Quality inspection is not easy when it comes to batteries. Typically, welded joints were statistically inspected on individual components using destructive tests such as transverse grinds or force pull-off tests. However, this procedure is time-consuming, expensive, and does not guarantee flawless quality of every weld.
Today, Manz uses industrial image processing (machine vision) to make it possible to inspect the welding process in its modular Battery Laser System BLS 500 and to automate it end-to-end. The deep learning functions integrated in the software based on artificial intelligence (AI) offer particular added value for quality inspection.
In addition, the software solves another problem: Normally, AI training processes require a six-digit number of valid image data to achieve acceptable defect detection rates. Quantities that are typically not available in mechanical and plant engineering. In addition, the potential defects are often not known in all their forms. At the beginning of a new process, for example, only between 10 and 100 sample images are available.
Anomaly Detection" provides a remedy here. Not only does it get by with a very small amount of image data, but it also requires only "good images" showing defect-free objects for training.
Manz can provide 100% quality control of the laser-based production processes in the modular laser system by using deep learning technologies. The reliable detection of errors allows the quality of battery production to be maintained at a consistently high level. In addition, the mechanical engineering company saves a lot of time and thus costs thanks to the accelerated training processes.