Automotive

Seat belt fabric inspection

Ensure that seat belts are safe and free of defect or other unwanted anomalies

Deep learning finds defects on seat belts fabric stitching as they are inspected on the production line.

관련 제품

VisionPro ViDi Product Tile

VisionPro Deep Learning

딥러닝 기반 산업용 이미지 분석을 위한 그래픽 프로그래밍 환경

In-Sight D900

In-Sight D900

In-Sight ViDi 딥러닝 기반 비전 소프트웨어로 작동

Automotive seat belts and safety harnesses are made from a strong woven polyester fabric called webbing. They are a crucial part of the safety equipment of a vehicle. Seat belt webbing is connected to various pieces of hardware such as anchor brackets and buckles with heavy stitching. Defect-free webbing and stitching are both important to the safety and reliability of the finished seat belt or harness.

Any defects in the fabric or stitching are unacceptable for safety, and if a later inspection reveals any error, the vehicle will have to be pulled from the line for removal and reinstallation of a new set of seat belts and safety harnesses.

Both the fabric of the webbing and the stitching that attaches it to hardware are complex and vary significantly in appearance. Errors in fabric weave and stitching can appear anywhere, and in a variety of forms. Programming all the possibilities is impossible, so conventional machine vision is inadequate for this task.

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Cognex Deep Learning quickly and easily detects any anomalies in seat belt fabric. It trains on a small set of images of good webbing and stitching. The defect detection tool then instantly detects any errors in the weave of the webbing or the pattern and thread of the stitching. If a new webbing or stitching design is introduced, the tool can train on a small image set of that new design and quickly be able to learn that it is acceptable, without significant downtime.

Installed seat belts and harnesses are free of even subtle weaving or stitching defects that might compromise their strength in a collision.

주요 Cognex 제품

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전 세계 어디에서든 코그넥스 담당자들이 여러분의 비전과 산업용 바코드 판독 관련 문제를 지원합니다.

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