Case Study: STIHL achieves 99.5% inspection accuracy with Zebra machine vision

STIHL boosts quality inspection accuracy to 99.5% with Zebra

STIHL Group, a global manufacturer of power tools, faced a challenge in automating the visual quality assessment of small, intricate components for its chainsaws. The human-led inspection process was time-consuming, costly, and prone to error. To address this, STIHL partnered with vendor Zebra and its reseller Rauscher GmbH to implement a machine vision solution using Zebra's Aurora Design Assistant software and a 4Sight vision controller.

The solution from Zebra utilized deep learning technology to classify components accurately. By training a convolutional neural network on thousands of manually labeled images, the system achieved a 99.5% hit rate accuracy. This fully automated inspection process resulted in significant time and cost savings for STIHL, allowing them to improve efficiency and overall product quality.

STIHL