Nut Geometry Inspection Using Improved Hough Line and Circle Methods.

Area of Science:

• Industrial Manufacturing
• Computer Vision
• Quality Control

Background:

• Traditional manual inspection of nuts, particularly A-grade types for high-stakes applications, is unreliable and compromises quality assurance.
• The demand for precision nuts in sectors like aerospace and power generation necessitates automated, high-accuracy inspection methods.

Purpose of the Study:

• To develop and implement an automated machine vision system for real-time geometric inspection of nuts on a production line.
• To enhance the quality control of A-grade nuts by ensuring precise measurements and automated screening.

Main Methods:

• A machine vision system was designed to perform seven geometric inspections: parallelism, opposite side length, straightness, radius, roundness, concentricity, and eccentricity.
• The core algorithm was optimized by modifying the Hough line and Hough circle detection techniques for improved speed and accuracy.
• The system integrates seamlessly into the production line for pre- and post-tapping inspection.

Main Results:

• The proposed system enables real-time, automated geometric inspection of nuts.
• Optimized Hough transforms significantly reduced detection time, making the system suitable for high-throughput production lines.
• The system accurately screens A-grade nuts, guaranteeing quality for demanding industrial applications.

Conclusions:

• The machine vision system provides a reliable and efficient solution for automated nut inspection.
• The optimized Hough transform algorithm is effective for fast and accurate geometric measurements in nut quality control.
• This technology ensures the consistent quality of A-grade nuts, crucial for safety-critical industries.