Manual operations feature prominently in the manufacture of many electrical machines. Even though high-volume electrical machine manufacture is dominated by automation, in the case of high-value, low-volume machines, several manufacturing operations continue to involve manual intervention because the complexity of such operations makes them heavily reliant on high dexterity manual skills and experience. For example, in a recent survey of EM manufacturing companies, 83% of EM manufacturing companies mentioned that the process of making terminations was manual with some automation . However, quality can be variable due to human involvement. Currently, in order to maintain a high precision of control and required tolerances, inspection is performed at various steps during manufacturing and assembly. Detecting a defect at these end-of-line tests can result in significant wasted time and costs due to rework or scrappage. The solution to this problem lies in process monitoring and inspection, particularly for error prone manual operations.
Working with the FEMM Hub
Digitisation of skill-intensive manufacturing processes offers process monitoring and inspection, particularly for error prone manual operations. The focus of this research is on Litz wires, which are special types of cables that are designed to reduce the skin effect and proximity effect losses in conductors. Litz wires consist of many thin wire strands, individually insulated and twisted together. A review of literature and discussions with industry revealed that up to 10% of Litz wires have failed connections due to incomplete stripping of some strands inside the connector. The Digital Manufacturing team from the FEMM Hub at The University of Sheffield are working with an aerospace manufacturer to develop techniques such as: infrared thermography for inspection of enamel removal on a Litz wire and monitoring techniques for the soldering process for making terminations on Litz wire.
How Sheffield’s Digital Manufacturing team is addressing the challenge
Infrared thermal imaging facilities at the Digital Manufacturing labs have been used to inspect bundles of Litz wires containing those with and without enamel. The temperature profiles of the wires with or without enamel are different during the cooling stage because of the difference in emissivity and thermal conductivities of copper and polyamide imide film. Machine learning techniques are being utilised for automated inspection of enamel removal on Litz wire bundles. Additionally, analytical techniques such as X-ray Computed Tomography (CT) are investigated on samples from industry.
A solution for industry
Working together and combining digitalisation expertise from the FEMM Hub and the industrial knowhow from the aerospace manufacturer, the team has successfully demonstrated the techniques on a low TRL level. There is potential to accelerate the impact of the research streams by integrating the methods and algorithms into a demonstrator that can be transferred to the shop floor for further testing. The proposed technology could assist in achieving the goal of manufacturing electrical machines with high precision of control and required tolerances, thus reducing waste of time and costs due to rework or scrappage.
Infrared images of three Litz wires (1,2 and 3) from a bundle during a heating and cooling cycle
CT scan of terminations
Machine vision techniques for inspection of solder
This activity is led by Dr. Divya Tiwari from the FEMM Hub, Department of Automatic Control and Systems Engineering (ACSE), at the University of Sheffield. For more details, contact Divya directly or email the FEMM Hub.
Reference:  Tiwari, D. et. al., 2021. In-process monitoring in electrical machine manufacturing: A review of state of the art and future directions. Proceedings of the IMechE, Part B: Journal of Engineering Manufacture.