Airbag Fabric Inspection
Cognex Deep Learning defect detection tool inspects textiles for cosmetic defects
Airbags are subject to strict quality standards to ensure passenger safety. Automotive manufacturers must double and triple check all safety-critical components to ensure quality, decrease warranty costs, and reduce recall liability. This is especially important for airbags, which must be inspected for holes, rips, tears, and seam and stitching issues that could cause them to fail. These kinds of quality issues are often missed or hard to detect in manual inspection. They are also difficult to program into a traditional machine vision system because of an airbag’s complex textile surface. The fabric pattern can be highly complex, and the visual appearance between airbags varies drastically due to the stretchable nature of the fabric, yarn thickness, and countless small tolerable variations. Because explicitly searching for all defects is too complicated and time consuming, Cognex Deep Learning offers a simple solution to identify all anomalous features, without training on “bad” images.
An engineer uses the Cognex Deep Learning defect detection tool in unsupervised mode to train the software on a set of “good” airbag images to build a reference model of an airbag. The model learns the normal appearance of an airbag’s fabric, including weaving pattern, fabric properties, and color. All features that deviate from the model’s normal appearance are characterized as anomalous. In this way, Cognex Deep Learning reliably and consistently detects all anomalies, such as holes, rips, tears, and unusual stitch patterns. Defective areas of the fabric can quickly be identified and reported without the need for extensive defect libraries.