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Estimation of grinding contact stiffness and damping parameters from dynamic output only using Hunt-Crossley force model and Unscented Kalman filter
No full text. This paper introduces a novel approach that combines the Unscented Kalman Filter with the Hunt-Crossley force model to accurately estimate the stiffness and damping characteristics at the contact point of a grinding process conducted by a flexible manipulator. The Hunt-Crossley force model is proposed for the force contact considering the flexibility of the manipulator structure and is written in a state form as functions of the contact stiffness and damping. Leveraging the Unscented Transform to linearize the nonlinear measurement functions, the Unscented Kalman Filter effectively estimates and updates the stiffness and damping parameters based on the state model. This method is put into practice in a real grinding scenario employing a flexible manipulator. Its practicality and convenience make it a promising technique for estimating operational machining parameters and developing an efficient vibration control strategy for machining applications
Study of wind-induced vibrations on a trellis pylon controlled through an active mass damper system
No full textThe rapid expansion of telecommunications infrastructure, driven by the deployment of the 5G network, necessitates innovative engineering solutions to ensure the reliability and stability of these critical structures. Steel trellis pylons, designed for hosting several telecommunication antennas, are particularly susceptible to wind-induced vibrations due to their slender profiles, high equivalent area exposed to wind loads and low structural damping. Such vibrations can lead to structural deterioration, signal disturbance, and, in severe cases, total structural failure. In this context, the need for effective vibration control measures is becoming more and more relevant. This paper underscores the complex challenge of windinduced vibrations in telecommunications pylons and the promising potential of AMD systems as a mitigation strategy. This study aims at advancing the state of the art by integrating experimental wind load measurements, modal analysis, and the application of AMD technology to a 50-meter-high steel trellis pylon. Through comprehensive analysis and numerical simulation, the effectiveness of AMD systems in enhancing structural performance and resilience under wind loading conditions is validated
Deep convolutional architectures for uncertainty quantification and forecast in inundation problems
No full textThis article provides a summary of our latest research, where we investigate the application of data-driven deep learning methods to simulate the dynamics of physical systems that are governed by partial differential equations (PDEs). The main challenge is the long-term temporal extrapolation for fluid dynamics problems that exhibit steep gradients and discontinuities. We make use of deep learning techniques, specifically designed for time-series predictions like LSTM, TCN, and Attention mechanism, as well as CNN. These methods are employed to model the dynamics of systems primarily influenced by advection. We propose a combination of a Convolutional Autoencoder (CAE) model for data compression and a novel CNN-based for forecasts. These models take a series of high-fidelity vector solutions and predict the solutions for the following time steps using auto-regression. To reduce complexity and computational demands during both online and offline stages, we implement deep auto-encoder networks. These techniques are used to compress the high-fidelity snapshots before feeding them into the forecasting models. Our models are evaluated on numerical benchmarks, such as the 1D Burgers’ equation and Stoker’s dam-break problem, to assess their long-term predictive accuracy, even in scenarios that extrapolate beyond the training domain. The model that demonstrates the highest accuracy is subsequently used to simulate a hypothetical dam break in a river with real 2D bathymetry. Due to space constraints, only a selection of results is showcased, with additional findings available in our work [1] and the newer ones will also be presented in the talk. Our findings indicate that the proposed CNN future-step predictor offers significantly accurate forecasts in the considered spatiotemporal problems
Fuzzy statistics-aided inference in experimental design
Get PDFConducting research based on active influence on the examined object or process requires distinguishing an explained quantity, measured quantitatively, the possible changes of which will be considered as influencing it through a group of quantities considered as explanatory quantities. This approach implicitly postulates the existence of a cause-and-effect relationship between the explanatory quantities and the explained quantity. In practice, especially industrial practice, explanatory quantities are often called controlled factors. Knowledge of possible cause-and-effect relationships can be graded, from the most comfortable situation of the existence of appropriate binding equations and their exact solutions, through the existence of binding equations but without knowing the exact solutions, to the absence of such equations. While in the first case, experimental research serves to refine the results originally calculated for idealized models, in the second case, it is a necessary stage of identifying the parameters of the postulated model, and in the third case, it is a necessary stage of collecting data for which the simplest possible forecasting model will be constructe
Structural optimization through generative adversarial networks
Get PDFThe finite element method (FEM) is a well known approach to solve partial differential equations. It has important applications in structural engineering, such as in topology optimization (TO). TO involves, at each iteration, the solution of structural problems via FEM, which can add up to a high computational cost. Therefore, a line of research to accelerate TO emerged over the years focusing on machine learning (ML) approaches. Particularly, Artificial Neural Networks (ANNs) have been proposed to significantly speed-up the process by eliminating the iterative algorithm, which is intrinsic to TO. Since ANN is a supervised ML method, first a dataset is generated, containing finite element analysis (FEA) inputs, volume fraction, postprocessing, and final topologies. Then, with the Wasserstein Generative Adversarial Networks (WGANs) is trained on this dataset to map fields of physical quantities, such as the von Mises stress, to the final optimized structure. The final designs obtained via ML are quantitatively analyzed according to the metrics
Acute or a Cute Robot? The Effect of Angularity on Medical Service Robot Perception
Get PDFDespite the importance of medical care, one in three Americans avoid doctor visits (Cleveland Clinic Medical Professional, 2021). Considering our modernizing world, applying robotics to healthcare, particularly with Medical Service Robots (MSRs), yields the potential to improve patient engagement and overall care (“Medical robots,” n.d.). Recognizing the impact of design on user perceptions, the purpose of this study was to manipulate the angularity and curvature of MSR designs to reduce perceived threat and increase warmth and trust among patients. Participants (N=230) were randomly assigned to one of three conditions: curved MSR design, angular design, or a no-image control group. After viewing the stimulus, participants answered Likert-type scales that measured their perception of them. ANOVA tests revealed that perceived trust was unaffected by angularity, while perceived warmth approached significance (p = .06). Significant effects were found for preference (p = .02) and perceived threat (p = .04), with the curved MSR design being most preferred and the angular version perceived as most threatening. Additionally, gender was found to have a significant main effect for preference (p < .01), warmth (p = .04), and trust (p = .04) as males rated the robots higher than females. These findings can be used to create MSR designs that are most positively perceived, thus optimizing their ability to improve patient care.
 
Application of machine learning for optimizing the heating process in in-situ consolidation of thermoplastic matrix composite materials
No full textThis study explores the development and application of data-driven control techniques for managing the power of a laser system used in the in-situ consolidation process of thermoplastic materials (ISC). We discuss the correlation among the main variables - temperature, power, layer number, and lamination speed - and how these interactions inform the design of our control models. Two types of prediction models, multiple polynomial regression and support vector machines are compared. Though the software solution developed here is for testing purposes and not for production, we demonstrate the utility and flexibility of machine learning control approaches for this type of manufacturing process
Hydromechanical embedded finite element for conductive and impermeable strong discontinuities in porous media
No full textThe pore pressure inside oil and gas reservoirs compartmentalized by sealing faults increases during injection processes. The rise in the pore pressure can induce fault reactivation, leading to hydraulic issues such as fluid leakage from the reservoir to other layers and seismicity. Therefore, it is essential to accurately model the mechanisms involved in this problem, primarily related to the presence of a strong discontinuity, a fault, inside the domain. Several numerical approaches can be used to represent the presence of discontinuities. The embedded finite element method (EFEM) has recently gained attention because it does not require the mesh to conform with the discontinuities, thus circumventing the typical mesh generation challenges of modeling faulted domains. The current EFEM formulations cannot properly model a hydromechanical problem such as fault reactivation, due to simplifications in their derivation. Hence, this work proposes a new fully coupled hydromechanical EFEM formulation based on the Strong Discontinuity Approach that can represent discontinuities acting as preferential flow paths or barriers for the fluid flow. The formulation is applied to a fault reactivation problem, showing the main reactivation mechanisms. This paper also discusses the presence of spurious oscillations along the discontinuities and their relations with the mesh discretization
Simultaneous, Dynamical Analysis of Structural Ropes and Membranes on all Level-sets
Get PDFThelevel sets of scalar functions may imply the geometries of individual ropes and membranes. All level sets within an interval, considered in some bulk domain, define infinitely many geometries at once. A mechanical model is proposed which enables the simultaneous, dynamic analysis of all such geometries. For the solution of the governing equations, a tailored numerical method coined Bulk Trace FEM is employed for the spatial discretization, using higher-order background meshes in the bulk domains. The HHT-α method is used for the temporal discretization. Numerical results are presented that demonstrate the potential of the proposed mechanical model and numerical metho
Finite Element Simulation of a Rainfall Induced Shallow Landslide in an Experimental Hillslope with a Multiphase Porous Media Model
Get PDFThis study presents the results of a FEM numerical simulation of a large scale physical model of a slope subjected to rainfall infiltration. The slope failure is modelled as a coupled variably saturated thermo-hydro-mechanical problem, using the Pastor-Zienkiewicz generalised plasticity model to obtain the soil’s mechanical response. Small strain and quasi static loading conditions are assumed, and plane strain conditions are adopted in the slope stability analysis. The hydraulic and mechanical parameters are calibrated based on the available experimental data. The numerical results are compared with the experimental data of the mechanical and the hydraulic responses up to failure