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Optimization of total cost of ownership (TCO) is an important, and challenging design target for present day manufacturing machines. This paper is concerned specifically with production machines with fast reciprocating loads (> 1 Hz), e.g. weaving looms and plate punching machines. Subsequent acceleration and deceleration give rise to a reciprocating energy flow that can be handled either mechanically or electrically. The chosen solution will affect the total cost of ownership. In addition to the cost of the energy storage device itself, there are the energy bill, the size and cost of the electric drive and power supply to consider. Moreover, there are certain constraints to be met: lifetime, DC-bus voltage limits and total power factor. This paper presents a methodology that takes all these aspects into account. It applies it to a bar linkage mechanism, which is representative for the targeted applications. In the mechanical domain, springs are considered for energy storage. The structural design of the spring is included in the analysis in order to account for lifetime and inertia added by the spring. In the electric domain, three different topologies are compared: a purely passive front end, where energy is stored directly on the DC-bus, a passive front end combined with a DC/DC converter and a separate storage capacitor, and an active front end.

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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.