Influence of feed drives on the structural dynamics of large-scale machine tools

Milling is one of the most widely used processes in the manufacturing industry and demands machines with high productivity rates. In large machine tool applications, the cutting capability is mainly limited by the appearance of structural chatter vibrations. Chatter arises from the dynamic interaction of the machining system compliance with the cutting process. For the specific case of large-scale machine tools, the low frequency resonances have modal shapes that generate relative displacements in the machine joints. This thesis presents new approaches to minimize the appearance of chatter vibrations by targeting and understanding the machine tool compliance, in particular, from the feed drive of the machine tool. A detailed model of the double pinion and rack feed drive system and the master-slave coupling improves the large machine tools modeling. As the vibrations are measured by the axes feedback sensors, a new strategy for feed drive controller tuning allows increasing the chatter stability using a judicious selection of the servo parameters. Then, in-motion dynamic characterizations demonstrate the important influence of the nonlinear friction on the machine compliance and improve the chatter stability predictions. Finally, an operational method for characterizing both tool and workpiece side dynamics while performing a cutting operation is developed. All the contributions of the thesis have been validated experimentally and tend to consider the influence of the feed drives on the structural dynamics of large-scale machine tools

Alternative experimental methods for machine tool dynamics identification: A review

An accurate machine dynamic characterization is essential to properly describe the dynamic response of the machine or predict its cutting stability. However, it has been demonstrated that current conventional dynamic characterization methods are often not reliable enough to be used as valuable input data. For this reason, alternative experimental methods to conventional dynamic characterization methods have been developed to increase the quality of the obtained data. These methods consider additional effects which influence the dynamic behavior of the machine and cannot be captured by standard methods. In this work, a review of the different machine tool dynamic identification methods is done, remarking the advantages and drawbacks of each method.The present work has been partially supported by the EU Horizon 2020 InterQ project (958357/H2020-EU.2.1.5.1.) and the CDTI CERVERA programme MIRAGED project (EXP-00,137,312/CER-20191001)

Effect of Rack and Pinion Feed Drive Control Parameters on Machine Tool Dynamics

In large heavy-duty machine tool applications, the parametrization of the controller that is used for the positioning of the machine can affect the machine tool dynamics. The aim of this paper is to build a Multiple-Input and Multiple-Output model that couples the servo controller and machine tool dynamics to predict the frequency response function (FRF) at the cutting point. The model is experimentally implemented and validated in an electronically preloaded rack and pinion machine tool. In addition, the influence of each control parameter on the machine tool’s compliance is analysed

Perspectives on manufacturing simulations of Li-S battery cathodes

Lithium-sulfur batteries (LSBs) are one of the main contenders for next generation post lithium-ion batteries (LIBs). As the process of scientific discovery advances, many of the challenges that prevent the commercial deployment of LSBs, especially at the most fundamental materials level, are slowly being addressed. However, batteries are complex systems that require not only the identifcation of suitable materials, but also require the knowledge of how to assemble and manufacture all the components together in order to obtain an optimally working battery. This is not a simple task, as battery manufacturing is a multi-stepped, multi-parameter, highly correlated process, where many parameters compete, and deep knowledge of the systems is required in order to achieve the optimal manufacturing conditions, which has already been shown in the case of LIBs. In these regards, manufacturing simulations have proven to be invaluable in order to advance in the knowledge of this exciting and technologically relevant field. Thus, in this work, we aim at providing future perspectives and opportunities that we think are interesting in order to create digital twins for the LSB manufacturing process. We also provide comprehensive and realistic ways in which already existing models could be adapted to LSBs in the short-term, and which are the challenges that might be found along the way.A A F, acknowledges the European Union's Horizon 2020 research and innovation programme for the funding support through the European Research Council (Grant Agreement 772873, 'ARTISTIC' project). A A F and O A acknowledge funding from the European Union's Horizon 2020 research and innovation program under Grant Agreement No. 957189 (BIG-MAP). BATTERY 2030+ initiative under Grant Agreement No. 957213 is also acknowledged. A A F acknowledges Institut Universitaire de France for the support. A A F and O A acknowledge the funding from Conseil régional des Hauts de France and the Université Picardie Jules Verne under project name (OPERANDO).With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000917-S).Peer reviewe

Heterogeneous Solid-Electrolyte Interphase in Graphite Electrodes Assessed by 4D-Resolved Computational Simulations

International audienceGraphite is one of the most used active materials in lithium-ion battery negative electrodes thanks to its high specific capacity and low equilibrium potential. For over 40 years, one of the most discussed issues with this material revolves around the complex formation mechanism of the solid-electrolyte interphase (SEI), which acts as a protective layer against electrolyte decomposition but causes capacity losses. Due to the difficulties to experimentally observe the SEI (air sensibility, low contrast and nanometric size), its impact on the performance of graphite-based porous electrodes has never been spatially assessed in regards of the three-dimensional features of the electrodes. We report here a new 4D (3D+time) resolved computational model which gives insights about the SEI heterogeneity within such porous electrodes. The model is applied to different graphite morphologies and is able to assess the electrode mesostructure impact on the SEI formation and the impact of the latter on the electrodes' electrochemical performance. This work paves the way towards a powerful tool to assist in the interpretation of SEI characterization experiments

Towards a 3D-resolved model of Si/Graphite composite electrodes from manufacturing simulations

International audienceComposite graphite/silicon (Si) electrodes with low Si weight percentages are considered as a promising anode for next generation Li-ion batteries. In this context, understanding the mesostructural changes due to Si volume expansion and the complex electrochemical interplay between graphite and Si becomes crucial to unlock real-life applications of such composite electrodes. This work presents, for the first time, a three-dimensional (3D) physics-based model for graphite/Si composite electrodes, coupling electrochemistry and mechanics, using as input electrode mesostructures obtained from manufacturing-related Coarse-Grained Molecular Dynamics models. The slurry and dried electrode mesostructure are first generated by considering graphite and additives only, while the Si is included in an additional step. The model herein presented is a step further into obtaining a fundamental understanding of the complex processes happening in graphite/Si composite electrodes, paving the way towards their optimization

Towards a 3D-resolved model of Si/Graphite composite electrodes from manufacturing simulations

Composite graphite/silicon (Si) electrodes with low Si weight percentages are considered as a promising anode for next generation Li-ion batteries. In this context, understanding the microstructural changes due to Si volume expansion and the complex electrochemical interplay between graphite and Si becomes crucial to unlock real-life applications of such composite electrodes. This work presents a three-dimensional (3D) physics-based model coupling electrochemistry and mechanics, using as input electrode microstructures obtained from manufacturing-related Coarse-Grained Molecular Dynamics models. The slurry and dried electrode microstructure are first generated by considering graphite and additives only, while the Si is included in an additional step. The model herein presented is a step further into obtaining a fundamental understanding of the complex processes happening in graphite/Si composite electrodes, paving the way towards their optimization

Lithium ion battery electrode manufacturing model accounting for 3D realistic shapes of active material particles

The demand for lithium ion batteries (LIBs) in the market has gradually risen, with production increasing and expected to be boosted through the massive emergence of gigafactories. To meet industrial needs, the development of digital twins designed to accelerate the optimization of LIB manufacturing processes is essential. We report here a new three-dimensional physics-based modeling workflow able to predict the influence of manufacturing parameters on the electrode microstructure. This novel modeling workflow accounts for real active material particle shapes obtained from X-ray micro-computed tomography, upgrading our previous models where the particles were considered to be spherical. The modeling workflow is supported on Coarse-Grained Molecular Dynamics simulating the slurry, its drying and the calendering of the electrode resulting from the drying simulation. This model enables to link the manufacturing parameters with the real microstructure of the electrodes and to better observe the effect of the former on the heterogeneity of the electrodes. By using as user case electrodes containing LiNi$_{0.33}$Co$_{0.33}$Mn$_{0.33}$O$_2$ as active material, the simulations allow us, among others, to observe the alteration of the electrode heterogeneity during the manufacturing process and the deformation of the secondary particles of active material

Machine learning 3D-resolved prediction of electrolyte infiltration in battery porous electrodes

International audienceElectrolyte infiltration is one of the critical steps of the manufacturing process of lithium ion batteries (LIB). We present here an innovative machine learning (ML) model, based on the multi-layers perceptron (MLP) approach, to fast and accurately predict electrolyte flow in three dimensions, as well as wetting degree and time for LIB electrodes. The ML model is trained on a database generated using a 3D-resolved physical model based on the Lattice Boltzmann Method (LBM) and a NMC electrode mesostructure obtained by X-ray micro-computer tomography. The trained ML model is able to predict the electrode filling process, with ultralow computational cost and with high accuracy. Also, systematic sensitivity analysis was carried out to unravel the spatial relationship between electrode mesostructure parameters and predicted infiltration process characteristics. This paves the way towards massive computational screening of electrode mesostructures/electrolyte pairs to unravel their impact on the cell wetting and optimize the infiltration conditions

Machine learning 3D-resolved prediction of electrolyte infiltration in battery porous electrodes

Electrolyte infiltration is one of the critical steps of the manufacturing process of lithium ion batteries (LIB). We present here an innovative machine learning (ML) model, based on the multi-layers perceptron (MLP) approach, to fast and accurately predict electrolyte flow in three dimensions, as well as wetting degree and time for LIB electrodes. The ML model is trained on a database generated using a 3D-resolved physical model based on the Lattice Boltzmann Method (LBM) and a NMC electrode mesostructure obtained by X-ray micro-computer tomography. The trained ML model is able to predict the electrode filling process, with ultralow computational cost and with high accuracy. Also, systematic sensitivity analysis was carried out to unravel the spatial relationship between electrode mesostructure parameters and predicted infiltration process characteristics. This paves the way towards massive computational screening of electrode mesostructures/electrolyte pairs to unravel their impact on the cell wetting and optimize the infiltration conditions