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Rotor-bearing systems take an important place in engineering applications and are used in many industrial systems: gas turbines, compressors, jet engines, machine-tool spindles, etc. The spindle is an essential component of manufacturing systems involving metal removal processes. Its performance largely influences the quality of machined parts. This importance has increased with the advent of High Speed Machining, High Productivity Machining and Hard Machining with low lubrication. In machining, manufacturers aim at higher cutting speeds and feed rates in order to increase productivity. These trends imply higher rotational speeds for spindles but also higher cutting forces. At the same time, manufacturers wish to improve surface roughness and tolerances of the machined parts. To meet those goals, it is essential to master the dynamic behavior of spindles. Stiffness is a fundamental parameter controlling the static and dynamic performances of the spindle. Modeling and controlling the dynamic behavior of spindles is a complex problem, because of the non-linear nature of the bearing stiffness, of its speed dependence and because of thermo mechanical effects associated with the heat dissipation in the bearings. In this dissertation, a mixed experimental-numerical method is presented for evaluating bearing stiffness of very high speed rotor-bearing systems. This method focuses on determining the stiffness properties of angular contact ball bearings used in the design of high-speed spindles for machine-tool applications. The goal of this method is to provide accurate and reliable stiffness data to improve dynamic predictive models of spindles. These models will then serve to improve and facilitate the design of high-speed spindles. A special attention is paid to the speed dependence of the bearing stiffness. The mixed identification method is based on the comparison of an experimental modal model with a numerical modal model of spindles. An optimization procedure based on a non-linear least square fit algorithm is used to estimate the bearing stiffness. The optimization criterion combines error functions based on natural frequencies and mode shapes. Based on the measurement of frequency response functions, the experimental modal parameters (natural frequencies and mode shapes) are extracted. In order to match numerical modal parameters with the experimental ones, the iterative optimization procedure updates the numerical parametric model. The model parameters are the bearing stiffness parameters to estimate. The procedure terminates once the error between the experimental and numerical parameter falls below a predefined threshold value. At this point, the bearing stiffness estimation is completed. The numerical model was developed to be easily implemented in a commercial finite element software. Moreover, the 3D finite element model allows to take into account all the surrounding structural elements which can influence the dynamic behavior of the spindle. The ball bearing is modeled as a stiffness matrix including radial, axial, tilting and coupling terms. On the other hand, a test rig using contact-free capacitive sensors to measure frequency response functions of motor-spindles was developed. Measurements can be performed either under non-rotating conditions or under rotation. Experimental modal parameters are extracted using a traditional curve fitting method. Several numerical applications were used to assess the performances of the identification method and validate it. As an example, the proposed identification procedure is applied to a mid-size (7kW) motor-spindle running at up to 70'000 rpm. Two test cases are presented: test spindle equipped with angular contact ball bearings with a contact angle of 15° and then 25°, commonly used in industrial high-speed spindles. For these two configurations, the goal is to estimate bearing stiffness and validate the method with experimental data. Several identifications were conducted at various rotational speeds. The rotational speed range was chosen to highlight the decrease in the bearing stiffness with rotational speed. In both test cases, we observed significant decrease in axial bearing stiffness with speed. With a contact angle of 15°, at 66'000 rpm, the axial stiffness can drop to 40% of its value at zero speed. With a contact angle of 25°, at 54'000 rpm, the axial stiffness can drop to 30% of its value at zero speed. The behavior of the radial stiffness is different in the two test cases. With a contact angle of 15°, the radial stiffness remains almost constant over the whole range of rotational speed. Conversely, with a contact angle of 25°, the behavior of the radial stiffness is similar to the behavior of the axial stiffness. At 54'000 rpm, its value can decrease to less than 40% of its value at zero speed. The obtained results are in good agreement with the literature and with numerically predicted stiffness values.

This study proposes a new advanced algorithm for determining material parameters based on in situ tests. In situ testing gives an opportunity to perform soil characterization in natural stress conditions on a representative soil mass. Most field techniques reduce soil disturbances to minimum, allowing investigating the response of virgin soil. Self-boring pressuremeter tests (SBPT) and standard piezocone tests (CPTU) are widely used to deduce properties of clayey soils through analytical and empirical correlations between soil properties and experimental measurements. Empirical correlations usually require some tuning based on reference laboratory data because first-order estimates for typical correlation coefficients may give unreliable evaluation of soil properties. Analytical correlations are mostly based on cavity expansion methods which are restricted to either fully drained or perfectly undrained problems, so that inverse closed-form solutions for relatively simple constitutive models can be derived. In practice, however, depending on physical and consolidation properties of the soil, partially drained conditions may occur during field testing, leading to an erroneous estimation of clay characteristics. Therefore, elaborating a generic parameter identification framework, which is based on the artificial neural network (NN) technique and which may improve the reliability of soil properties derived from in situ testing, is the main goal of this research. This study explores the possibility of using NNs to solve complex inverse problems including partially drained conditions. In other words, NNs are used to map experimental measurements onto set of soil properties. The development of NN-based inverse models is based on a training data sets which consists of pseudo-experimental measurements derived from numerical simulations of both the SBPT and the CPTU test in normally- and lightly overconsolidated clay type material. The study presents a generic two-level procedure designed for the calibration of constitutive models of soils. It is demonstrated that NN inverse models can be easily integrated into the classical back-analysis. At the first level, the NN approach is applied to achieve the first approximation of parameters. This technique is used to avoid potential pitfalls related to the conventional gradient-based optimization (GBO) technique, considered here as a corrector that improves predicted parameters. Trained NNs as parallel operating systems can provide output variables instantly and without a costly GBO iterative scheme. The proposed framework is verified for the elasto-plastic Modified Cam Clay (MCC) model that can be calibrated based on standard triaxial laboratory tests, i.e. the isotropic consolidation test and the consolidated isotropic drained compression test. The study presents formulations of the input data for the NN predictors, enhanced by a dimensional reduction of experimental data using principal component analysis (PCA). The determination of model characteristics is demonstrated, first on numerical pseudo-experiments and then on the experimental data. Furthermore, the efficiency of the proposed approach in terms of accuracy and computational effort is also discussed. The verified two-level strategy is applied to a numerical procedure of parameter identification for the boundary value problem (BVP) of the SBPT. The coupled hydro-mechanical finite element (FE) formulation allows the generated excess pore water pressure to be dissipated during simulations of the expansion test, followed then by a holding test. Numerical simulations demonstrate that volume changes that may occur in clay during the expansion test due to partial drainage, can cause local soil hardening near the cavity wall and affect parameter interpretation for pressuremeter tests. Therefore, the NN technique is applied to obtain an initial guess for model parameters, taking into account the possible partially drained conditions during the expansion test. Parameter identification based on measurements obtained through the pressuremeter expansion test and two types of holding tests is illustrated on the MCC model. NNs are trained using a set of synthetic test samples, which are generated by means of FE simulations based on constrained random permutations of input model parameters. The measurements obtained through expansion and consolidation tests are normalized by the proposed normalizing formulas so that NN predictors operate independently of the testing depth. Examples of parameter determination are demonstrated on both numerical data and field measurements from the Fucino clay deposit. The efficiency of the combined parameter identification in terms of accuracy, effectiveness and computational effort is also discussed. Finally, an application of NN predictors as a stand-alone support for soil profiling is presented for the piezocone test. By similarity to the SBPT problem, a number of NN inverse models are developed based on the results derived from rigorous FE analyzes. The FE model of piezocone penetration involves numerical formulation for the two-phase material obeying the MCC law and including the large strain theory, as well as the large deformation formulation for contact interface. It is demonstrated that a considerable computational effort related to the generation of the training database can be reduced by optimizing the mesh size and "steady-state" depth in function of soil rigidity index. Due to a severe loss of measurement accuracy observed in the finite elements adhered to the "rough" interface, an equivalent semi-numerical approach is proposed to account for frictional effects in different drainage conditions which are delineated from a number of numerical simulations. The validity of the developed penetration model is verified in detail by means of comparisons with other theoretical solutions and parametric studies synthesized from literature, as well as experimental evidence for both undrained and partially drained scenarios. An extended parametric study including influence analyzes of strength and stress anisotropy, rigidity index and cone roughness on two cone factors provides new insight into the analysis of cone penetration. The shortcomings of the FE model due to the limitations of the applied constitutive model are also discussed. As regards NN models, different configurations of input variables, including standard normalized piezocone metrics and other available soil characteristics are investigated in terms of feasibility of effective NN training. The post-training regression analyzes are performed for numerical data allowing the assessment of the influence of specific input variables on accuracy of parameters predictions. Finally, the developed NN models are applied to predict parameters based on field measurements for a number of characterization sites. Provided examples demonstrate that NN inverse models may constitute an effective complementary support during the first-order quantification of the MCC parameters from piezocone measurements.