Electrical machines have recently received a lot of attention due to a variety of applications in several industries. Although advances in digital technologies have enabled more efficient production of electrical machines, faults are still identified at the end of the line tests. In order to avoid accumulation of defects during the production chain, it is desirable to identify faults early in the process. This can be achieved by identifying how critical process parameters and the interdependencies between them influence the occurrence of faults. This poses a challenge in electrical machine manufacturing because of the complexity involved in various manufacturing steps involving deformable material, an example is coil winding.

This paper proposes a computational framework to model interdependencies in a complex electrical machine manufacturing process involving deformable material. A Discrete Event Simulation model representing the coil winding process demonstrated that input parameters such as wire tension and winding speed influence physical and electrical properties of the coil (enamelled copper wire) leading to generation of defects in the final product.