Methodology for model-based uncertainty quantification of the vibrational properties of machining robots

Busch, Maximilian; Schnoes, Florian; Elsharkawy, Amr; Zaeh, Michael F.

doi:10.1016/j.rcim.2021.102243

Cited by 15 publications

(8 citation statements)

References 23 publications

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“…Occasionally, publications on robot vibrations present resonance curves that show the dependence of the resonance frequencies on the position of the robot arm [31,32]. They are prepared on the basis of modal experiments in which the vibration is excited by an impact hammer pulse.…”

Section: Natural Vibrations Of a Linear Systemmentioning

confidence: 99%

Analysis of Bifurcation Vibrations of an Industrial Robot Arm System with Joints Compliance

Gierlak,

Warmiński

2023

Applied Sciences

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This paper deals with the analysis of nonlinear vibration phenomena in the arm system of an industrial robot. The presented mathematical model of the robot takes into account the flexibility of the joints, which results in low-frequency vibrations in the arm. The study of vibration phenomena has been carried out using analytical, numerical and experimental methods. Special attention has been paid to bifurcation phenomena. The influence of two bifurcation parameters on the behaviour of the robot arm was studied. It was shown that the amplitude and frequency of the harmonic excitation create a nonlinear effect on the vibration amplitudes of the links. In bifurcation regions, the vibration amplitudes can even differ several times depending on the direction of changes in the bifurcation parameter. In general, the results indicate that, contrary to common practice, it is worth paying attention to nonlinear phenomena when analysing robot vibrations.

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Section: Natural Vibrations Of a Linear Systemmentioning

confidence: 99%

Analysis of Bifurcation Vibrations of an Industrial Robot Arm System with Joints Compliance

Gierlak,

Warmiński

2023

Applied Sciences

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“…In their efforts to comprehensively model a robot's vibrational attributes, ref. [17] endeavored to quantify the uncertainty linked with eigenfrequency prediction, leveraging the precision of Monte Carlo uncertainty propagation.…”

Section: Introductionmentioning

confidence: 99%

Unveiling the Black Box: A Unified XAI Framework for Signal-Based Deep Learning Models

Shojaeinasab,

Jalayer,

Baniasadi

et al. 2024

Machines

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Condition monitoring (CM) is essential for maintaining operational reliability and safety in complex machinery, particularly in robotic systems. Despite the potential of deep learning (DL) in CM, its ‘black box’ nature restricts its broader adoption, especially in mission-critical applications. Addressing this challenge, our research introduces a robust, four-phase framework explicitly designed for DL-based CM in robotic systems. (1) Feature extraction utilizes advanced Fourier and wavelet transformations to enhance both the model’s accuracy and explainability. (2) Fault diagnosis employs a specialized Convolutional Long Short-Term Memory (CLSTM) model, trained on the features to classify signals effectively. (3) Model refinement uses SHAP (SHapley Additive exPlanation) values for pruning nonessential features, thereby simplifying the model and reducing data dimensionality. (4) CM interpretation develops a system offering insightful explanations of the model’s decision-making process for operators. This framework is rigorously evaluated against five existing fault diagnosis architectures, utilizing two distinct datasets: one involving torque measurements from a robotic arm for safety assessment and another capturing vibration signals from an electric motor with multiple fault types. The results affirm our framework’s superior optimization, reduced training and inference times, and effectiveness in transparently visualizing fault patterns.

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“…Several studies achieved vibration suppression using mechanical modeling given specific vibration properties [11] [12] [17]. The others [13][18]- [24] proposed methods for identifying the vibration properties in mechanical models either through the use of sensors or by experimental exploration using an optimization algorithm. However, as described earlier, an accurate estimation of the vibrational behavior of soft robotic hands is challenging because of the low reproducibility of motion, which limits their adoption in soft robotic hand systems.…”

Section: Introductionmentioning

confidence: 99%

“…This study develops a parameter-tuning method based on the Bayesian optimization algorithm [25]- [29], which is noted for its efficiency in parameter search among various optimization methods. As shown in Table II, various other optimization methods can achieve optimization with a small number of search trials, [17] ✘Required (assumed to be given) -Using high speed camera [14]- [16] ✘Required (obtained by sensors) Using internal sensors [13] [18] SSA-based method* [19] ✘Required (identified by searching) PSO-based method* [20] BO-based method* [21] Hardware-based method Installing additional structures or mechanisms [7]- [10] ✘Required -* PSO, SHA, BO, and SSA refer to particle swarm optimization, S. Lin's heuristic algorithm, Bayesian optimization, and sparrow search algorithm, respectively.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Motion Parameters in Soft Robotic Hands Using Bayesian Optimization: Enhancing Cycle Time, Addressing Vibration, and Repeatability

Nishimura,

Isogai,

Suzuki

et al. 2024

IEEE Access

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This study proposes a novel motion optimization method for a manipulator equipped with a soft robotic hand for object transportation. The flexibility of the soft robotic hand induces large vibrations. The manipulator must pause until the vibration converges, leading to an increase in the cycle time. The robotic system also has issue with the low motion repeatability in the robotic hand, even under identical operational conditions. In this study, a method based on Bayesian algorithms is developed to optimize the motion of a manipulator. The objective is to minimize the cycle time for object transportation tasks, while considering the challenges of vibration and low repeatability in soft robotic hand systems. The optimization is performed through an exploratory search using actual experiments. To achieve a low-cost and versatile measurement system, this study proposes a method for deriving the cycle time, which is a key metric for optimization, based on the measurement results obtained using a web camera with standard specifications. The proposed optimization method is evaluated through a comparison with existing optimization methods, including the grid-search-based, conventional Bayesian optimization, particle swarm optimization, S. Lin's heuristic algorithm, and sparrow search algorithm. The proposed method achieves optimization results comparable in accuracy to those obtained using the grid-search-based optimization method, whereas requires 95% fewer searches. Furthermore, it provides more stable optimization results than those obtained using the conventional Bayesian optimization method.

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Methodology for model-based uncertainty quantification of the vibrational properties of machining robots

Cited by 15 publications

References 23 publications

Analysis of Bifurcation Vibrations of an Industrial Robot Arm System with Joints Compliance

Analysis of Bifurcation Vibrations of an Industrial Robot Arm System with Joints Compliance

Unveiling the Black Box: A Unified XAI Framework for Signal-Based Deep Learning Models

Optimizing Motion Parameters in Soft Robotic Hands Using Bayesian Optimization: Enhancing Cycle Time, Addressing Vibration, and Repeatability

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