The principle, structure and key technology of the intelligent inspection system for the shape and s3

Door and window hinges play a crucial role in the quality and safety of modern buildings. One of the main challenges in hinge production is the use of stainless steel, which has poor manufacturability, leading to lower precision and increased quality issues during assembly. The traditional inspection process relies on manual inspection using tools like gauges, calipers, and feeler gauges. However, this method is not accurate or efficient enough to detect and address process quality problems, resulting in higher rates of defective products.

To overcome these challenges, the author has developed a new intelligent detection system that enables rapid and precise inspection of hinge components. This system ensures the manufacturing accuracy of parts and lays the foundation for maintaining assembly quality.

The system has specific testing requirements, including measuring the total length of the workpiece, the relative position of the workpiece hole, the diameter of the workpiece, the symmetry of the workpiece hole relative to the width, the flatness of the workpiece surface, and the step height between two planes of the workpiece. Since these are mainly two-dimensional visible contour and size measurements, non-contact detection methods like machine vision and laser technology are employed.

The system structure has been designed to accommodate over 1,000 types of hinge products. It combines machine vision, laser detection, and servo control technologies. The system incorporates a material table on a linear guide rail, which is driven by a servo motor connected to a ball screw to facilitate the detection feed. The workpiece is placed on the material table and positioned using the edge for subsequent detection.

The workflow of the system involves feeding the workpiece to the detection area using the material table. The detection area comprises two cameras and a laser displacement sensor. The cameras are used to detect the dimensions and shape of the workpiece, while the laser sensor measures the flatness of the surface. To accommodate workpieces with steps, two cameras are used to detect both sides of the T-shaped piece. The laser displacement sensor, mounted on two electric slides, can move vertically and horizontally to adapt to various workpiece dimensions.

The system also incorporates machine vision inspection methods to measure the total length of the workpiece, the relative position and diameter of the workpiece holes, the symmetry of the workpiece hole, and the sub-pixel algorithm for improved accuracy. The sub-pixel algorithm employs bilinear interpolation to extract image contours and enhance detection precision.

To ensure ease of operation and accommodate the wide variety of workpieces, the system incorporates workpiece classification and parameter threshold extraction. Workpieces are classified based on the parameters to be detected, and each type is assigned a coded barcode. By scanning the barcode, the system can identify the type of workpiece and the corresponding detection parameters. This enables precise positioning of the workpiece and accurate detection.

In conclusion, the intelligent detection system developed by the author addresses the challenges in hinge production and ensures precise inspection of large-scale workpieces. The system generates statistical reports of inspection results in minutes and allows for interchangeability and interoperability on inspection fixtures. It can be widely applied to the precision inspection of hinges, slide rails, and other similar products.