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Electronics

Mobile Device Speaker Mesh Inspection

Confidently flag defects on complex and visually confusing meshes

Mobile Device Speaker Mesh Inspection

Zugehörige Produkte

In-Sight D900

In-Sight D900 Vision-System

Ausgestattet mit der In-Sight ViDi Deep-Learning-basierten Vision-Software

ViDi software with all defect detection tools

VisionPro Deep Learning

Ein völlig neuer Ansatz bei komplexen Prüfungen, Teilelokalisierung, Klassifikation und OCR

The speaker mesh is a piece of perforated metal that protects a cellphone’s acoustic module from dust and damage while minimally affecting the sound.

Meshes must be visually inspected for cracks, scratches, missed perforations, deformation, broken edges, foreign inclusions such as dirt or hair, and damage from the perforation process. This mesh is visible to the end user, and so cosmetic damage must also be detected.

Because meshes are three-dimensional textured parts, they create complex lighting patterns of reflections and highlights. Even a small tilt in the mesh can completely change the pattern. Meanwhile, defects such as contamination or irregular scratches can appear anywhere on the part. This can be on the outer surface, or in the gaps between the wires. These variations make it difficult to program traditional machine vision to handle every possible case.

To identify speaker mesh defects, Cognex Deep Learning’s defect detection tool is trained on a wide selection of speaker meshes to learn the full variation of normal parts, including common lighting variations and the acceptable level of cosmetic defects. As it scans through meshes it analyzes and flags any that are outside of the acceptable range, while minimizing false positives.

Cognex Deep Learning can work in concert with traditional machine vision, which is best for precision alignment tasks and gauging the speaker mesh dimensions. The combination of deep learning plus traditional vision tools far outstrips that of manual inspection, at significantly higher speed. And it does so while maintaining consistency across shifts and production lines.

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