AI for increased process effectiveness

Purpose and goal:

The goal is to demonstrate AI-based quality inspection and process control in the production at Nilar and Sura, that share the need to automate inspection routines (lot of manual work required). AI is used on existing process data in real time from production flows to detect quality deficiencies in product/process. The solution enables rapid data analysis of many images to identify surface defects as well as the monitoring of the fraction of defects in the process.

Expected results and effects:

Results include (i) object detection tool for assembling process of Nilar batteries, (ii) automated detection and defect marking tool for Sura´s electrical steel sheets for electric vehicles, and (iii) ML tools for detecting deviations in batteries. They enable surface defects to be identified more efficiently and earlier in the process aiming to reduce material waste by >30% and manual work by >50% in the inspection process step, as well as increasing exploitation of available information.

Approach and implementation:

Project aimed to demonstrate AI-based process control for automatic quality control in Nilar and Sura production. The project has demonstrated to Surahammar the usefulness of AI-based process control throughout their production processes and product quality control. Nilar has integrated AI solutions to identify defects on photos during assembling battery modules. With the enhanced AI-based vison system, Nilar can now remove the worst defects from being included in the modules.