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Classic Processing Cases in the Hardware Industry Based on Machine Vision

Time : 2025-11-05

The development and optimization of machine vision systems are gradually replacing many traditional manual inspection tasks. Today, we will briefly describe some application cases of machine vision inspection systems in the hardware processing industry.

 

1. Defect Detection of Internal Screw Threads

 Sample Image

Machine Vision (2)(860d29781a).png

 Lighting Used: Dome Light + Prism Module

 Case Description:

For cylindrical screws, curved surfaces pose a challenge. Conventional lighting methods often require tilting the camera lens to see only a small portion of the defects, necessitating multiple shots or rotating the product. By incorporating a prism module, the inner wall of the product can be divided into four segments and imaged in a single capture. The dome light, with its multiple diffuse angles and uniform illumination, increases the effective imaging arc length. Therefore, the combination of the dome light and the prism module captures defects more clearly and comprehensively.

 

2. Appearance Inspection of Hardware Copper Sheet

 Sample Image

Machine Vision (3)(0a79b26faf).png

 Lighting Used: Low-Angle Ring Light

 Case Description:

The center of the product is a silvery-white metal, surrounded by a plastic film. Using a large on-axis ring light (low-angle) avoids reflections from the surrounding areas while brightly illuminating the central metal surface, creating a bright field. Defects appear dark due to light scattering, creating a sharp contrast against the bright background.

 

3. Hardware Pin Tip Inspection

 Sample Image

Machine Vision (4)(1f9af05596).png

 Lighting Used: High-Angle Ring Light

 Case Description:

The pins of the hardware component are conical. Using vertically incident light, which is transformed into multi-angle illumination primarily at medium to low angles, brightly illuminates the pin tip while avoiding direct light on the conical sides. This highlights the pin tip more effectively and reduces the difficulty of image processing.

 

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