Tool wear in terms of vibration effects in milling medium-density fibreboard with an industrial robot

Tratar, Janez; Pušavec, Franci; Kopač, Janez

doi:10.1007/s12206-014-1010-9

Cited by 18 publications

(6 citation statements)

References 11 publications

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“…where e wmin and e wmax are the eigenvectors of A w corresponding to λ wmin and λ wmax respectively. Equations (19) and (20) give the range of the displacement-torque frequency response ratio η w and the amplitudes and phases of the torque components for the boundary values of η w . The maximum frequency response ratios η vmax and η wmax provide a measure of the maximum amplitudes of tool displacement due to harmonic loading with an amplitude of unit force and unit torque respectively, and the corresponding eigenvectors represent the amplitudes and phases of the force and torque components under which the worst vibration situation occurs.…”

Section: Frequency Response Ratiosmentioning

confidence: 99%

“…Since the natural frequencies of an industrial robot strongly depend on its configuration [15] and its stiffness varies significantly in different directions [16,17], the vibration characteristics depend on the robot configuration and the direction of external force. It has been found that structural vibrations of the robot can cause tool misalignment [18], damage the surface quality [19], and reduce the fatigue lives of the tool and the robot [20,21]. Furthermore, the system can become instable due to the interaction between the robot vibrations and the process forces [22,23] and the stability condition can be affected by some process parameters such as feed rate and spindle speed [24,25].…”

Section: Introductionmentioning

confidence: 99%

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New Indices for Evaluating Vibration Characteristics of Flexible-Joint Robots

Zhang

2020

Applied Sciences

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Structural vibration is a significant consideration for robotic applications such as machining where the robot is subject to large dynamic loading. Aiming at providing an efficient means to evaluating the vibration characteristics of industrial robots for these applications, this work proposes two new indices to quantify the elastic displacement of the tool mounted on the robot caused by the vibrations induced by external process loading for flexible-joint robots. For this purpose, a structural dynamic model is first developed to derive the frequency responses of the tool displacement. Then, the displacement-force and displacement-torque frequency response ratios are defined, which represent the mapping from the amplitudes of an external harmonic force and torque to the amplitude of tool displacement respectively. The upper bounds of the two ratios are used as evaluation indices for the vibration characteristics of the robot, which represent the worst situation of the tool displacement due to harmonic excitation with amplitude of unit force and unit torque respectively. With these indices, an efficient method is provided to predict whether the tool misalignment caused by periodic loading is acceptable for process quality requirement. Numerical simulation demonstrates the effectiveness of the proposed method for a robotic riveting system being developed for aerospace assembly.

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Section: Frequency Response Ratiosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New Indices for Evaluating Vibration Characteristics of Flexible-Joint Robots

Zhang

2020

Applied Sciences

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“…The resulting forced vibrations of the robot structure lead to much more complicated dynamics behaviors than many other robotic tasks such as pick-and-place and welding where static or very low contact forces act on the robot. First, the forced vibrations of the robot can seriously affect the process, for example, by decreasing the efficiency [17], damaging the surface quality [18], causing tool misalignment [19], and reducing the fatigue lives of the tool and the robot to a great extent [20,21]. Second, not only static but also inertial forces are important for these applications.…”

Section: Introductionmentioning

confidence: 99%

Workpiece Pose Optimization for Milling with Flexible-Joint Robots to Improve Quasi-Static Performance

Qin

Xiong

2019

Applied Sciences

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Although industrial robots are widely used in production automation, their applications in machining have been limited because of the structural vibrations induced by periodic cutting forces. Since the dynamic characteristics of an industrial robot depends on its configuration, the responses of the robot structure to the cutting forces are affected by how the workpiece is placed within the workspace of the robot. This paper presents a method for workpiece pose optimization for a robotic milling system to improve the quasi-static performance during machining. Since the milling forces are time-varying due to the characteristics of the multi-tooth and discontinuity of milling, these forces can excite vibrations inside the robot structure. To address this issue, a structural dynamics model is established for industrial robots, considering their joint flexibility, and a milling force formulation is incorporated into the robot dynamics model to investigate the forced vibrations of the flexible joints. Then, the quasi-static performance of the robotic machining system is evaluated by the vibration-induced offset of the cutter tool that is mounted on the end-effector. Finally, an optimization approach is given for the workpiece pose to minimize the cutter tool offset under the periodic milling force. A numerical simulation demonstrates that the optimal workpiece pose can significantly reduce the overall tool offset during machining and can lower the variation of the tool offset along the milling path.

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“…As an engineering product, medium density fiberboard (MDF) is used especially in furniture industry applications because of its better machinability, surface characteristics and dimensional stability (Bütün et al , 2019). Researchers are mainly focused on drilling, facing and milling with the machining of MDF (Dippon et al , 2000; Davim et al , 2008; Benlatreche et al , 2009; Kowaluk et al , 2009; Tratar et al , 2014; De Deus et al , 2015; Koc et al , 2017; Igaz et al , 2019; Chladil et al , 2019; İşleyen and Karamanoğlu, 2019; Dąbrowski and Górski, 2019; Uriarte et al , 2008; Díaz-Tena et al , 2013; Beranoagirre et al , 2019). Palanikumar et al researched the machining of MDF.…”

Section: Introductionmentioning

confidence: 99%

Modeling for prediction of surface roughness in milling medium density fiberboard with a parallel robot

Ayyıldız

2019

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Purpose This paper aims to discuss the utilization of artificial neural networks (ANNs) and multiple regression method for estimating surface roughness in milling medium density fiberboard (MDF) material with a parallel robot. Design/methodology/approach In ANN modeling, performance parameters such as root mean square error, mean error percentage, mean square error and correlation coefficients (R2) for the experimental data were determined based on conjugate gradient back propagation, Levenberg–Marquardt (LM), resilient back propagation, scaled conjugate gradient and quasi-Newton back propagation feed forward back propagation training algorithm with logistic transfer function. Findings In the ANN architecture established for the surface roughness (Ra), three neurons [cutting speed (V), feed rate (f) and depth of cut (a)] were contained in the input layer, five neurons were included in its hidden layer and one neuron was contained in the output layer (3-5-1).Trials showed that LM learning algorithm was the best learning algorithm for the surface roughness. The ANN model obtained with the LM learning algorithm yielded estimation training values R2 (97.5 per cent) and testing values R2 (99 per cent). The R2 for multiple regressions was obtained as 96.1 per cent. Originality/value The result of the surface roughness estimation model showed that the equation obtained from the multiple regressions with quadratic model had an acceptable estimation capacity. The ANN model showed a more dependable estimation when compared with the multiple regression models. Hereby, these models can be used to effectively control the milling process to reach a satisfactory surface quality.

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Tool wear in terms of vibration effects in milling medium-density fibreboard with an industrial robot

Cited by 18 publications

References 11 publications

New Indices for Evaluating Vibration Characteristics of Flexible-Joint Robots

New Indices for Evaluating Vibration Characteristics of Flexible-Joint Robots

Workpiece Pose Optimization for Milling with Flexible-Joint Robots to Improve Quasi-Static Performance

Modeling for prediction of surface roughness in milling medium density fiberboard with a parallel robot

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