IPANEMA

 

PEM Develops New Test Methods for The Hairpin Stator Production

The chair "Production Engineering of E-Mobility Components" (PEM) of RWTH Aachen University is conducting research in the "IPANEMA" project on new testing methods for the production of so-called hairpin stators. In order to make the complex production of these components, which are crucial for the performance of electric motors, more efficient, machine-learning concepts are also to be used in the project that is part of the federal Central Innovation Program for SMEs (ZIM).

Advantage: Promising opportunities

To meet the growing demand in the electric vehicle market, innovative electric motor topologies have been developed in recent years. Hairpin technology, whose name derives from the hairpin-shaped geometry of the copper conductors, has become particularly popular. The technology offers high potential on both the product and process sides. In contrast to established processes in which a continuous round wire is inserted into the stator core, in hairpin stator technology individual plug-in coil elements made of solid copper flat wire are inserted into the core before making contact with each other. This enables high copper fill factors and thus offers promising possibilities in terms of performance and efficiency while being well suited for automotive mass production.

Disadvantage: Current test methods mostly inappropriate

At present, the level of experience with the technologies required to produce hairpin windings is still considered low. In addition, there are a number of technical challenges to date. Above all, the time and financial effort that must go into improving overall system effectiveness is enormous. At present, it can only be ensured by using suitable measurement and testing methods in combination with new applications from the field of "smart data management". However, current methods – such as optical test methods – are only suitable for individual quality indicators in the hairpin stator process chain and are also extremely cost-intensive. Moreover, since optical inspection methods can only detect what is visible, non-visible insulation defects, for example, remain undetected. End-of-line (EoL) inspection techniques are only applied at the end of the production chain, which means that the cost of defects is enormously high due to the added value already introduced.

Solution: Early detection of defects in the process chain

The aim of the "IPANEMA" research project is therefore to identify quality- and safety-relevant defects in the hairpin-stator process chain at an early stage in order to keep the resulting costs as low as possible. In addition to new testing technologies, certain data management applications from the "Industry 4.0" sector also offer high potential for sustainably increasing overall plant effectiveness.

A German report on this topic can be found in the "Automobil Industrie" news portal.

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The project

  • "IPANEMA": Innovative test methods combined with a machine-learning concept for hairpin stator production

Research objectives

  • Development of a marketable and production-ready test method with an integrated machine-learning concept
  • Enabling higher process stability and better product quality with simultaneous cost and time benefits

Research and project partners

PEM of RWTH Aachen University
API Hard- und Software GmbH (German)

Duration

  • 03/01/2020 through 03/31/2022
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Project sponsor

AiF (ZIM cooperation project)

Funding code

  • ZF4676803PO9

Grantor

Federal Ministry for Economic Affairs and Climate Action (BMWK)