A5003869841- Adhesion, Friction, and Surface Interactions
- Mechanical stress and fatigue analysis
- Model Reduction and Neural Networks
- Modular Robots and Swarm Intelligence
- Contact Mechanics and Variational Inequalities
- Micro and Nano Robotics
- Textile materials and evaluations
- Machine Learning in Materials Science
- Soft Robotics and Applications
- Gear and Bearing Dynamics Analysis
- Advanced Materials and Mechanics
- Probabilistic and Robust Engineering Design
- Distributed and Parallel Computing Systems
- Graph Theory and Algorithms
- Simulation Techniques and Applications
- Dynamics and Control of Mechanical Systems
- Tribology and Wear Analysis
- Quantum and electron transport phenomena
- Oil and Gas Production Techniques
- Elasticity and Material Modeling
- Industrial Engineering and Technologies
- Robot Manipulation and Learning
- Structural Engineering and Materials Analysis
- Gaussian Processes and Bayesian Inference
- Brake Systems and Friction Analysis
Institute of Fundamental Technological Research
2014-2024
Polish Academy of Sciences
2014-2024
University of Luxembourg
2020-2024
École Polytechnique Fédérale de Lausanne
2018
For many novel applications, such as patient-specific computer-aided surgery, conventional solution techniques of the underlying nonlinear problems are usually computationally too expensive and lacking information about how certain can we be their predictions. In present work, propose a highly efficient deep-learning surrogate framework that is able to accurately predict response bodies undergoing large deformations in real-time. The model has convolutional neural network architecture,...
In many cutting-edge applications, high-fidelity computational models prove to be too slow for practical use and are therefore replaced by much faster surrogate models. Recently, deep learning techniques have increasingly been utilized accelerate such predictions. To enable on large-dimensional complex data, specific neural network architectures developed, including convolutional graph networks. this work, we present a novel encoder–decoder geometric framework called MAgNET, which extends...
Solid contacts involving soft materials are important in mechanical engineering or biomechanics. Experimentally, such have been shown to shrink significantly under shear, an effect which is usually explained using adhesion models. Here we show that quantitative agreement with recent high-load experiments can be obtained, no adjustable parameter, a non-adhesive model, provided finite deformations taken into account. Analysis of the model uncovers basic mechanisms underlying shear-induced area...
Due to its multifactorial nature, skin friction remains a multiphysics and multiscale phenomenon poorly understood despite relevance for many biomedical engineering applications (from superficial pressure ulcers, through shaving cosmetics, automotive safety sports equipment). For example, it is unclear whether, in which measure, the microscopic surface topography, internal microstructure associated nonlinear mechanics can condition modulate friction. This study addressed this question...
Deep learning surrogate models are being increasingly used in accelerating scientific simulations as a replacement for costly conventional numerical techniques. However, their use remains significant challenge when dealing with real-world complex examples. In this work, we demonstrate three types of neural network architectures efficient highly non-linear deformations solid bodies. The first two based on the recently proposed CNN U-NET and MAgNET (graph U-NET) frameworks which have shown...
Abstract The aim of this paper is to present a general method for automation finite element formulations large deformation contact problems. A new automatic‐differentiation‐based notation introduced that represents bridge between the classical mathematical mechanics and actual computer implementation elements. Automation derivation required formulas (e.g. residual tangent matrix) combined with automatic code generation makes possible at moderate effort. Accordingly, several 3D have been...
The role of contact pressure on skin friction has been documented in multiple experimental studies. Skin significantly raises the low-pressure regime as load increases while, after a critical value is reached, coefficient against an external surface becomes mostly insensitive to pressure. However, up now, no study elucidated qualitative and quantitative nature interplay between pressure, material microstructural properties skin, size indenting slider resulting measured macroscopic friction....
We propose a methodology of planning effective shape shifting and locomotion large-ensemble modular robots based on cubic lattice. The modules are divided into two groups: fixed ones, that build rigid porous frame, mobile flow through the frame. Mobile which out structure attach to advancing its boundary. Conversely, deficiency in other parts boundary is corrected by decomposition Inside structure, appropriate module arranged transport desired direction, planned special distributed version...
We present a distributed framework for predicting whether planned reconfiguration step of modular robot will mechanically overload the structure, causing it to break or lose stability under its own weight. The algorithm is executed by itself and based on iterative solution mechanical equilibrium equations derived from simplified model robot. treats inter-modular connections as beams assumes no-sliding contact between modules ground. also provide procedure instability detection. verified in...
Being able to reposition tumors from prone imaging supine surgery stances is key for bypassing current invasive marking used conservative breast surgery. This study aims demonstrate the feasibility of using Digital Volume Correlation (DVC) measure deformation a female quarter thorax between two different body positioning when subjected gravity. A segmented multipart mesh (bones, cartilage and tissue) was constructed three-step FE-based DVC procedure with heterogeneous elastic regularization...
Abstract The key novelty of this contribution is a dedicated technique to efficiently determine the distance (gap) function between parallel or almost beams with circular and elliptical cross-sections. consists parametrizing surfaces two in contact, fixing point on centroid line one searching for constrained minimum (two variants are investigated). resulting unilateral (frictionless) contact condition then enforced Penalty method, which introduces compliance the, otherwise rigid, beams’ Two...
In many cutting-edge applications, high-fidelity computational models prove to be too slow for practical use and are therefore replaced by much faster surrogate models. Recently, deep learning techniques have increasingly been utilized accelerate such predictions. To enable on large-dimensional complex data, specific neural network architectures developed, including convolutional graph networks. this work, we present a novel encoder-decoder geometric framework called MAgNET, which extends...
The term Programmable Matter (PM) describes the class of future meta-materials programmable and controllable properties behavior, e.g., able to autonomously transform into an arbitrary shape. robotic approaches towards PM are based on concept cooperation millions micro-robots (modules), acting at a very fine length-scale collectively imitating deformation macroscopically continuous material. Recent ideas about reconfiguration collective modules obtain desired overall mechanical response...
SUMMARY We propose a new class of modular-robotic structures, intended to produce forces which scale with the number modules. adopt concept spherical catom and extend it by connection type is relatively strong but static. examine analytically numerically mechanical properties two collective-actuator designs. The simulations are based on discrete element method (DEM), friction elastic deformations taken into account. One actuators shown generate proportional its volume. This property seems...
The macroscopic behaviors of materials are determined by interactions that occur at multiple lengths and time scales. Depending on the application, describing, predicting, understanding these may require models rely insights from atomic electronic In such cases, classical simplified approximations those scales insufficient, quantum-based modeling is required. this paper, we study how quantum effects can modify mechanical properties systems relevant to engineering. We base our a high-fidelity...