No abstract

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.

In this work, we present a cloud‐based digital twin for monitoring of a clamping technology for machining of composite parts. Supporting large and/or freeform composite parts is crucial to avoid bending during drilling. Bending of the part will lead to delamination and frayed edges of the drilled holes. The new active clamping technology allows to realise a stabilised fixture, localised in the area where the drilling occurs, to avoid bending. This significantly improves the quality of the drilled holes. The clamping device is equipped with an IoT edge device, with a bidirectional communication to the cloud. The cloud‐based digital twin analyses the quality of the drilled holes based on computer vision, monitors the drill wear and detects incorrect operation of the active clamping device. All data is stored in the cloud. By means of a knowledge graph, which acquires and integrates information into an ontology and provides a central information access, it will be easier for a data scientist to query this data and to gain new insights in the operation of the drill with active clamping device. The full deployment occurs on the Microsoft Azure cloud platform. This transforms the standard machine into an Industry 4.0 compliant machine.

Parallel elastic actuation is a highly promising concept for assisting preplanned trajectories such as repetitive tasks in industrial machines and robots. Nevertheless, due to the persisting challenges on spring lifetime, its full potential has yet to be leveraged in the industry. We propose a novel adaptive magnetic spring as a fatigue-free spring mechanism to enable variable stiffness actuators in long lifetime applications. The spring is designed to flexibly deal with variations in operating conditions, i.e., mass customization. We propose a co-design methodology which simultaneously optimizes the sizing of the magnetic spring (for the given machine and its operating conditions), together with the controls and ideal dynamic response of the system. This mechanism and the methodology are applied to a design problem of a weaving machine drivetrain, where the benefits of the adaptive magnetic spring are highlighted with respect to a fixed stiffness magnetic spring, and the current industrial benchmark without springs. Experimentally validated findings show a consistent and considerable improvement with respect to energy consumption and peak torque reduction of up to 47% and 64%, respectively, when comparing to the current industrial benchmark.