This pilot will develop vision in VIS and IR range for monitoring the laser welding process. In the VIS range, high-speed images of the molten pool can be acquired, and phenomena and geometrical features of the melt pool, keyhole, spatters and plasma plume distribution can be effectively monitored.

In the IR range, it is possible to evaluate the thermal profile of the weld, which is crucial to assess its quality. The vision systems will embody AI-based algorithms for defect detection and process characterisation, which running on an edge device, equipped with a powerful GPU.

Expected goals

An in-line quality assessment system for the welding process, and feeding control algorithms that adapt the process parameters in real-time are of utmost importance to ensure weld consistency. A closed-loop control system that will accurately measure the gap between the components before the weld and fix any misalignments is required.

Moreover, considering the optimisation of the production of the battery trays, as well as the need for digitalisation of the production, a solution is needed to collect production data in a non-destructive approach, incorporate data analytics to enhance the reconfigurability as a result of quality assessment of the process, aid decision-making and contribute to the goal of zero-defect.

Identified challenges

• A very significant challenge with battery tray welding is the length of the bead (~1m), making it very difficult to achieve a consistent, high-quality weld in the whole length of the bead.
• Another challenge is related to the gap control between the individual components.

Impact from openZDM technologies

Through the reduction of defective parts, less time and resources will be consumed for reworking. Moreover, it will improve the sustainability of the production process.