An approximation method for the numerical solution of planar discontinuous dynamical systems with stick-slip friction

Olejnik, Paweł; Awrejcewicz, Jan; Fečkan, Michal

doi:10.12988/ams.2014.44282

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…In this study, the part remains in contact with the oscillating platform, so that transport is only because of frictional sliding. A good reading for stick-slip mechanism could be found in Olejnik et al (2014), Pawełand and Awrejcewicz (2013) work, which described numerical estimations of the stick-slip transitions existing in the Filippovtype discontinuous dynamical systems with dry friction.…”

Section: Introductionmentioning

confidence: 95%

Modeling and analysis of vibratory feeder system based on robust stick–slip motion

Mayyas

2021

Journal of Vibration and Control

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This study presents model and analysis of a robust one-dimensional stick–slip transportation of an object, on dry contact with an oscillating platform suspended by nonlinear leaf spring. The oscillating platform is designed and modeled such that the elastic spring constant is direction dependent. A recursive analytical solution algorithm is proposed to solve nonlinear system dynamics. The system and the input force parameters are both investigated with goal to optimize the average velocity of the object. Both experimental and analytical results showed that the stick–slip motion is highly nonlinear and sensitive to the system and input force parameters. Moreover, the simulations showed that it is feasible to design a platform with robust sets of parameters (natural frequencies) and further use the input force parameters (amplitude and driving frequency) to tune for desired average velocity, under given dry friction conditions.

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Section: Introductionmentioning

confidence: 95%

Modeling and analysis of vibratory feeder system based on robust stick–slip motion

Mayyas

2021

Journal of Vibration and Control

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“…However, achieving accurate models often requires identifying the system's parameters. One major challenge is accurately representing the impact of friction, a nonlinear phenomenon that affects the efficiency of these systems [37,38].…”

Section: Dynamic Modeling With Frictionmentioning

confidence: 99%

Time-Series Machine Learning Techniques for Modeling and Identification of Mechatronic Systems with Friction: A Review and Real Application

Ayankoso,

Olejnik

2023

Electronics

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Developing accurate dynamic models for various systems is crucial for optimization, control, fault diagnosis, and prognosis. Recent advancements in information technologies and computing platforms enable the acquisition of input–output data from dynamical systems, resulting in a shift from physics-based methods to data-driven techniques in science and engineering. This review examines different data-driven modeling approaches applied to the identification of mechanical and electronic systems. The approaches encompass various neural networks (NNs), like the feedforward neural network (FNN), convolutional neural network (CNN), long short-term memory (LSTM), transformer, and emerging machine learning (ML) techniques, such as the physics-informed neural network (PINN) and sparse identification of nonlinear dynamics (SINDy). The main focus is placed on applying these techniques to real-world problems. A real application is presented to demonstrate the effectiveness of different machine learning techniques, namely, FNN, CNN, LSTM, transformer, SINDy, and PINN, in data-driven modeling and the identification of a geared DC motor. The results show that the considered ML techniques (traditional and state-of-the-art methods) perform well in predicting the behavior of such a classic dynamical system. Furthermore, SINDy and PINN models stand out for their interpretability compared to the other data-driven models examined. Our findings explicitly show the satisfactory predictive performance of six different ML models while also highlighting their pros and cons, such as interpretability and computational complexity, using a real-world case study. The developed models have various applications and potential research areas are discussed.

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Friction modelling and the use of a physics-informed neural network for estimating frictional torque characteristics

Olejnik,

Ayankoso

2023

Meccanica

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This paper presents an exploration of friction modeling encompassing theoretical and practical aspects, utilizing a planar or 2D contact system. Various white-box friction models, including static and dynamic variants, are introduced, highlighting the superior capability of dynamic models in comprehensively capturing friction effects, substantiated through numerical simulation. Practical aspects of friction measurement and data-driven friction modeling are elucidated. The discourse extends to the development of grey-box and black-box friction models. A significant contribution lies in the proposition of a physics-informed neural network-based friction modeling approach, presenting it as an advanced and preferable alternative for friction estimation. To exemplify its efficacy, a case study of a torsion-based frictional contact scenario, employing Physics-Informed Neural Network (PINN) and the Nelder–Mead (N–M) algorithm for concurrent dynamics and friction model identification, was examined. Experimental data from a double torsion pendulum system, characterized by discontinuous dynamics, is employed for training. Results demonstrate the PINN’s superiority, providing more accurate representation of stick–slip phases at the contact zone and exhibiting faster performance compared to the N–M algorithm. The paper concludes by deliberating on challenges, prospects, and future directions in friction modeling.

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An approximation method for the numerical solution of planar discontinuous dynamical systems with stick-slip friction

Cited by 7 publications

References 29 publications

Modeling and analysis of vibratory feeder system based on robust stick–slip motion

Modeling and analysis of vibratory feeder system based on robust stick–slip motion

Time-Series Machine Learning Techniques for Modeling and Identification of Mechatronic Systems with Friction: A Review and Real Application

Friction modelling and the use of a physics-informed neural network for estimating frictional torque characteristics

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