based mode coupling (VF-MC) mechanism applicable to robotic milling based on robotic modal directionality, which describes the coupling and feedback process of modal vibration among multiple modes through dynamic cutting forces. The feedback process is modeled considering the time delay effect. The LFC can be judged by the trend of the amplitudes of the feedback vibrations. The effectiveness and efficiency of the VF-MC model for predicting the LFC are verified by comparing with the milling experiment results and the traditional mode coupling chatter model. Furthermore, the dynamical conditions for the LFC are investigated based on the VF-MC mechanism with the experimental evidence. The core contribution of this paper is to quantitatively reveal the feedback mechanism of low-frequency self-excited vibration among modes in robotic milling, and further develop the VF-MC model to efficiently predict the LFC. The code for the VF-MC model is available at https://github.com/Dr-JwWu/VF-MC__A-chatter-model-for-robotic-milling.

Stability prediction for robotic milling based on tool tip frequency response prediction by considering the interface stiffness of spindle-tool system

The change of tool tip frequency response caused by the posture dependence of robot dynamic is one of the key problems that make it difficult to accurately predict the milling stability of robot. In this paper, a tool tip frequency response prediction method considering the interface stiffness characteristics of spindle-tool system is proposed for stability prediction of robotic milling under the condition of posture variation. Firstly, the interface stiffness models of spindle-toolholder, toolholder-spring clip and spring clip-tool are established based on Yoshimura's unit area method. Then, The dynamics model for the robot body and the interface stiffness models for spindle-tool system are imported into the finite element analysis model of the spindle system, so that the prediction of the tool tip frequency response is realized by harmonic response analysis. Compared with the experimental results, the maximum error of the natural frequency was not more than 2 %, and the maximum error of the amplitude was not more than 12 %. Finally, the 2 DOF robot milling stability prediction model is established. Then the robot milling chatter is predicted considering redundant degrees of freedom from the perspective of regenerative chatter prediction theory, and the accuracy of prediction results is verified by milling experiment.

A novel method of suppressing machining vibration in robotic milling using magneto-rheological foam damper

Owing to their remarkable flexibility and favorable cost-effectiveness, industrial robots have found extensive applications to cutting of materials across sophisticated manufacturing fields. However, the structurally low rigidity of these robots renders the tool tip prone to substantial oscillations during machining processes, significantly impacting product fabrication quality. The objective of this study is to present a novel methodology employing magnetorheological dampers for mitigating vibrations during robotic milling operations. Specifically, a new type of ring nested Magneto-Rheological Foam Damper (MRFD) working in the squeeze mode is developed. Firstly, the MRFD's structure is designed considering the vibrational characteristics of robotic milling. Subsequently, a damping force model of the MRFD is derived. The feasibility of the MRFD’s structural design is validated by the finite element analyses, which is instrumental in comprehending the influence of structural parameters on the electromagnetic characteristics of the MRFD. Next, a prototype of the MRFD is fabricated selecting appropriate parameters. Finally, a series of excitation and milling experiments are conducted on a KUKA KR500 robot. The outcomes demonstrate a substantial reduction (37%-69%) in radial vibration amplitude at the tool tip during robotic milling, highlighting the effectiveness of the developed MRFD. This research endeavor has introduced a pioneering avenue and framework for vibration control in robotic milling, offering a novel paradigm for enhancing the precision of robotic machining.

Understanding the tool wear mechanism during robotic milling of glass fibre reinforced plastic

Glass fiber reinforced plastics (GFRP) are widely appiled for high-value components, but it faces limitations due to the heterogeneity and limited milling tool life. This study investigates the wear mechanism of tools in varying states of wear during the milling of GFRP under different combinations of cutting parameters. The findings demonstrate a significant increase in flank wear values of new tools, initially worn tools, moderate worn tools and severely worn tools associated with the large material removal rate (LMRR), exhibiting respective increments of 56.7%, 59.8%, 74.3%, and 26.5% in contrast to the small material removal rate (SMRR). The wear mechanisms in distinct cutting regions of the tool were systematically classified and characterized. The flank face and tool nose primarily experience abrasion and adhesion, while the rake face and cutting edge predominantly display characteristics of microchipping and abrasive wear. In contrast, the bottom edge is exclusively impacted by microchipping. The deposition of chemical elements on the cutting edge was examined through electron dispersive spectroscopy (EDS) and scanning electron microscopy (SEM). A comprehensive investigation was undertaken to elucidate the progression of tool wear. The findings indicate that the principal factors influencing tool wear in LMRR are adhesive wear resulting from cutting heat and chipping wear arising from substantial cutting loads. In contrast, tool wear during SMRR is predominantly attributed to abrasive wear caused by the presence of fiber-based hard points within GFRP workpieces. The wear characteristics of milling tools during the machining of GFRP are systematically investigated and analyzed. This study holds theoretical significance in guiding the design of tool materials for GFRP milling operations.