Model-based force estimation is an emerging methodology in the mechatronic community given the possibility to exploit physically inspired high-fidelity models in tandem with ready-to-use cheap sensors. In this work, an inverse input load identification methodology is presented combining high-fidelity multibody models with a Kalman filter-based estimator and providing the means for an accurate and computationally efficient state-input estimation strategy. A particular challenge addressed in this work is the handling of the redundant state-description encountered in common multibody model descriptions. A novel linearization framework is proposed on the time-discretized equations in order to extract the required system model matrices for the Kalman filter. The presented framework is experimentally validated on a slider-crank mechanism. The nonlinear kinematics and dynamics are well represented through a rigid multibody model with lumped flexibilities to account for localized interaction phenomena among bodies. The proposed methodology is validated estimating the input torque delivered by a driver electro-motor together with the system states and comparing the experimental data with the estimated quantities. The results show the stability and accuracy of the estimation framework by only employing the angular motor velocity, measured by the motor encoder sensor and available in most of the commercial electro-motors.
Although gears have been used in mechanical industry for a long time and first systematic research activities, aimed at understanding gear meshing, started in the '50s, still nowadays a lack of accurate description for several physical phenomena represents a limitation for transmission design. One of the main causes of such limitations is the threedimensional (3D), local and non-linear nature of contact problems. Given the complexity of these problems, a variety of modelling techniques with different levels of fidelity and required computational effort have been set up. Among the available methodologies, analytical modelling, which has been developed for a vast variety of applications from the early days of gear dynamics research, still attracts the interest of researchers thanks to the low computational requirements and to the capability to efficiently describe specific phenomena. Among analytical models, one-dimensional (1D) description aims at studying the torsional gear vibrations around the rotational axes and can be used to simulate either gear whine or gear rattle phenomena. The aim of this paper is to illustrate a numerical approach, based on 3D (Finite Element) FE simulations, to estimate the mass and inertia properties of a 1D gear pair model. The proposed approach is based on pre-strained modal analyses, carried out on the 3D FE model of a pair of meshing gears, from which the variable meshing stiffness and modal mass values in a given system configuration are derived and used to calibrate the equivalent 1D model. An application example of the proposed method is provided by analyzing a pair of identical spur gears, for which the 1D model is created and used to estimate the dynamic TE at different rotation speeds and under constant external load in steady-state conditions.
Due to ever increasing performance requirements, model-based optimization and control strategies are increasingly being adopted by machine builders and automotive companies. However, this demands an increase in modelling effort and a growing knowledge of optimization techniques, as a sufficient level of detail is required in order to evaluate certain performance characteristics. Modelling tools such as MATLAB Simscape have been created to reduce this modelling effort, allowing for greater model complexity and fidelity. Unfortunately, this tool cannot be used with high-performance gradient-based optimization algorithms due to obfuscation of the underlying model equations. In this work, an optimization toolchain is presented that efficiently interfaces with MATLAB Simscape to reduce user effort and the necessary skill and computation time required for the optimization of high-fidelity drivetrain models. The toolchain is illustrated on an industrially relevant conjugate cam-follower system, which is modelled in the Simscape environment and validated with respect to a higher-fidelity modeling technique, namely, the finite element method (FEM).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.