Cohesive, frictional powders: contact models for tension

The contacts between cohesive, frictional particles with sizes in the range 0.1–10 μm are the subject of this study. Discrete element model (DEM) simulations rely on realistic contact force models—however, too much details make both implementation and interpretation prohibitively difficult. A rather simple, objective contact model is presented, involving the physical properties of elastic–plastic repulsion, dissipation, adhesion, friction as well as rolling- and torsion-resistance. This contact model allows to model bulk properties like friction, cohesion and yield-surfaces. Very loose packings and even fractal agglomerates have been reported in earlier work. The same model also allows for pressure-sintering and tensile strength tests as presented in this study

Stochastic Properties of Static Friction

The onset of frictional motion is mediated by rupture-like slip fronts, which nucleate locally and propagate eventually along the entire interface causing global sliding. The static friction coefficient is a macroscopic measure of the applied force at this particular instant when the frictional interface loses stability. However, experimental studies are known to present important scatter in the measurement of static friction; the origin of which remains unexplained. Here, we study the nucleation of local slip at interfaces with slip-weakening friction of random strength and analyze the resulting variability in the measured global strength. Using numerical simulations that solve the elastodynamic equations, we observe that multiple slip patches nucleate simultaneously, many of which are stable and grow only slowly, but one reaches a critical length and starts propagating dynamically. We show that a theoretical criterion based on a static equilibrium solution predicts quantitatively well the onset of frictional sliding. We develop a Monte-Carlo model by adapting the theoretical criterion and pre-computing modal convolution terms, which enables us to run efficiently a large number of samples and to study variability in global strength distribution caused by the stochastic properties of local frictional strength. The results demonstrate that an increasing spatial correlation length on the interface, representing geometric imperfections and roughness, causes lower global static friction. Conversely, smaller correlation length increases the macroscopic strength while its variability decreases. We further show that randomness in local friction properties is insufficient for the existence of systematic precursory slip events. Random or systematic non-uniformity in the driving force, such as potential energy or stress drop, is required for arrested slip fronts. Our model and observations..

Slow slip and the transition from fast to slow fronts in the rupture of frictional interfaces

The failure of the population of micro-junctions forming the frictional interface between two solids is central to fields ranging from biomechanics to seismology. This failure is mediated by the propagation along the interface of various types of rupture fronts, covering a wide range of velocities. Among them are so-called slow fronts, which are recently discovered fronts much slower than the materials' sound speeds. Despite intense modelling activity, the mechanisms underlying slow fronts remain elusive. Here, we introduce a multi-scale model capable of reproducing both the transition from fast to slow fronts in a single rupture event and the short-time slip dynamics observed in recent experiments. We identify slow slip immediately following the arrest of a fast front as a phenomenon sufficient for the front to propagate further at a much slower pace. Whether slow fronts are actually observed is controlled both by the interfacial stresses and by the width of the local distribution of forces among micro-junctions. Our results show that slow fronts are qualitatively different from faster fronts. Since the transition from fast to slow fronts is potentially as generic as slow slip, we anticipate that it might occur in the wide range of systems in which slow slip has been reported, including seismic faults.Comment: 35 pages, 5 primary figures, 6 supporting figures. Post-print version with improvements from review process include

Multiphysics models for friction stir welding simulation

Purpose: The Friction Stir Welding (FSW) process comprises of several highly coupled (and non-linear) physical phenomena: large plastic deformation, material flow transportation, mechanical stirring of the tool, tool-workpiece surface interaction, dynamic structural evolution, heat generation from friction and plastic deformation, etc. In this paper, an advanced Finite Element (FE) model encapsulating this complex behavior is presented and various aspects associated with the FE model such as contact modeling, material model and meshing techniques are discussed in detail. Methodology: The numerical model is continuum solid mechanics-based, fully thermomechanically coupled and has successfully simulated the friction stir welding process including plunging, dwelling and welding stages. Findings: The development of several field variables are quantified by the model: temperature, stress, strain, etc. Material movement is visualized by defining tracer particles at the locations of interest. The numerically computed material flow patterns are in very good agreement with the general findings from experiments. Value: The model is, to the best of the authors’ knowledge, the most advanced simulation of FSW published in the literature

Multi-physics simulation of friction stir welding process

Purpose: The Friction Stir Welding (FSW) process comprises of several highly coupled (and non-linear) physical phenomena: large plastic deformation, material flow transportation, mechanical stirring of the tool, tool-workpiece surface interaction, dynamic structural evolution, heat generation from friction and plastic deformation, etc. In this paper, an advanced Finite Element (FE) model encapsulating this complex behavior is presented and various aspects associated with the FE model such as contact modeling, material model and meshing techniques are discussed in detail. Methodology: The numerical model is continuum solid mechanics-based, fully thermomechanically coupled and has successfully simulated the friction stir welding process including plunging, dwelling and welding stages. Findings: The development of several field variables are quantified by the model: temperature, stress, strain, etc. Material movement is visualized by defining tracer particles at the locations of interest. The numerically computed material flow patterns are in very good agreement with the general findings from experiments. Value: The model is, to the best of the authors’ knowledge, the most advanced simulation of FSW published in the literature

Friction Variability in Planar Pushing Data: Anisotropic Friction and Data-collection Bias

Friction plays a key role in manipulating objects. Most of what we do with our hands, and most of what robots do with their grippers, is based on the ability to control frictional forces. This paper aims to better understand the variability and predictability of planar friction. In particular, we focus on the analysis of a recent dataset on planar pushing by Yu et al. [1] devised to create a data-driven footprint of planar friction. We show in this paper how we can explain a significant fraction of the observed unconventional phenomena, e.g., stochasticity and multi-modality, by combining the effects of material non-homogeneity, anisotropy of friction and biases due to data collection dynamics, hinting that the variability is explainable but inevitable in practice. We introduce an anisotropic friction model and conduct simulation experiments comparing with more standard isotropic friction models. The anisotropic friction between object and supporting surface results in convergence of initial condition during the automated data collection. Numerical results confirm that the anisotropic friction model explains the bias in the dataset and the apparent stochasticity in the outcome of a push. The fact that the data collection process itself can originate biases in the collected datasets, resulting in deterioration of trained models, calls attention to the data collection dynamics.Comment: 8 pages, 13 figure