A 3D study of the influence of friction on the Adiabatic Shear Band formation during High Speed Machining

International audienceAdiabatic Shear Band (ASB) may form at very high speeds with materials having poor thermal conductivity. It results from the competition between plastic hardening and strain softening and initiates when the latter becomes predominant. Because of high speeds, the heat created does not have sufficient time to propagate, leading to the formation of high strain localized zones. Starting from a previous description of the ASB formation process where the friction phenomenon has been considered as negligible, the aim of this paper is to describe the influence of the latter on the ASB formation process. Consequently, using a very general 3D finite element code where mesh adaptation is triggered by an error estimator within an Arbitrary Lagrangian Eulerian formulation, the formation of several ASB has been simulated in 3D High Speed Machining taking friction into account. The results obtained allow proposing a description of its influence on both the ASB build up process and the final chip geometry

3D simulation of adiabatic shear bands in high speed machining

Reprinted with permission from AIP Conf. Proc May 17, 2007 Volume 908, pp. 1137-1142 MATERIALS PROCESSING AND DESIGN; Modeling, Simulation and Applications; NUMIFORM '07; Proceedings of the 9th International Conference on Numerical Methods in Industrial Forming Processes; doi:10.1063/1.2740963. Copyright 2007 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of PhysicsInternational audienceA finite element model of three-dimensional high speed machining is developed. In order to catch Adiabatic Shear Band (ASB), which is about few microns wide, the simulation uses mesh adaptation triggered by an isotropic error estimator. An enhanced version of the Zienkiewicz and Booromand REP in Patches technique is used. As ASB is a much localized phenomenon, the adaptive procedure provides highly refined meshes with strong gradients of the element size, which makes it quite difficult to produce satisfactory 3D meshes. Furthermore, high speed machining leads to very important values of strain rate, deformation and possibly to extreme mesh distortion. So, an Arbitrary Lagrangian Eulerian (ALE) method is employed. With the utilized splitting method and linear finite element interpolation, the transport of nodal variables is based on the gradient calculated in the upwind element. For variables stored at the integration points, a remapping procedure using patch recovery techniques is preferred. Finally, because of the very strong thermo-mechanical coupling taking place in ASB, several thermo-mechanical coupling schemes are studied. Explicit and fully implicit schemes are compared, showing that the second one offers a stabilizing effect and a better accuracy. All of these ingredients provide a fully automatic and process independent procedure which allows detecting and following the formation of Adiabatic Shear Band in High Speed Machining. The creation of 3D segmented chip is observed and compared to 2D reference results obtained by Baker in [1]. The influence of numerical coefficients like the mesh size is investigated. Other application to actual 3D high speed machining such as blanking is also presented

Reconstruction and simulation of neocortical microcircuitry

We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. An objective anatomical method defines a neocortical volume of 0.29 ± 0.01 mm3 containing ∼31,000 neurons, and patch-clamp studies identify 55 layer-specific morphological and 207 morpho-electrical neuron subtypes. When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ∼8 million connections with ∼37 million synapses. Simulations reproduce an array of in vitro and in vivo experiments without parameter tuning. Additionally, we find a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms. The spectrum of network states, dynamically reconfigured around this transition, supports diverse information processing strategies

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Modélisation et étude 3D des phénomènes de cisaillement adiabatiques dans les procédés de mise en forme à grande vitesse

Despite promising results high-speed machining is still not widely used in manufacturing due to the lack of understanding in Adiabatic Shear Band (ASB) phenomenon.This work presents the development of numerical tools to support both the adaptive simulation and the physical study of ASB in 3D high speed machining process. The use of an adaptive Arbitrary Lagrangian Eulerian (ALE) sequential model developed within Forge3 software allows automatically simulating successive ASB in 3D for the first time. The physical study of the numerical results allows proposing an innovative description of ASB formation. Due to the need for extensive computational resource, a new highly parallelized finite element code called Forge++ is developed. It includes new numerical algorithms such as fully implicit coupling, Residual Free Buble (RFB) stabilization and parallel recovery by patch methods that support better modeling ASB.Malgré des résultats prometteurs, les procédés de mise en forme à grande vitesse sont encore peu utilisés dans l'industrie du fait d'un manque de compréhension du phénomène de Bande de Cisaillement Adiabatique (BCA).Ce travail présente le développement d'outils numériques permettant la simulation adaptative et l'analyse de BCA dans des procédés 3D de mise en forme à grande vitesse. L'utilisation du modèle ALE-adaptatif séquentiel développé dans le logiciel Forge3 permet pour la première fois la simulation automatique de BCA 3D. L'étude des résultats numériques permet de proposer une description innovante du processus de formation de BCA. Les moyens de calcul requis s'avérant très importants, un nouveau code éléments finis hautement parallèle appelé Forge++ est développé. Ce dernier inclut de nouveaux algorithmes tels que le couplage thermomécanique implicite, la méthode de stabilisation RFB et un recouvrement par patch parallèle pour une meilleure simulation de BCA

Modélisation et étude 3D des phénomènes de cisaillement adiabatiques dans les procédés de mise en forme à grande vitesse

PARIS-MINES ParisTech (751062310) / SudocSOPHIA ANTIPOLIS-Mines ParisTech (061522302) / SudocSudocFranceF

Cyme: A Library Maximizing SIMD Computation on User-Defined Containers

This paper presents Cyme, a C++ library aiming at abstracting the usage of SIMD instructions while maximizing the usage of the underlying hardware. Unlike similar efforts such as Boost.simd or VC, Cyme provides generic high level containers to the users which hides SIMD complexity. Cyme accomplishes this by 1) optimization of the Abstract Syntax Tree using Expression Templates Programming to prevent temporary copies and maximize the use of Fuse Multiply Add instructions and 2) creating a data layout in memory (AoS or AoSoA), which minimizes data addressing and manipulation throughout all SIMD registers. Implementation of Cyme library has been accomplished on the IBM Blue Gene/Q architecture using the 256 bit SIMD extensions (QPX) of the Power A2 processor. Functionality of the library is demonstrated on a computationally intensive kernel of a neuro-scientific application where an increase of GFlop/s performance by a factor of 6.72 over the original implementation is observed using Clang compile

Polynomial Evaluation on Superscalar Architecture, Applied to the Elementary Function e(x)

The evaluation of small degree polynomials is critical for the computation of elementary functions. It has been extensively studied and is well documented. In this article, we evaluate existing methods for polynomial evaluation on superscalar architecture. In addition, we have completed this work with a factorization method, which is surprisingly neglected in the literature. This work focuses on out-of-order Intel processors, amongst others, of which computational units are available. Moreover, we applied ourwork on the elementary function ex that requires, in the current implementation, an evaluation of a polynomial of degree 10 for a satisfying precision and performance. Our results show that the factorization scheme is the fastest in benchmarks, and that latency and throughput are intrinsically dependent on each other on superscalar architecture

CoreNEURON : An Optimized Compute Engine for the NEURON Simulator

The NEURON simulator has been developed over the past three decades and is widely used by neuroscientists to model the electrical activity of neuronal networks. Large network simulation projects using NEURON have supercomputer allocations that individually measure in the millions of core hours. Supercomputer centers are transitioning to next generation architectures and the work accomplished per core hour for these simulations could be improved by an order of magnitude if NEURON was able to better utilize those new hardware capabilities. In order to adapt NEURON to evolving computer architectures, the compute engine of the NEURON simulator has been extracted and has been optimized as a library called CoreNEURON. This paper presents the design, implementation, and optimizations of CoreNEURON. We describe how CoreNEURON can be used as a library with NEURON and then compare performance of different network models on multiple architectures including IBM BlueGene/Q, Intel Skylake, Intel MIC and NVIDIA GPU. We show how CoreNEURON can simulate existing NEURON network models with 4-7x less memory usage and 2-7x less execution time while maintaining binary result compatibility with NEURON