pisco_log
banner

Electric Motor Winding Automation (EMWA) for Hairpin Stator High-Volume Production

Andrea Marchetti

Abstract


This article takes the Electric Motor Winding Automation (EMWA) technology for mass production of hairpin stators as the research object. It investigates key processes including automatic forming of hairpin wires, stator slot insulation and automatic wire insertion,
end shaping, twisting and welding, as well as intelligent assembly line integration. A numerical simulation model is established based on
forming accuracy, wire insertion cycle time, welding defect rate and overall equipment effectiveness (OEE). Discrete event simulation and
finite element analysis are adopted to simulate and analyze process parameters and cycle characteristics of the EMWA production line at different operational stages. The results show that the optimized EMWA production line reduces the springback of hairpin wires to 0.21 mm, cuts
the wire insertion cycle down to 47 seconds per workpiece, keeps the welding defect rate below 1.2%, and raises OEE to over 88%. It realizes
high-stability automated mass production of drive motor windings for new energy vehicles.

Keywords


Card type stator; EMWA; New energy vehicle motors; Intelligent assembly line

Full Text:

PDF

Included Database


References


[1] Azizi, Diako, Gholami, et al. Improvement of Stator Insulation System for 24, 36 and 72 Slots HV Generators based on SNOPT

Method[J]. IEEE transactions on dielectrics and electrical insulation: A publication of the IEEE Dielectrics and Electrical Insulation Society, 2014, 21(2):594-602.

[2] Azizi D, Gholami A. Multiobjective optimization of stator slot insulation of high-voltage generator based on coupled SNOPT-fi nite element method analysis [J]. IEEE Electrical Insulation Magazine, 2013, 29 (2): 69-76.

[3] Zhong B, Zhou W, Wang Y, et al. Researching Technology and Equipment about the Vertical Billet in the Intelligent Manufacturing Production Line[J]. E3S Web of Conferences, 2021, 23(6):40.

[4] Liyu W, Jack H, Dan Y, et al. Automatic modeling and fault diagnosis of car production lines based on fi rst-principle qualitative mechanics and semantic web technology [J]. Advanced Engineering Informatics, 2021, 49.




DOI: http://dx.doi.org/10.70711/frim.v4i8.10016

Refbacks

  • There are currently no refbacks.