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.

Lightweight gears have the potential to substantially contribute to the green economy demands. However, gear lightweighting is a challenging problem where various factors, such as the definition of the optimization problem and the parameterization of the design space, must be handled to achieve design targets and meet performance criteria. Recent advances in FE-based contact analysis have demonstrated that using hybrid FE–analytical gear contact models can offer a good compromise between computational costs and predictive accuracy. This paper exploits these enabling methodologies in a fully automated process, efficiently and reliably achieving an optimal lightweight gear design. The proposed methodology is demonstrated by prototyping a software architecture that combines commercial solutions and ad hoc procedures. The feasibility and validity of the proposed methodology are assessed, considering the multiobjective optimization of a transmission consisting of a pair of helical gears.

Gears remain a fundamental component in mechanical power transmission, with ongoing research focused on enhancing performance and sustainability. This study addresses the process of gear lightweighting, a key factor for efficiency improvements in automotive and aerospace sectors. Traditionally, material removal from gear bodies results in weight reduction, but at the cost of increased noise and vibration. A novel approach using hybrid gears, which combine a metal rim and hub with a composite material web, offers a promising solution. This research proposes a comparative environmental analysis among a conventional full steel, a lightweight and a hybrid gear using a life cycle energy quantification. The study considers two End-of-Life (EoL) scenarios: a conventional open loop scenario with partial recycling and a closed loop scenario with comprehensive recycling, including a thermal recycling for carbon fiber-reinforced plastics. The Cumulative Energy Demand (CED) has been conducted by applying a cradle-to-grave approach. The CED has been evaluated for each gear configuration quantifying the impact of each unit process involved in the production of the gear, from raw material extraction to product manufacturing and from use phase to different EoL scenarios. The cumulative results, performed preserving the same mechanical performance, indicate that the CED of the hybrid gear in the conventional open loop scenario is comparable to the one of the full gears, with an increase of 12.58%. In contrast, in the closed loop scenario, the hybrid gear exhibits substantial energy recovery benefits, with an overall CED difference of 7.50% compared to the lightweight gear and of 28.82% compared to the full gear. These results underline the potential of hybrid gears to improve efficiency, being able to achieve a 20% weight reduction with respect to the full gears, and to reduce environmental impact if effective recycling strategies were implemented.

As stroke is becoming more frequent nowadays, cutting edge rehabilitation approaches are required to recover upper limb functionalities and to support patients during daily activities. Recently, focus has moved to robotic rehabilitation; however, therapeutic devices are still highly expensive, making rehabilitation not easily affordable. Moreover, devices are not easily accepted by patients, who can refuse to use them due to not feeling comfortable. The presented work proposes the exploitation of a virtual prototype of the human–robot ecosystem for the study and analysis of patient–robot interactions, enabling their simulation-based investigation in multiple scenarios. For the accomplishment of this task, the Dynamics of Multi-physical Systems platform, previously presented by the authors, is further developed to enable the integration of biomechanical models of the human body with mechatronics models of robotic devices for motion assistance, as well as with PID-based control strategies. The work begins with (1) a description of the background; hence, the current state of the art and purpose of the study; (2) the platform is then presented and the system is formalized, first from a general side and then (3) in the application-specific scenario. (4) The use case is described, presenting a controlled gym weightlifting exercise supported by an exoskeleton and the results are analyzed in a final paragraph (5).

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).