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.

To comply with the increasing complexity of new mechatronic systems and stricter safety regulations, advanced estimation algorithms are currently undergoing a transformation towards higher model complexity. However, more complex models often face issues regarding the observability and computational effort needed. Moreover, sensor selection is often still conducted pragmatically based on experience and convenience, whereas a more cost-effective approach would be to evaluate the sensor performance based on its effective estimation performance. In this work, a novel estimation and sensor selection approach is presented that is able to stabilise the estimator Riccati equation for unobservable and non-linear system models. This is possible when estimators only target some specific quantities of interest that do not necessarily depend on all system states. An Extended Kalman Filter-based estimation framework is proposed where the Riccati equation is projected onto an observable subspace based on a Singular Value Decomposition (SVD) of the Kalman observability matrix. Furthermore, a sensor selection methodology is proposed, which ranks the possible sensors according to their estimation performance, as evaluated by the error covariance of the quantities of interest. This allows evaluating the performance of a sensor set without the need for costly test campaigns. Finally, the proposed methods are evaluated on a numerical example, as well as an automotive experimental validation case.

In the past decades, an increasing trend towards the structural combination of lightweight materials and traditional metals has been observed. For these multi-material interfaces, traditional joining technologies such as bolting and welding have to be re-thought and adhesive bonds can be a valid alternative. The substitution of the joints has an impact on the stiffness, damping, and mass distribution of the entire assembly and should be taken into account while designing for NVH, durability, and performance. The aim of this work is to experimentally compare the effect of bolts and adhesive bonding on the vibrational response of the assembly, in terms of modal frequency, shape, and damping. Plate-to-plate single lap joints are tested under transverse vibration. In particular, we compare one M4 bolted connection and three adhesive connections, respectively, realized with stiff epoxy, flexible foam tape, and toughened acrylic. Results show that, in a low-mid frequency region between 0 and 250 Hz, the substitution of bolts with adhesives has a limited influence on mode shapes but can lead up to a 6-fold increase of the modal damping values and 10% increase of the modal frequencies.

Torsional vibrations play a critical role in the design and operation of a mechanical or mechatronic drivetrain due to their impact on lifetime, performance, and cost. A magnetic spring allows one to reduce these vibrations and improve the actuator performance yet introduces additional challenges on the identification. As a direct torque measurement is generally not favourable because of its intrusive nature, this paper proposes a nonintrusive approach to identify torsional load profiles. The approach combines a physics-based lumped parameter model of the torsional dynamics of the drivetrain with measurements coming from a motor encoder and two MEMS accelerometers in a combined state/input estimation, using an augmented extended Kalman filter (A-EKF). In order to allow a generic magnetic spring torque estimation, a random walk input model is used, where additionally the angle-dependent behaviour is exploited by constructing an angle-dependent estimate and variance map. Experimental validation leads to a significant reduction in bias in the load torque estimation for this approach, compared to conventional estimators. Moreover, this newly proposed approach significantly reduces the variance on the estimated states by exploiting the angle dependency. The proposed approach provides knowledge of the torsional vibrations in a nonintrusive way, without the need for an extensive magnetic spring torque identification. Further, the approach is applicable on any drivetrain with angle-dependent input torques.

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).