TAS GmbH Implements Deep Learning for Quality Control

This document is a case study detailing how TAS GmbH utilizes Zebra's Aurora Vision Studio with a deep learning add-on to enhance the quality control process for automotive components, specifically the covers for battery compartments in electric vehicles. The study outlines the meticulous inspection process employed by TAS GmbH, which includes the use of a customized camera system designed by ID Engineering. This system inspects each cover for defects such as irregularities in coating and surface scratches. The deep learning technology allows the system to be trained on specific defects, improving its recognition capabilities over time. The implementation of this technology has led to increased product quality, reliability, and safety, meeting the stringent demands of the automotive industry. The case study highlights the advantages of a no-code approach in developing the image processing solution, enabling rapid training and consistent quality, thus reinforcing TAS GmbH's commitment to innovation and excellence in manufacturing.